# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.

# Lint as: python3
"""SUPERB: Speech processing Universal PERformance Benchmark."""


import datasets


_CITATION = ""
_DESCRIPTION = ""


class AsrDummyConfig(datasets.BuilderConfig):
    """BuilderConfig for Superb."""

    def __init__(
        self,
        data_url,
        url,
        **kwargs,
    ):
        super().__init__(version=datasets.Version("1.9.0", ""), **kwargs)
        self.data_url = data_url
        self.url = url


class AsrDummy(datasets.GeneratorBasedBuilder):
    """Superb dataset."""

    BUILDER_CONFIGS = [
        AsrDummyConfig(
            name="conversational",
            description="",
            url="",
            data_url="",
        )
    ]

    DEFAULT_CONFIG_NAME = "conversational"

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "generated_responses": datasets.features.Sequence(
                        datasets.Value("string")
                    ),
                    "past_user_inputs": datasets.features.Sequence(
                        datasets.Value("string")
                    ),
                    "new_user_input": datasets.Value("string"),
                }
            ),
            supervised_keys=("file",),
            homepage=self.config.url,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={},
            ),
        ]

    def _generate_examples(self):
        """Generate examples."""
        # Only odd number to have user prompt
        textss = [
            ["Hello there", "Hello There", "Who are you ?"],
            ["Hello there"],
            [
                "Hello there",
                "Hello There",
                "Can you help me ?",
                "Yes what do you need ?",
                "I am having a problem with your product",
            ],
        ]
        for i, texts in enumerate(textss):
            key = str(i)
            past_user_inputs = texts[:-1:2]
            generated_responses = texts[1::2]
            new_user_input = texts[-1]
            example = {
                "id": key,
                "generated_responses": generated_responses,
                "past_user_inputs": past_user_inputs,
                "new_user_input": new_user_input,
            }
            yield key, example