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	# coding=utf-8
	# Copyright 2023 The LAION-AI Team and The HuggingFace Team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	""" PyTorch CLAP model."""
	import collections
	import math
	from dataclasses import dataclass
	from typing import Any, List, Optional, Tuple, Union

	import torch
	import torch.nn.functional as F
	from torch import nn

	from ...activations import ACT2FN
	from ...modeling_outputs import (
	BaseModelOutputWithPastAndCrossAttentions,
	BaseModelOutputWithPooling,
	BaseModelOutputWithPoolingAndCrossAttentions,
	)
	from ...modeling_utils import PreTrainedModel
	from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, meshgrid, prune_linear_layer
	from ...utils import (
	ModelOutput,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	logging,
	replace_return_docstrings,
	)
	from .configuration_clap import ClapAudioConfig, ClapConfig, ClapTextConfig


	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "laion/clap-htsat-fused"

	CLAP_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"laion/clap-htsat-fused",
	"laion/clap-htsat-unfused",
	# See all clap models at https://huggingface.co/models?filter=clap
	]


	# Adapted from: https://github.com/LAION-AI/CLAP/blob/6ad05a971ba0622f6acee8c41993e0d02bbed639/src/open_clip/utils.py#L191
	def interpolate(hidden_states, ratio):
	"""
	Interpolate data in time domain. This is used to compensate the resolution reduction in downsampling of a CNN.

	Args:
	hidden_states (`torch.FloatTensor` of shape (batch_size, time_length, classes_num)):
	Input hidden states
	ratio (`int`):
	The ratio of the length of the output to the length of the input.
	"""
	(batch_size, time_length, classes_num) = hidden_states.shape
	upsampled = hidden_states[:, :, None, :].repeat(1, 1, ratio, 1)
	upsampled = upsampled.reshape(batch_size, time_length * ratio, classes_num)
	return upsampled


	# Adapted from https://github.com/LAION-AI/CLAP/blob/6ad05a971ba0622f6acee8c41993e0d02bbed639/src/open_clip/htsat.py#L249
	def window_partition(hidden_states, window_size):
	"""
	Returns the resized hidden states. The output shape should be `(batch_size * num_windows, window_size, window_size,
	num_channels)`

	Args:
	hidden_states (`torch.FloatTensor` of shape `(batch_size, height, width, num_channels)`):
	Input hidden states
	window_size (`int`):
	Window size
	"""
	batch_size, height, width, num_channels = hidden_states.shape

	hidden_states = hidden_states.view(
	batch_size, height // window_size, window_size, width // window_size, window_size, num_channels
	)
	windows = hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, num_channels)
	return windows


	# Adapted from https://github.com/LAION-AI/CLAP/blob/6ad05a971ba0622f6acee8c41993e0d02bbed639/src/open_clip/htsat.py#L263
	def window_reverse(windows, window_size, height, width):
	"""
	Args:
	windows (`torch.FloatTensor` of shape `(num_windows * batch_size, window_size, window_size, num_channels)`):
	Input windows
	window_size (`int`):
	Window size
	height (`int`):
	Height of the resized audio
	width (`int`):
	Width of the resized audio
	"""
	batch_size = int(windows.shape[0] / (height * width / window_size / window_size))

	hidden_states = windows.view(batch_size, height // window_size, width // window_size, window_size, window_size, -1)
	hidden_states = hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().view(batch_size, height, width, -1)
	return hidden_states


	# Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids
	def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
	"""
	Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
	are ignored. This is modified from fairseq's `utils.make_positions`.

	Args:
	x: torch.Tensor x:

	Returns: torch.Tensor
	"""
	# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
	mask = input_ids.ne(padding_idx).int()
	incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
	return incremental_indices.long() + padding_idx


	# contrastive loss function, adapted from
	# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html#CLIP-loss-function
	def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
	labels = torch.arange(len(logits), device=logits.device)
	return nn.functional.cross_entropy(logits, labels)


	@dataclass
	# Copied from transformers.models.clip.modeling_clip.CLIPTextModelOutput with CLIP->Clap
	class ClapTextModelOutput(ModelOutput):
	"""
	Base class for text model's outputs that also contains a pooling of the last hidden states.

	Args:
	text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` optional returned when model is initialized with `with_projection=True`):
	The text embeddings obtained by applying the projection layer to the pooler_output.
	last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
	Sequence of hidden-states at the output of the last layer of the model.
	hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
	one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

	Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
	attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
	Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
	sequence_length)`.

	Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
	heads.
	"""

	text_embeds: Optional[torch.FloatTensor] = None
	last_hidden_state: torch.FloatTensor = None
	hidden_states: Optional[Tuple[torch.FloatTensor]] = None
	attentions: Optional[Tuple[torch.FloatTensor]] = None


	@dataclass
	class ClapAudioModelOutput(ModelOutput):
	"""
	ClapAudio model output to mimic the output of the original implementation.

	Args:
	audio_embeds (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
	The Audio embeddings obtained by applying the projection layer to the pooler_output.
	last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
	Sequence of hidden-states at the output of the last layer of the model.
	attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
	Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
	sequence_length)`.

	Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
	heads.
	hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
	one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

	Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
	"""

	audio_embeds: Optional[torch.FloatTensor] = None
	last_hidden_state: torch.FloatTensor = None
	hidden_states: Optional[Tuple[torch.FloatTensor]] = None
	attentions: Optional[Tuple[torch.FloatTensor]] = None


	@dataclass
	# Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->Clap, vision->audio, Vision->Audio, image->audio
	class ClapOutput(ModelOutput):
	"""
	Args:
	loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `return_loss` is `True`):
	Contrastive loss for audio-text similarity.
	logits_per_audio:(`torch.FloatTensor` of shape `(audio_batch_size, text_batch_size)`):
	The scaled dot product scores between `audio_embeds` and `text_embeds`. This represents the audio-text
	similarity scores.
	logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, audio_batch_size)`):
	The scaled dot product scores between `text_embeds` and `audio_embeds`. This represents the text-audio
	similarity scores.
	text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
	The text embeddings obtained by applying the projection layer to the pooled output of [`ClapTextModel`].
	audio_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
	The audio embeddings obtained by applying the projection layer to the pooled output of [`ClapAudioModel`].
	text_model_output(`BaseModelOutputWithPooling`):
	The output of the [`ClapTextModel`].
	audio_model_output(`BaseModelOutputWithPooling`):
	The output of the [`ClapAudioModel`].
	"""

	loss: Optional[torch.FloatTensor] = None
	logits_per_audio: torch.FloatTensor = None
	logits_per_text: torch.FloatTensor = None
	text_embeds: torch.FloatTensor = None
	audio_embeds: torch.FloatTensor = None
	text_model_output: BaseModelOutputWithPooling = None
	audio_model_output: BaseModelOutputWithPooling = None

	def to_tuple(self) -> Tuple[Any]:
	return tuple(
	self[k] if k not in ["text_model_output", "audio_model_output"] else getattr(self, k).to_tuple()
	for k in self.keys()
	)


	# Adapted from transformers.models.swin.modeling_swin.SwinDropPath
	class ClapDropPath(nn.Module):
	"""
	Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is a slightly
	refactored version of the `SwinDropPath` implementation.
	"""

	def __init__(self, drop_prob=None):
	super().__init__()
	self.drop_prob = drop_prob

	def forward(self, hidden_states):
	if self.drop_prob == 0.0 or not self.training:
	return hidden_states

	keep_prob = 1 - self.drop_prob
	# work with diff dim tensors, not just 2D ConvNets
	shape = (hidden_states.shape[0],) + (1,) * (hidden_states.ndim - 1)

	random_tensor = keep_prob + torch.rand(shape, dtype=hidden_states.dtype, device=hidden_states.device)
	random_tensor.floor_() # binarize
	output = hidden_states.div(keep_prob) * random_tensor
	return output


	# Adapted from https://github.com/LAION-AI/CLAP/blob/6ad05a971ba0622f6acee8c41993e0d02bbed639/src/open_clip/feature_fusion.py#L133
	class ClapAudioAFFBlock(nn.Module):
	r"""
	ATTENTIONAL FEATURE FUSION Block from CLAP, since in CLAP we are always in 2D mode, it is not needed to implement
	the 1D version.
	"""

	def __init__(self, config: ClapAudioConfig):
	super().__init__()
	channels = config.patch_embeds_hidden_size
	downsize_ratio = config.aff_block_r
	inter_channels = int(channels // downsize_ratio)

	self.local_att = nn.Sequential(
	nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
	nn.BatchNorm2d(inter_channels),
	nn.ReLU(inplace=True),
	nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
	nn.BatchNorm2d(channels),
	)
	self.global_att = nn.Sequential(
	nn.AdaptiveAvgPool2d(1),
	nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
	nn.BatchNorm2d(inter_channels),
	nn.ReLU(inplace=True),
	nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
	nn.BatchNorm2d(channels),
	)

	self.sigmoid = nn.Sigmoid()

	def forward(self, hidden_states, residual):
	attention_input = hidden_states + residual

	fused_layer_output = self.local_att(attention_input) + self.global_att(attention_input)
	fused_layer_output = self.sigmoid(fused_layer_output)

	output = 2 * hidden_states * fused_layer_output + 2 * residual * (1 - fused_layer_output)
	return output


	class ClapAudioPatchEmbed(nn.Module):
	"""
	This module converts the hidden states reshaped as an image to patch embeddings ready to be passed to the
	Transformer block.
	"""

	def __init__(self, config: ClapAudioConfig):
	super().__init__()
	img_size = (config.spec_size, config.spec_size) if isinstance(config.spec_size, int) else config.spec_size
	patch_size = (
	(config.patch_size, config.patch_size) if isinstance(config.patch_size, int) else config.patch_size
	)
	patch_stride = (
	(config.patch_stride, config.patch_stride) if isinstance(config.patch_stride, int) else config.patch_stride
	)

	self.img_size = img_size
	self.patch_stride = patch_stride

	self.grid_size = (img_size[0] // patch_stride[0], img_size[1] // patch_stride[1])
	self.num_patches = self.grid_size[0] * self.grid_size[1]

	self.flatten = config.flatten_patch_embeds
	self.enable_fusion = config.enable_fusion

	padding = ((patch_size[0] - patch_stride[0]) // 2, (patch_size[1] - patch_stride[1]) // 2)

	scale_factor = 4 if (self.enable_fusion) and (config.fusion_type == "channel_map") else 1

	self.proj = nn.Conv2d(
	config.patch_embed_input_channels * scale_factor,
	config.patch_embeds_hidden_size,
	kernel_size=patch_size,
	stride=patch_stride,
	padding=padding,
	)

	self.norm = nn.LayerNorm(config.patch_embeds_hidden_size) if config.enable_patch_layer_norm else nn.Identity()
	if self.enable_fusion:
	self.fusion_model = ClapAudioAFFBlock(config)
	self.mel_conv2d = nn.Conv2d(
	config.patch_embed_input_channels,
	config.patch_embeds_hidden_size,
	kernel_size=(patch_size[0], patch_size[1] * 3),
	stride=(patch_stride[0], patch_stride[1] * 3),
	padding=padding,
	)

	def forward(self, hidden_states, is_longer_idx=None):
	if self.enable_fusion:
	# retrieve the last mel as we have transposed the input
	global_hidden_states = hidden_states[:, 0:1, :, :]

	# global processing
	batch_size, num_channels, height, width = global_hidden_states.shape

	if height != self.img_size[0] or width != self.img_size[1]:
	raise ValueError(
	f"Input audio size ({height}{width}) doesn't match model ({self.img_size[0]}{self.img_size[1]})."
	)

	global_hidden_states = self.proj(global_hidden_states)
	output_width = global_hidden_states.size(-1)
	if len(is_longer_idx) > 0:
	# local processing
	local_hidden_states = hidden_states[is_longer_idx, 1:, :, :].contiguous()
	batch_size, num_channels, height, width = local_hidden_states.shape
	local_hidden_states = local_hidden_states.view(batch_size * num_channels, 1, height, width)

	local_hidden_states = self.mel_conv2d(local_hidden_states)

	_, features, height, width = local_hidden_states.shape
	local_hidden_states = local_hidden_states.view(batch_size, num_channels, features, height, width)
	local_hidden_states = local_hidden_states.permute((0, 2, 3, 1, 4)).contiguous().flatten(3)

	local_width = local_hidden_states.size(-1)
	local_hidden_states = torch.nn.functional.pad(
	local_hidden_states, (0, output_width - local_width), "constant", 0
	)

	global_hidden_states[is_longer_idx] = self.fusion_model(
	global_hidden_states[is_longer_idx], local_hidden_states
	)
	hidden_states = global_hidden_states
	else:
	_, _, height, width = hidden_states.shape
	if height != self.img_size[0] or width != self.img_size[1]:
	raise ValueError(
	f"Input audio size ({height}{width}) doesn't match model ({self.img_size[0]}{self.img_size[1]})."
	)
	hidden_states = self.proj(hidden_states)

	if self.flatten:
	hidden_states = hidden_states.flatten(2).transpose(1, 2)
	hidden_states = self.norm(hidden_states)
	return hidden_states


	# Copied from transformers.models.swin.modeling_swin.SwinSelfAttention with Swin->ClapAudio
	class ClapAudioSelfAttention(nn.Module):
	def __init__(self, config, dim, num_heads, window_size):
	super().__init__()
	if dim % num_heads != 0:
	raise ValueError(
	f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
	)

	self.num_attention_heads = num_heads
	self.attention_head_size = int(dim / num_heads)
	self.all_head_size = self.num_attention_heads * self.attention_head_size
	self.window_size = (
	window_size if isinstance(window_size, collections.abc.Iterable) else (window_size, window_size)
	)

	self.relative_position_bias_table = nn.Parameter(
	torch.zeros((2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1), num_heads)
	)

	# get pair-wise relative position index for each token inside the window
	coords_h = torch.arange(self.window_size[0])
	coords_w = torch.arange(self.window_size[1])
	coords = torch.stack(meshgrid([coords_h, coords_w], indexing="ij"))
	coords_flatten = torch.flatten(coords, 1)
	relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
	relative_coords = relative_coords.permute(1, 2, 0).contiguous()
	relative_coords[:, :, 0] += self.window_size[0] - 1
	relative_coords[:, :, 1] += self.window_size[1] - 1
	relative_coords[:, :, 0] = 2 self.window_size[1] - 1
	relative_position_index = relative_coords.sum(-1)
	self.register_buffer("relative_position_index", relative_position_index)

	self.query = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
	self.key = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
	self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)

	self.dropout = nn.Dropout(config.attention_probs_dropout_prob)

	def transpose_for_scores(self, x):
	new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
	x = x.view(new_x_shape)
	return x.permute(0, 2, 1, 3)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.Tensor]:
	batch_size, dim, num_channels = hidden_states.shape
	mixed_query_layer = self.query(hidden_states)

	key_layer = self.transpose_for_scores(self.key(hidden_states))
	value_layer = self.transpose_for_scores(self.value(hidden_states))
	query_layer = self.transpose_for_scores(mixed_query_layer)

	# Take the dot product between "query" and "key" to get the raw attention scores.
	attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

	attention_scores = attention_scores / math.sqrt(self.attention_head_size)

	relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)]
	relative_position_bias = relative_position_bias.view(
	self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
	)

	relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
	attention_scores = attention_scores + relative_position_bias.unsqueeze(0)

	if attention_mask is not None:
	# Apply the attention mask is (precomputed for all layers in ClapAudioModel forward() function)
	mask_shape = attention_mask.shape[0]
	attention_scores = attention_scores.view(
	batch_size // mask_shape, mask_shape, self.num_attention_heads, dim, dim
	)
	attention_scores = attention_scores + attention_mask.unsqueeze(1).unsqueeze(0)
	attention_scores = attention_scores.view(-1, self.num_attention_heads, dim, dim)

	# Normalize the attention scores to probabilities.
	attention_probs = nn.functional.softmax(attention_scores, dim=-1)

	# This is actually dropping out entire tokens to attend to, which might
	# seem a bit unusual, but is taken from the original Transformer paper.
	attention_probs = self.dropout(attention_probs)

	# Mask heads if we want to
	if head_mask is not None:
	attention_probs = attention_probs * head_mask

	context_layer = torch.matmul(attention_probs, value_layer)
	context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
	new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
	context_layer = context_layer.view(new_context_layer_shape)

	outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

	return outputs


	# Copied from transformers.models.swin.modeling_swin.SwinSelfOutput with Swin->ClapAudio
	class ClapAudioSelfOutput(nn.Module):
	def __init__(self, config, dim):
	super().__init__()
	self.dense = nn.Linear(dim, dim)
	self.dropout = nn.Dropout(config.attention_probs_dropout_prob)

	def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
	hidden_states = self.dense(hidden_states)
	hidden_states = self.dropout(hidden_states)

	return hidden_states


	# Copied from transformers.models.swin.modeling_swin.SwinAttention with Swin->ClapAudio
	class ClapAudioAttention(nn.Module):
	def __init__(self, config, dim, num_heads, window_size):
	super().__init__()
	self.self = ClapAudioSelfAttention(config, dim, num_heads, window_size)
	self.output = ClapAudioSelfOutput(config, dim)
	self.pruned_heads = set()

	def prune_heads(self, heads):
	if len(heads) == 0:
	return
	heads, index = find_pruneable_heads_and_indices(
	heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
	)

	# Prune linear layers
	self.self.query = prune_linear_layer(self.self.query, index)
	self.self.key = prune_linear_layer(self.self.key, index)
	self.self.value = prune_linear_layer(self.self.value, index)
	self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

	# Update hyper params and store pruned heads
	self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
	self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
	self.pruned_heads = self.pruned_heads.union(heads)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.Tensor]:
	self_outputs = self.self(hidden_states, attention_mask, head_mask, output_attentions)
	attention_output = self.output(self_outputs[0], hidden_states)
	outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
	return outputs


	# Copied from transformers.models.swin.modeling_swin.SwinIntermediate with Swin->ClapAudio
	class ClapAudioIntermediate(nn.Module):
	def __init__(self, config, dim):
	super().__init__()
	self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
	if isinstance(config.hidden_act, str):
	self.intermediate_act_fn = ACT2FN[config.hidden_act]
	else:
	self.intermediate_act_fn = config.hidden_act

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	hidden_states = self.dense(hidden_states)
	hidden_states = self.intermediate_act_fn(hidden_states)
	return hidden_states


	# Copied from transformers.models.swin.modeling_swin.SwinOutput with Swin->ClapAudio
	class ClapAudioOutput(nn.Module):
	def __init__(self, config, dim):
	super().__init__()
	self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	hidden_states = self.dense(hidden_states)
	hidden_states = self.dropout(hidden_states)
	return hidden_states


	# Copied from transformers.models.swin.modeling_swin.SwinLayer with SwinDropPath->ClapDropPath, Swin->ClapAudio
	class ClapAudioLayer(nn.Module):
	def __init__(self, config, dim, input_resolution, num_heads, shift_size=0):
	super().__init__()
	self.chunk_size_feed_forward = config.chunk_size_feed_forward
	self.shift_size = shift_size
	self.window_size = config.window_size
	self.input_resolution = input_resolution
	self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps)
	self.attention = ClapAudioAttention(config, dim, num_heads, window_size=self.window_size)
	self.drop_path = ClapDropPath(config.drop_path_rate) if config.drop_path_rate > 0.0 else nn.Identity()
	self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
	self.intermediate = ClapAudioIntermediate(config, dim)
	self.output = ClapAudioOutput(config, dim)

	def set_shift_and_window_size(self, input_resolution):
	if min(input_resolution) <= self.window_size:
	# if window size is larger than input resolution, we don't partition windows
	self.shift_size = 0
	self.window_size = min(input_resolution)

	def get_attn_mask(self, height, width, dtype):
	if self.shift_size > 0:
	# calculate attention mask for SW-MSA
	img_mask = torch.zeros((1, height, width, 1), dtype=dtype)
	height_slices = (
	slice(0, -self.window_size),
	slice(-self.window_size, -self.shift_size),
	slice(-self.shift_size, None),
	)
	width_slices = (
	slice(0, -self.window_size),
	slice(-self.window_size, -self.shift_size),
	slice(-self.shift_size, None),
	)
	count = 0
	for height_slice in height_slices:
	for width_slice in width_slices:
	img_mask[:, height_slice, width_slice, :] = count
	count += 1

	mask_windows = window_partition(img_mask, self.window_size)
	mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
	attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
	attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
	else:
	attn_mask = None
	return attn_mask

	def maybe_pad(self, hidden_states, height, width):
	pad_right = (self.window_size - width % self.window_size) % self.window_size
	pad_bottom = (self.window_size - height % self.window_size) % self.window_size
	pad_values = (0, 0, 0, pad_right, 0, pad_bottom)
	hidden_states = nn.functional.pad(hidden_states, pad_values)
	return hidden_states, pad_values

	def forward(
	self,
	hidden_states: torch.Tensor,
	input_dimensions: Tuple[int, int],
	head_mask: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = False,
	always_partition: Optional[bool] = False,
	) -> Tuple[torch.Tensor, torch.Tensor]:
	if not always_partition:
	self.set_shift_and_window_size(input_dimensions)
	else:
	pass
	height, width = input_dimensions
	batch_size, _, channels = hidden_states.size()
	shortcut = hidden_states

	hidden_states = self.layernorm_before(hidden_states)

	hidden_states = hidden_states.view(batch_size, height, width, channels)

	# pad hidden_states to multiples of window size
	hidden_states, pad_values = self.maybe_pad(hidden_states, height, width)

	_, height_pad, width_pad, _ = hidden_states.shape
	# cyclic shift
	if self.shift_size > 0:
	shifted_hidden_states = torch.roll(hidden_states, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
	else:
	shifted_hidden_states = hidden_states

	# partition windows
	hidden_states_windows = window_partition(shifted_hidden_states, self.window_size)
	hidden_states_windows = hidden_states_windows.view(-1, self.window_size * self.window_size, channels)
	attn_mask = self.get_attn_mask(height_pad, width_pad, dtype=hidden_states.dtype)
	if attn_mask is not None:
	attn_mask = attn_mask.to(hidden_states_windows.device)

	attention_outputs = self.attention(
	hidden_states_windows, attn_mask, head_mask, output_attentions=output_attentions
	)

	attention_output = attention_outputs[0]

	attention_windows = attention_output.view(-1, self.window_size, self.window_size, channels)
	shifted_windows = window_reverse(attention_windows, self.window_size, height_pad, width_pad)

	# reverse cyclic shift
	if self.shift_size > 0:
	attention_windows = torch.roll(shifted_windows, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
	else:
	attention_windows = shifted_windows

	was_padded = pad_values[3] > 0 or pad_values[5] > 0
	if was_padded:
	attention_windows = attention_windows[:, :height, :width, :].contiguous()

	attention_windows = attention_windows.view(batch_size, height * width, channels)

	hidden_states = shortcut + self.drop_path(attention_windows)

	layer_output = self.layernorm_after(hidden_states)
	layer_output = self.intermediate(layer_output)
	layer_output = hidden_states + self.output(layer_output)

	layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,)
	return layer_outputs


	# Copied from transformers.models.swin.modeling_swin.SwinStage with Swin->ClapAudio
	class ClapAudioStage(nn.Module):
	def __init__(self, config, dim, input_resolution, depth, num_heads, drop_path, downsample):
	super().__init__()
	self.config = config
	self.dim = dim
	self.blocks = nn.ModuleList(
	[
	ClapAudioLayer(
	config=config,
	dim=dim,
	input_resolution=input_resolution,
	num_heads=num_heads,
	shift_size=0 if (i % 2 == 0) else config.window_size // 2,
	)
	for i in range(depth)
	]
	)

	# patch merging layer
	if downsample is not None:
	self.downsample = downsample(input_resolution, dim=dim, norm_layer=nn.LayerNorm)
	else:
	self.downsample = None

	self.pointing = False

	def forward(
	self,
	hidden_states: torch.Tensor,
	input_dimensions: Tuple[int, int],
	head_mask: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = False,
	always_partition: Optional[bool] = False,
	) -> Tuple[torch.Tensor]:
	height, width = input_dimensions
	for i, layer_module in enumerate(self.blocks):
	layer_head_mask = head_mask[i] if head_mask is not None else None

	layer_outputs = layer_module(
	hidden_states, input_dimensions, layer_head_mask, output_attentions, always_partition
	)

	hidden_states = layer_outputs[0]

	hidden_states_before_downsampling = hidden_states
	if self.downsample is not None:
	height_downsampled, width_downsampled = (height + 1) // 2, (width + 1) // 2
	output_dimensions = (height, width, height_downsampled, width_downsampled)
	hidden_states = self.downsample(hidden_states_before_downsampling, input_dimensions)
	else:
	output_dimensions = (height, width, height, width)

	stage_outputs = (hidden_states, hidden_states_before_downsampling, output_dimensions)

	if output_attentions:
	stage_outputs += layer_outputs[1:]
	return stage_outputs


	# Copied from transformers.models.swin.modeling_swin.SwinPatchMerging with Swin->ClapAudio
	class ClapAudioPatchMerging(nn.Module):
	"""
	Patch Merging Layer.

	Args:
	input_resolution (`Tuple[int]`):
	Resolution of input feature.
	dim (`int`):
	Number of input channels.
	norm_layer (`nn.Module`, optional, defaults to `nn.LayerNorm`):
	Normalization layer class.
	"""

	def __init__(self, input_resolution: Tuple[int], dim: int, norm_layer: nn.Module = nn.LayerNorm) -> None:
	super().__init__()
	self.input_resolution = input_resolution
	self.dim = dim
	self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
	self.norm = norm_layer(4 * dim)

	def maybe_pad(self, input_feature, height, width):
	should_pad = (height % 2 == 1) or (width % 2 == 1)
	if should_pad:
	pad_values = (0, 0, 0, width % 2, 0, height % 2)
	input_feature = nn.functional.pad(input_feature, pad_values)

	return input_feature

	def forward(self, input_feature: torch.Tensor, input_dimensions: Tuple[int, int]) -> torch.Tensor:
	height, width = input_dimensions
	# `dim` is height * width
	batch_size, dim, num_channels = input_feature.shape

	input_feature = input_feature.view(batch_size, height, width, num_channels)
	# pad input to be disible by width and height, if needed
	input_feature = self.maybe_pad(input_feature, height, width)
	# [batch_size, height/2, width/2, num_channels]
	input_feature_0 = input_feature[:, 0::2, 0::2, :]
	# [batch_size, height/2, width/2, num_channels]
	input_feature_1 = input_feature[:, 1::2, 0::2, :]
	# [batch_size, height/2, width/2, num_channels]
	input_feature_2 = input_feature[:, 0::2, 1::2, :]
	# [batch_size, height/2, width/2, num_channels]
	input_feature_3 = input_feature[:, 1::2, 1::2, :]
	# batch_size height/2 width/2 4*num_channels
	input_feature = torch.cat([input_feature_0, input_feature_1, input_feature_2, input_feature_3], -1)
	input_feature = input_feature.view(batch_size, -1, 4 * num_channels) # batch_size height/2width/2 4C

	input_feature = self.norm(input_feature)
	input_feature = self.reduction(input_feature)

	return input_feature


	class ClapAudioEncoder(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.num_layers = len(config.depths)

	self.config = config
	self.patch_embed = ClapAudioPatchEmbed(config)
	self.enable_fusion = config.enable_fusion
	self.patch_stride = self.patch_embed.patch_stride
	self.spec_size = config.spec_size
	self.freq_ratio = config.spec_size // config.num_mel_bins

	self.num_features = int(config.patch_embeds_hidden_size * 2 ** (self.num_layers - 1))

	drop_path_rate = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]

	grid_size = self.patch_embed.grid_size
	self.input_resolutions = [(grid_size[0] // (2i), grid_size[1] // (2i)) for i in range(self.num_layers)]

	self.layers = nn.ModuleList(
	[
	ClapAudioStage(
	config=config,
	dim=int(config.patch_embeds_hidden_size * 2**i_layer),
	input_resolution=self.input_resolutions[i_layer],
	depth=config.depths[i_layer],
	num_heads=config.num_attention_heads[i_layer],
	drop_path=drop_path_rate[sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])],
	downsample=ClapAudioPatchMerging if (i_layer < self.num_layers - 1) else None,
	)
	for i_layer in range(self.num_layers)
	]
	)

	self.gradient_checkpointing = False

	self.batch_norm = nn.BatchNorm2d(config.num_mel_bins)
	self.norm = nn.LayerNorm(self.num_features)
	self.depths = config.depths
	self.avgpool = nn.AdaptiveAvgPool1d(1)

	def reshape_mel2img(self, normalized_input_features):
	"""
	The input is 4 normalized log mel spectrograms. It is reshape to the common shape of images. Each channel
	should represent 1 of the 4 crops of the spectrogram. For more details, refer to the [`ClapFeatureExtractor`].
	"""
	_, _, time_length, freq_length = normalized_input_features.shape

	spec_width = int(self.spec_size * self.freq_ratio)
	spec_heigth = self.spec_size // self.freq_ratio

	if time_length > spec_width or freq_length > spec_heigth:
	raise ValueError("the wav size should be less than or equal to the swin input size")

	# to avoid bicubic zero error
	if time_length < spec_width:
	normalized_input_features = nn.functional.interpolate(
	normalized_input_features, (spec_width, freq_length), mode="bicubic", align_corners=True
	)
	if freq_length < spec_heigth:
	normalized_input_features = nn.functional.interpolate(
	normalized_input_features, (time_length, spec_heigth), mode="bicubic", align_corners=True
	)

	batch, channels, time, freq = normalized_input_features.shape

	# batch_size, channels, spec_width, spec_heigth --> batch_size, channels, spec_heigth * freq_ratio, spec_width // freq_ratio
	normalized_input_features = normalized_input_features.reshape(
	batch, channels * self.freq_ratio, time // self.freq_ratio, freq
	)
	normalized_input_features = normalized_input_features.permute(0, 1, 3, 2).contiguous()
	normalized_input_features = normalized_input_features.reshape(
	batch, channels, freq * self.freq_ratio, time // self.freq_ratio
	)

	return normalized_input_features

	def forward(
	self,
	input_features,
	is_longer: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = False,
	output_hidden_states: Optional[bool] = False,
	output_hidden_states_before_downsampling: Optional[bool] = False,
	always_partition: Optional[bool] = False,
	return_dict: Optional[bool] = True,
	) -> Union[Tuple, ClapAudioModelOutput]:
	input_features = input_features.transpose(1, 3)
	normalized_input_features = self.batch_norm(input_features)
	normalized_input_features = normalized_input_features.transpose(1, 3)

	is_longer_list_idx = None
	if self.enable_fusion:
	is_longer_list = is_longer.to(input_features.device)
	is_longer_list_idx = torch.where(is_longer_list == 1)[0]

	hidden_states = self.reshape_mel2img(normalized_input_features)

	frames_num = hidden_states.shape[2]

	hidden_states = self.patch_embed(hidden_states, is_longer_list_idx)

	all_hidden_states = () if output_hidden_states else None
	all_reshaped_hidden_states = () if output_hidden_states else None
	all_self_attentions = () if output_attentions else None

	input_dimensions = self.input_resolutions[0]

	if output_hidden_states:
	batch_size, _, hidden_size = hidden_states.shape
	# rearrange batch_size (height width) channels -> batch_size channel height width
	reshaped_hidden_state = hidden_states.view(batch_size, *input_dimensions, hidden_size)
	reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2)
	all_hidden_states += (hidden_states,)
	all_reshaped_hidden_states += (reshaped_hidden_state,)

	for i, layer_module in enumerate(self.layers):
	layer_head_mask = head_mask[i] if head_mask is not None else None

	input_dimensions = self.input_resolutions[i]

	if self.gradient_checkpointing and self.training:

	def create_custom_forward(module):
	def custom_forward(*inputs):
	return module(*inputs, output_attentions)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(layer_module), hidden_states, input_dimensions, layer_head_mask
	)
	else:
	layer_outputs = layer_module(
	hidden_states, input_dimensions, layer_head_mask, output_attentions, always_partition
	)

	hidden_states = layer_outputs[0]

	hidden_states_before_downsampling = layer_outputs[1]
	output_dimensions = layer_outputs[2]

	input_dimensions = (output_dimensions[-2], output_dimensions[-1])

	if output_hidden_states and output_hidden_states_before_downsampling:
	batch_size, _, hidden_size = hidden_states_before_downsampling.shape
	# rearrange batch_size (height width) channels -> batch_size channel height width
	# here we use the original (not downsampled) height and width
	reshaped_hidden_state = hidden_states_before_downsampling.view(
	batch_size, *(output_dimensions[0], output_dimensions[1]), hidden_size
	)
	reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2)
	all_hidden_states += (hidden_states_before_downsampling,)
	all_reshaped_hidden_states += (reshaped_hidden_state,)
	elif output_hidden_states and not output_hidden_states_before_downsampling:
	batch_size, _, hidden_size = hidden_states.shape
	# rearrange batch_size (height width) channels -> batch_size channel height width
	reshaped_hidden_state = hidden_states.view(batch_size, *input_dimensions, hidden_size)
	reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2)
	all_hidden_states += (hidden_states,)
	all_reshaped_hidden_states += (reshaped_hidden_state,)

	if output_attentions:
	all_self_attentions += layer_outputs[3:]

	last_hidden_state = self.norm(hidden_states)

	batch_size, _, n_channels = last_hidden_state.shape

	freq_shape = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[0]
	temporal_shape = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[1]

	last_hidden_state = (
	last_hidden_state.permute(0, 2, 1).contiguous().reshape(batch_size, n_channels, freq_shape, temporal_shape)
	)

	batch_size, n_channels, n_frequencies, n_temp = last_hidden_state.shape
	# group 2D CNN
	c_freq_bin = n_frequencies // self.freq_ratio
	last_hidden_state = last_hidden_state.reshape(
	batch_size, n_channels, n_frequencies // c_freq_bin, c_freq_bin, n_temp
	)
	last_hidden_state = (
	last_hidden_state.permute(0, 1, 3, 2, 4).contiguous().reshape(batch_size, n_channels, c_freq_bin, -1)
	)
	latent_output = self.avgpool(torch.flatten(last_hidden_state, 2))
	latent_output = torch.flatten(latent_output, 1)

	if not return_dict:
	return tuple(
	v
	for v in [
	last_hidden_state,
	latent_output,
	all_reshaped_hidden_states,
	all_self_attentions,
	]
	if v is not None
	)

	return BaseModelOutputWithPooling(
	last_hidden_state=last_hidden_state,
	pooler_output=latent_output,
	hidden_states=all_reshaped_hidden_states,
	attentions=all_self_attentions,
	)


	CLAP_START_DOCSTRING = r"""
	This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
	library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
	etc.)

	This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
	Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
	and behavior.

	Parameters:
	config ([`ClapConfig`]): Model configuration class with all the parameters of the model.
	Initializing with a config file does not load the weights associated with the model, only the
	configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
	"""

	CLAP_TEXT_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
	it.

	Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
	config.max_position_embeddings - 1]`.

	[What are position IDs?](../glossary#position-ids)
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
	tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
	more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""

	CLAP_AUDIO_INPUTS_DOCSTRING = r"""
	Args:
	input_features (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
	Input audio features. This should be returnes by the [`ClapFeatureExtractor`] class that you can also
	retrieve from [`AutoFeatureExtractor`]. See [`ClapFeatureExtractor.__call__`] for details.
	is_longer (`torch.FloatTensor`, of shape `(batch_size, 1)`, optional):
	Whether the audio clip is longer than `max_length`. If `True`, a feature fusion will be enabled to enhance
	the features.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
	tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
	more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""

	CLAP_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
	it.

	Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
	config.max_position_embeddings - 1]`.

	[What are position IDs?](../glossary#position-ids)
	input_features (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
	Input audio features. This should be returnes by the [`ClapFeatureExtractor`] class that you can also
	retrieve from [`AutoFeatureExtractor`]. See [`ClapFeatureExtractor.__call__`] for details.
	return_loss (`bool`, optional):
	Whether or not to return the contrastive loss.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
	tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
	more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""


	class ClapProjectionLayer(nn.Module):
	def __init__(self, config: Union[ClapAudioConfig, ClapTextConfig]):
	super().__init__()
	self.config = config
	hidden_size = config.hidden_size
	projection_dim = config.projection_dim

	self.linear1 = nn.Linear(hidden_size, projection_dim)
	self.activation = ACT2FN[config.projection_hidden_act]
	self.linear2 = nn.Linear(projection_dim, projection_dim)

	def forward(self, hidden_states):
	hidden_states = self.linear1(hidden_states)
	hidden_states = self.activation(hidden_states)
	hidden_states = self.linear2(hidden_states)
	return hidden_states


	# Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings with Roberta->ClapText, persistent=False->persistent=True
	class ClapTextEmbeddings(nn.Module):
	"""
	Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
	"""

	# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
	def __init__(self, config):
	super().__init__()
	self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
	self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
	self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)

	# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
	# any TensorFlow checkpoint file
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)
	# position_ids (1, len position emb) is contiguous in memory and exported when serialized
	self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
	self.register_buffer(
	"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=True
	)
	self.register_buffer(
	"token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=True
	)

	# End copy
	self.padding_idx = config.pad_token_id
	self.position_embeddings = nn.Embedding(
	config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
	)

	def forward(
	self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
	):
	if position_ids is None:
	if input_ids is not None:
	# Create the position ids from the input token ids. Any padded tokens remain padded.
	position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
	else:
	position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)

	if input_ids is not None:
	input_shape = input_ids.size()
	else:
	input_shape = inputs_embeds.size()[:-1]

	seq_length = input_shape[1]

	# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
	# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
	# issue #5664
	if token_type_ids is None:
	if hasattr(self, "token_type_ids"):
	buffered_token_type_ids = self.token_type_ids[:, :seq_length]
	buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
	token_type_ids = buffered_token_type_ids_expanded
	else:
	token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)

	if inputs_embeds is None:
	inputs_embeds = self.word_embeddings(input_ids)
	token_type_embeddings = self.token_type_embeddings(token_type_ids)

	embeddings = inputs_embeds + token_type_embeddings
	if self.position_embedding_type == "absolute":
	position_embeddings = self.position_embeddings(position_ids)
	embeddings += position_embeddings
	embeddings = self.LayerNorm(embeddings)
	embeddings = self.dropout(embeddings)
	return embeddings

	def create_position_ids_from_inputs_embeds(self, inputs_embeds):
	"""
	We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.

	Args:
	inputs_embeds: torch.Tensor

	Returns: torch.Tensor
	"""
	input_shape = inputs_embeds.size()[:-1]
	sequence_length = input_shape[1]

	position_ids = torch.arange(
	self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
	)
	return position_ids.unsqueeze(0).expand(input_shape)


	# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->ClapText
	class ClapTextSelfAttention(nn.Module):
	def __init__(self, config, position_embedding_type=None):
	super().__init__()
	if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
	raise ValueError(
	f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
	f"heads ({config.num_attention_heads})"
	)

	self.num_attention_heads = config.num_attention_heads
	self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
	self.all_head_size = self.num_attention_heads * self.attention_head_size

	self.query = nn.Linear(config.hidden_size, self.all_head_size)
	self.key = nn.Linear(config.hidden_size, self.all_head_size)
	self.value = nn.Linear(config.hidden_size, self.all_head_size)

	self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
	self.position_embedding_type = position_embedding_type or getattr(
	config, "position_embedding_type", "absolute"
	)
	if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
	self.max_position_embeddings = config.max_position_embeddings
	self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)

	self.is_decoder = config.is_decoder

	def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
	new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
	x = x.view(new_x_shape)
	return x.permute(0, 2, 1, 3)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.Tensor]:
	mixed_query_layer = self.query(hidden_states)

	# If this is instantiated as a cross-attention module, the keys
	# and values come from an encoder; the attention mask needs to be
	# such that the encoder's padding tokens are not attended to.
	is_cross_attention = encoder_hidden_states is not None

	if is_cross_attention and past_key_value is not None:
	# reuse k,v, cross_attentions
	key_layer = past_key_value[0]
	value_layer = past_key_value[1]
	attention_mask = encoder_attention_mask
	elif is_cross_attention:
	key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
	value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
	attention_mask = encoder_attention_mask
	elif past_key_value is not None:
	key_layer = self.transpose_for_scores(self.key(hidden_states))
	value_layer = self.transpose_for_scores(self.value(hidden_states))
	key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
	value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
	else:
	key_layer = self.transpose_for_scores(self.key(hidden_states))
	value_layer = self.transpose_for_scores(self.value(hidden_states))

	query_layer = self.transpose_for_scores(mixed_query_layer)

	use_cache = past_key_value is not None
	if self.is_decoder:
	# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
	# Further calls to cross_attention layer can then reuse all cross-attention
	# key/value_states (first "if" case)
	# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
	# all previous decoder key/value_states. Further calls to uni-directional self-attention
	# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
	# if encoder bi-directional self-attention `past_key_value` is always `None`
	past_key_value = (key_layer, value_layer)

	# Take the dot product between "query" and "key" to get the raw attention scores.
	attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

	if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
	query_length, key_length = query_layer.shape[2], key_layer.shape[2]
	if use_cache:
	position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
	-1, 1
	)
	else:
	position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
	position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
	distance = position_ids_l - position_ids_r

	positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
	positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility

	if self.position_embedding_type == "relative_key":
	relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
	attention_scores = attention_scores + relative_position_scores
	elif self.position_embedding_type == "relative_key_query":
	relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
	relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
	attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key

	attention_scores = attention_scores / math.sqrt(self.attention_head_size)
	if attention_mask is not None:
	# Apply the attention mask is (precomputed for all layers in ClapTextModel forward() function)
	attention_scores = attention_scores + attention_mask

	# Normalize the attention scores to probabilities.
	attention_probs = nn.functional.softmax(attention_scores, dim=-1)

	# This is actually dropping out entire tokens to attend to, which might
	# seem a bit unusual, but is taken from the original Transformer paper.
	attention_probs = self.dropout(attention_probs)

	# Mask heads if we want to
	if head_mask is not None:
	attention_probs = attention_probs * head_mask

	context_layer = torch.matmul(attention_probs, value_layer)

	context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
	new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
	context_layer = context_layer.view(new_context_layer_shape)

	outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

	if self.is_decoder:
	outputs = outputs + (past_key_value,)
	return outputs


	# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
	class ClapTextSelfOutput(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.hidden_size, config.hidden_size)
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)

	def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
	hidden_states = self.dense(hidden_states)
	hidden_states = self.dropout(hidden_states)
	hidden_states = self.LayerNorm(hidden_states + input_tensor)
	return hidden_states


	# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->ClapText
	class ClapTextAttention(nn.Module):
	def __init__(self, config, position_embedding_type=None):
	super().__init__()
	self.self = ClapTextSelfAttention(config, position_embedding_type=position_embedding_type)
	self.output = ClapTextSelfOutput(config)
	self.pruned_heads = set()

	def prune_heads(self, heads):
	if len(heads) == 0:
	return
	heads, index = find_pruneable_heads_and_indices(
	heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
	)

	# Prune linear layers
	self.self.query = prune_linear_layer(self.self.query, index)
	self.self.key = prune_linear_layer(self.self.key, index)
	self.self.value = prune_linear_layer(self.self.value, index)
	self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

	# Update hyper params and store pruned heads
	self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
	self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
	self.pruned_heads = self.pruned_heads.union(heads)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.Tensor]:
	self_outputs = self.self(
	hidden_states,
	attention_mask,
	head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	past_key_value,
	output_attentions,
	)
	attention_output = self.output(self_outputs[0], hidden_states)
	outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
	return outputs


	# Copied from transformers.models.bert.modeling_bert.BertIntermediate
	class ClapTextIntermediate(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
	if isinstance(config.hidden_act, str):
	self.intermediate_act_fn = ACT2FN[config.hidden_act]
	else:
	self.intermediate_act_fn = config.hidden_act

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	hidden_states = self.dense(hidden_states)
	hidden_states = self.intermediate_act_fn(hidden_states)
	return hidden_states


	# Copied from transformers.models.bert.modeling_bert.BertOutput
	class ClapTextOutput(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)

	def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
	hidden_states = self.dense(hidden_states)
	hidden_states = self.dropout(hidden_states)
	hidden_states = self.LayerNorm(hidden_states + input_tensor)
	return hidden_states


	# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->ClapText
	class ClapTextLayer(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.chunk_size_feed_forward = config.chunk_size_feed_forward
	self.seq_len_dim = 1
	self.attention = ClapTextAttention(config)
	self.is_decoder = config.is_decoder
	self.add_cross_attention = config.add_cross_attention
	if self.add_cross_attention:
	if not self.is_decoder:
	raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
	self.crossattention = ClapTextAttention(config, position_embedding_type="absolute")
	self.intermediate = ClapTextIntermediate(config)
	self.output = ClapTextOutput(config)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.Tensor]:
	# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
	self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
	self_attention_outputs = self.attention(
	hidden_states,
	attention_mask,
	head_mask,
	output_attentions=output_attentions,
	past_key_value=self_attn_past_key_value,
	)
	attention_output = self_attention_outputs[0]

	# if decoder, the last output is tuple of self-attn cache
	if self.is_decoder:
	outputs = self_attention_outputs[1:-1]
	present_key_value = self_attention_outputs[-1]
	else:
	outputs = self_attention_outputs[1:] # add self attentions if we output attention weights

	cross_attn_present_key_value = None
	if self.is_decoder and encoder_hidden_states is not None:
	if not hasattr(self, "crossattention"):
	raise ValueError(
	f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
	" by setting `config.add_cross_attention=True`"
	)

	# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
	cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
	cross_attention_outputs = self.crossattention(
	attention_output,
	attention_mask,
	head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	cross_attn_past_key_value,
	output_attentions,
	)
	attention_output = cross_attention_outputs[0]
	outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights

	# add cross-attn cache to positions 3,4 of present_key_value tuple
	cross_attn_present_key_value = cross_attention_outputs[-1]
	present_key_value = present_key_value + cross_attn_present_key_value

	layer_output = apply_chunking_to_forward(
	self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
	)
	outputs = (layer_output,) + outputs

	# if decoder, return the attn key/values as the last output
	if self.is_decoder:
	outputs = outputs + (present_key_value,)

	return outputs

	def feed_forward_chunk(self, attention_output):
	intermediate_output = self.intermediate(attention_output)
	layer_output = self.output(intermediate_output, attention_output)
	return layer_output


	# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->ClapText
	class ClapTextEncoder(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.layer = nn.ModuleList([ClapTextLayer(config) for _ in range(config.num_hidden_layers)])
	self.gradient_checkpointing = False

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = False,
	output_hidden_states: Optional[bool] = False,
	return_dict: Optional[bool] = True,
	) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
	all_hidden_states = () if output_hidden_states else None
	all_self_attentions = () if output_attentions else None
	all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None

	if self.gradient_checkpointing and self.training:
	if use_cache:
	logger.warning_once(
	"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
	)
	use_cache = False

	next_decoder_cache = () if use_cache else None
	for i, layer_module in enumerate(self.layer):
	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	layer_head_mask = head_mask[i] if head_mask is not None else None
	past_key_value = past_key_values[i] if past_key_values is not None else None

	if self.gradient_checkpointing and self.training:

	def create_custom_forward(module):
	def custom_forward(*inputs):
	return module(*inputs, past_key_value, output_attentions)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(layer_module),
	hidden_states,
	attention_mask,
	layer_head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	)
	else:
	layer_outputs = layer_module(
	hidden_states,
	attention_mask,
	layer_head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	past_key_value,
	output_attentions,
	)

	hidden_states = layer_outputs[0]
	if use_cache:
	next_decoder_cache += (layer_outputs[-1],)
	if output_attentions:
	all_self_attentions = all_self_attentions + (layer_outputs[1],)
	if self.config.add_cross_attention:
	all_cross_attentions = all_cross_attentions + (layer_outputs[2],)

	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	if not return_dict:
	return tuple(
	v
	for v in [
	hidden_states,
	next_decoder_cache,
	all_hidden_states,
	all_self_attentions,
	all_cross_attentions,
	]
	if v is not None
	)
	return BaseModelOutputWithPastAndCrossAttentions(
	last_hidden_state=hidden_states,
	past_key_values=next_decoder_cache,
	hidden_states=all_hidden_states,
	attentions=all_self_attentions,
	cross_attentions=all_cross_attentions,
	)


	# Copied from transformers.models.bert.modeling_bert.BertPooler
	class ClapTextPooler(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.hidden_size, config.hidden_size)
	self.activation = nn.Tanh()

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	# We "pool" the model by simply taking the hidden state corresponding
	# to the first token.
	first_token_tensor = hidden_states[:, 0]
	pooled_output = self.dense(first_token_tensor)
	pooled_output = self.activation(pooled_output)
	return pooled_output


	class ClapPreTrainedModel(PreTrainedModel):
	"""
	An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
	models.
	"""

	config_class = ClapConfig
	base_model_prefix = "clap"
	supports_gradient_checkpointing = False

	def _init_weights(self, module):
	"""Initialize the weights"""
	factor = self.config.initializer_factor

	if isinstance(module, ClapTextEmbeddings):
	module.position_embeddings.weight.data.normal_(mean=0.0, std=factor * 0.02)
	module.token_type_embeddings.weight.data.normal_(mean=0.0, std=factor * 0.02)
	elif isinstance(module, ClapModel):
	nn.init.normal_(module.logit_scale_a, std=factor * 0.02)
	nn.init.normal_(module.logit_scale_t, std=factor * 0.02)
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=factor * 0.02)

	elif isinstance(module, nn.LayerNorm):
	module.bias.data.zero_()
	module.weight.data.fill_(1.0)
	elif isinstance(module, (nn.Conv2d, nn.Linear)):
	in_proj_std = (self.config.hidden_size*-0.5) ((2 * self.config.num_hidden_layers) ** -0.5) * factor
	nn.init.normal_(module.weight, std=in_proj_std)
	if module.bias is not None:
	module.bias.data.zero_()

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, ClapTextEncoder):
	module.gradient_checkpointing = value


	class ClapAudioModel(ClapPreTrainedModel):
	config_class = ClapAudioConfig
	main_input_name = "input_features"

	def __init__(self, config: ClapAudioConfig):
	super().__init__(config)
	self.audio_encoder = ClapAudioEncoder(config)
	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self) -> nn.Module:
	return self.audio_encoder.patch_embed.proj

	@add_start_docstrings_to_model_forward(CLAP_AUDIO_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=ClapAudioConfig)
	def forward(
	self,
	input_features: Optional[torch.FloatTensor] = None,
	is_longer: Optional[torch.BoolTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPooling]:
	r"""
	Returns:

	Examples:

	```python
	>>> from datasets import load_dataset
	>>> from transformers import AutoProcessor, ClapAudioModel

	>>> dataset = load_dataset("ashraq/esc50")
	>>> audio_sample = dataset["train"]["audio"][0]["array"]

	>>> model = ClapAudioModel.from_pretrained("laion/clap-htsat-fused")
	>>> processor = AutoProcessor.from_pretrained("laion/clap-htsat-fused")

	>>> inputs = processor(audios=audio_sample, return_tensors="pt")

	>>> outputs = model(**inputs)
	>>> last_hidden_state = outputs.last_hidden_state
	```"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)

	return self.audio_encoder(
	input_features=input_features,
	is_longer=is_longer,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)


	class ClapTextModel(ClapPreTrainedModel):
	"""

	The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
	cross-attention is added between the self-attention layers, following the architecture described in *Attention is
	all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz
	Kaiser and Illia Polosukhin.

	To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
	to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
	`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.

	.. _Attention is all you need: https://arxiv.org/abs/1706.03762

	"""

	config_class = ClapTextConfig

	# Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->ClapText
	def __init__(self, config, add_pooling_layer=True):
	super().__init__(config)
	self.config = config

	self.embeddings = ClapTextEmbeddings(config)
	self.encoder = ClapTextEncoder(config)

	self.pooler = ClapTextPooler(config) if add_pooling_layer else None

	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self):
	return self.embeddings.word_embeddings

	def set_input_embeddings(self, value):
	self.embeddings.word_embeddings = value

	# Copied from transformers.models.bert.modeling_bert.BertModel.forward
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
	r"""
	encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional):
	Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
	the model is configured as a decoder.
	encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
	the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.
	past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
	Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

	If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
	don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
	`decoder_input_ids` of shape `(batch_size, sequence_length)`.
	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
	`past_key_values`).
	"""
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	if self.config.is_decoder:
	use_cache = use_cache if use_cache is not None else self.config.use_cache
	else:
	use_cache = False

	if input_ids is not None and inputs_embeds is not None:
	raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
	elif input_ids is not None:
	self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
	input_shape = input_ids.size()
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

	batch_size, seq_length = input_shape
	device = input_ids.device if input_ids is not None else inputs_embeds.device

	# past_key_values_length
	past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

	if attention_mask is None:
	attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)

	if token_type_ids is None:
	if hasattr(self.embeddings, "token_type_ids"):
	buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
	buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
	token_type_ids = buffered_token_type_ids_expanded
	else:
	token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

	# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
	# ourselves in which case we just need to make it broadcastable to all heads.
	extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)

	# If a 2D or 3D attention mask is provided for the cross-attention
	# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
	if self.config.is_decoder and encoder_hidden_states is not None:
	encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
	encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
	if encoder_attention_mask is None:
	encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
	encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
	else:
	encoder_extended_attention_mask = None

	# Prepare head mask if needed
	# 1.0 in head_mask indicate we keep the head
	# attention_probs has shape bsz x n_heads x N x N
	# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
	# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
	head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

	embedding_output = self.embeddings(
	input_ids=input_ids,
	position_ids=position_ids,
	token_type_ids=token_type_ids,
	inputs_embeds=inputs_embeds,
	past_key_values_length=past_key_values_length,
	)
	encoder_outputs = self.encoder(
	embedding_output,
	attention_mask=extended_attention_mask,
	head_mask=head_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_extended_attention_mask,
	past_key_values=past_key_values,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	sequence_output = encoder_outputs[0]
	pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

	if not return_dict:
	return (sequence_output, pooled_output) + encoder_outputs[1:]

	return BaseModelOutputWithPoolingAndCrossAttentions(
	last_hidden_state=sequence_output,
	pooler_output=pooled_output,
	past_key_values=encoder_outputs.past_key_values,
	hidden_states=encoder_outputs.hidden_states,
	attentions=encoder_outputs.attentions,
	cross_attentions=encoder_outputs.cross_attentions,
	)


	@add_start_docstrings(CLAP_START_DOCSTRING)
	class ClapModel(ClapPreTrainedModel):
	config_class = ClapConfig

	def __init__(self, config: ClapConfig):
	super().__init__(config)

	if not isinstance(config.text_config, ClapTextConfig):
	raise ValueError(
	"config.text_config is expected to be of type ClapTextConfig but is of type"
	f" {type(config.text_config)}."
	)

	if not isinstance(config.audio_config, ClapAudioConfig):
	raise ValueError(
	"config.audio_config is expected to be of type ClapAudioConfig but is of type"
	f" {type(config.audio_config)}."
	)

	text_config = config.text_config
	audio_config = config.audio_config

	self.logit_scale_a = nn.Parameter(torch.tensor(math.log(config.logit_scale_init_value)))
	self.logit_scale_t = nn.Parameter(torch.tensor(math.log(config.logit_scale_init_value)))

	self.projection_dim = config.projection_dim

	self.text_model = ClapTextModel(text_config)
	self.text_projection = ClapProjectionLayer(text_config)

	self.audio_model = ClapAudioModel(audio_config)
	self.audio_projection = ClapProjectionLayer(audio_config)

	# Initialize weights and apply final processing
	self.post_init()

	@add_start_docstrings_to_model_forward(CLAP_TEXT_INPUTS_DOCSTRING)
	def get_text_features(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> torch.FloatTensor:
	r"""
	Returns:
	text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
	applying the projection layer to the pooled output of [`ClapTextModel`].

	Examples:

	```python
	>>> from transformers import AutoTokenizer, ClapModel

	>>> model = ClapModel.from_pretrained("laion/clap-htsat-unfused")
	>>> tokenizer = AutoTokenizer.from_pretrained("laion/clap-htsat-unfused")

	>>> inputs = tokenizer(["the sound of a cat", "the sound of a dog"], padding=True, return_tensors="pt")
	>>> text_features = model.get_text_features(**inputs)
	```"""
	# Use CLAP model's config for some fields (if specified) instead of those of audio & text components.
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	text_outputs = self.text_model(
	input_ids=input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	pooled_output = text_outputs[1] if return_dict is not None else text_outputs.pooler_output
	text_features = self.text_projection(pooled_output)
	text_features = F.normalize(text_features, dim=-1)

	return text_features

	@add_start_docstrings_to_model_forward(CLAP_AUDIO_INPUTS_DOCSTRING)
	def get_audio_features(
	self,
	input_features: Optional[torch.Tensor] = None,
	is_longer: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> torch.FloatTensor:
	r"""
	Returns:
	audio_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The audio embeddings obtained by
	applying the projection layer to the pooled output of [`ClapAudioModel`].

	Examples:

	```python
	>>> from transformers import AutoFeatureExtractor, ClapModel
	>>> import torch

	>>> model = ClapModel.from_pretrained("laion/clap-htsat-unfused")
	>>> feature_extractor = AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused")
	>>> random_audio = torch.rand((16_000))
	>>> inputs = feature_extractor(random_audio, return_tensors="pt")
	>>> audio_features = model.get_audio_features(**inputs)
	```"""
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	audio_outputs = self.audio_model(
	input_features=input_features,
	is_longer=is_longer,
	return_dict=return_dict,
	)

	pooled_output = audio_outputs[1] if not return_dict else audio_outputs.pooler_output

	audio_features = self.audio_projection(pooled_output)
	audio_features = F.normalize(audio_features, dim=-1)

	return audio_features

	@add_start_docstrings_to_model_forward(CLAP_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=ClapOutput, config_class=ClapConfig)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	input_features: Optional[torch.FloatTensor] = None,
	is_longer: Optional[torch.BoolTensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	return_loss: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, ClapOutput]:
	r"""
	Returns:

	Examples:

	```python
	>>> from datasets import load_dataset
	>>> from transformers import AutoProcessor, ClapModel

	>>> dataset = load_dataset("ashraq/esc50")
	>>> audio_sample = dataset["train"]["audio"][0]["array"]

	>>> model = ClapModel.from_pretrained("laion/clap-htsat-unfused")
	>>> processor = AutoProcessor.from_pretrained("laion/clap-htsat-unfused")

	>>> input_text = ["Sound of a dog", "Sound of vaccum cleaner"]

	>>> inputs = processor(text=input_text, audios=audio_sample, return_tensors="pt", padding=True)

	>>> outputs = model(**inputs)
	>>> logits_per_audio = outputs.logits_per_audio # this is the audio-text similarity score
	>>> probs = logits_per_audio.softmax(dim=-1) # we can take the softmax to get the label probabilities
	```"""
	# Use CLAP model's config for some fields (if specified) instead of those of audio & text components.
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	audio_outputs = self.audio_model(
	input_features=input_features,
	is_longer=is_longer,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	text_outputs = self.text_model(
	input_ids=input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	audio_embeds = audio_outputs[1] if not return_dict else audio_outputs.pooler_output
	audio_embeds = self.audio_projection(audio_embeds)

	text_embeds = text_outputs[1] if not return_dict else text_outputs.pooler_output
	text_embeds = self.text_projection(text_embeds)

	# normalized features
	audio_embeds = audio_embeds / audio_embeds.norm(p=2, dim=-1, keepdim=True)
	text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)

	# cosine similarity as logits
	logit_scale_text = self.logit_scale_t.exp()
	logit_scale_audio = self.logit_scale_a.exp()
	logits_per_text = torch.matmul(text_embeds, audio_embeds.t()) * logit_scale_text
	logits_per_audio = torch.matmul(audio_embeds, text_embeds.t()) * logit_scale_audio

	loss = None
	if return_loss:
	caption_loss = contrastive_loss(logits_per_text)
	audio_loss = contrastive_loss(logits_per_audio.t())
	loss = (caption_loss + audio_loss) / 2.0

	if not return_dict:
	output = (logits_per_audio, logits_per_text, text_embeds, audio_embeds, text_outputs, audio_outputs)
	return ((loss,) + output) if loss is not None else output

	return ClapOutput(
	loss=loss,
	logits_per_audio=logits_per_audio,
	logits_per_text=logits_per_text,
	text_embeds=text_embeds,
	audio_embeds=audio_embeds,
	text_model_output=text_outputs,
	audio_model_output=audio_outputs,
	)


	@add_start_docstrings(
	"""
	CLAP Text Model with a projection layer on top (a linear layer on top of the pooled output).
	""",
	CLAP_START_DOCSTRING,
	)
	class ClapTextModelWithProjection(ClapPreTrainedModel):
	config_class = ClapTextConfig

	def __init__(self, config: ClapTextConfig):
	super().__init__(config)
	self.text_model = ClapTextModel(config)
	self.text_projection = ClapProjectionLayer(config)
	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self) -> nn.Module:
	return self.text_model.embeddings.word_embeddings

	def set_input_embeddings(self, value):
	self.text_model.embeddings.word_embeddings = value

	@add_start_docstrings_to_model_forward(CLAP_TEXT_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=ClapTextModelOutput, config_class=ClapTextConfig)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, ClapTextModelOutput]:
	r"""
	Returns:

	Examples:

	```python
	>>> from transformers import AutoTokenizer, ClapTextModelWithProjection

	>>> model = ClapTextModelWithProjection.from_pretrained("laion/clap-htsat-unfused")
	>>> tokenizer = AutoTokenizer.from_pretrained("laion/clap-htsat-unfused")

	>>> inputs = tokenizer(["a sound of a cat", "a sound of a dog"], padding=True, return_tensors="pt")

	>>> outputs = model(**inputs)
	>>> text_embeds = outputs.text_embeds
	```"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	text_outputs = self.text_model(
	input_ids=input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	pooled_output = text_outputs[1] if not return_dict else text_outputs.pooler_output

	text_embeds = self.text_projection(pooled_output)

	if not return_dict:
	outputs = (text_embeds, text_outputs[0]) + text_outputs[2:]
	return tuple(output for output in outputs if output is not None)

	return ClapTextModelOutput(
	text_embeds=text_embeds,
	last_hidden_state=text_outputs.last_hidden_state,
	hidden_states=text_outputs.hidden_states,
	attentions=text_outputs.attentions,
	)


	@add_start_docstrings(
	"""
	CLAP Audio Model with a projection layer on top (a linear layer on top of the pooled output).
	""",
	CLAP_START_DOCSTRING,
	)
	class ClapAudioModelWithProjection(ClapPreTrainedModel):
	config_class = ClapAudioConfig
	main_input_name = "input_features"

	def __init__(self, config: ClapAudioConfig):
	super().__init__(config)
	self.audio_model = ClapAudioModel(config)
	self.audio_projection = ClapProjectionLayer(config)
	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self) -> nn.Module:
	return self.audio_model.audio_encoder.patch_embed.proj

	@add_start_docstrings_to_model_forward(CLAP_AUDIO_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=ClapAudioModelOutput, config_class=ClapAudioConfig)
	def forward(
	self,
	input_features: Optional[torch.FloatTensor] = None,
	is_longer: Optional[torch.BoolTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, ClapAudioModelOutput]:
	r"""
	Returns:

	Examples:

	```python
	>>> from datasets import load_dataset
	>>> from transformers import ClapAudioModelWithProjection, ClapProcessor

	>>> model = ClapAudioModelWithProjection.from_pretrained("laion/clap-htsat-fused")
	>>> processor = ClapProcessor.from_pretrained("laion/clap-htsat-fused")

	>>> dataset = load_dataset("ashraq/esc50")
	>>> audio_sample = dataset["train"]["audio"][0]["array"]

	>>> inputs = processor(audios=audio_sample, return_tensors="pt")
	>>> outputs = model(**inputs)
	>>> audio_embeds = outputs.audio_embeds
	```"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)

	audio_outputs = self.audio_model(
	input_features=input_features,
	is_longer=is_longer,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	pooled_output = audio_outputs[1] if not return_dict else audio_outputs.pooler_output

	audio_embeds = self.audio_projection(pooled_output)

	if not return_dict:
	outputs = (audio_embeds, audio_outputs[0]) + audio_outputs[2:]
	return tuple(output for output in outputs if output is not None)

	return ClapAudioModelOutput(
	audio_embeds=audio_embeds,
	last_hidden_state=audio_outputs.last_hidden_state,
	attentions=audio_outputs.attentions,
	hidden_states=audio_outputs.hidden_states,
	)