from typing import Dict, Any, List
from transformers import WhisperForConditionalGeneration, AutoProcessor, WhisperTokenizer, WhisperProcessor, pipeline, WhisperFeatureExtractor
import torch
#import io


#device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

#### USE of PIPELINE

class EndpointHandler:
    def __init__(self, path=""):
        #tokenizer = WhisperTokenizer.from_pretrained('openai/whisper-large', language="korean", task='transcribe')        
        #model = WhisperForConditionalGeneration.from_pretrained(path)
        #self.tokenizer = WhisperTokenizer.from_pretrained(path)
        #self.processor = WhisperProcessor.from_pretrained(path, language="korean", task='transcribe')
        #processor = AutoProcessor.from_pretrained(path)
        #self.pipe = pipeline("automatic-speech-recognition", model=model, tokenizer=processor.feature_extractor, feature_extractor=processor.feature_extractor)
        #feature_extractor = WhisperFeatureExtractor.from_pretrained('openai/whisper-large')
        self.pipe = pipeline(task='automatic-speech-recognition', model=path)
        

        
        # Move model to device
#        self.model.to(device)
        
    def __call__(self, data: Any) -> List[Dict[str, str]]:
        print('==========NEW PROCESS=========')
        transcribe = self.pipe
        transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="ko", task="transcribe")
        result = transcribe(data['inputs'])
        
        
        #print(f"{data}")
        #inputs = data.pop("inputs", data)
        #print(f'1. inputs: {inputs}')
        
        
        #inputs, _ = sf.read(io.BytesIO(data['inputs']))
        #inputs, _ = sf.read(data['inputs'])
        #print(f'2. inputs: {inputs}')
        
        # input_features = self.feature_extractor(inputs, sampling_rate=16000).input_features[0]
        # #print(f'3. input_features: {input_features}')
        # input_features_tensor = torch.tensor(input_features).unsqueeze(0)
        # input_ids = self.model.generate(input_features_tensor)
        # #(f'4. input_ids: {input_ids}')

        # transcription = self.tokenizer.batch_decode(input_ids, skip_special_tokens=True)[0]
        
        # #inputs, _ = torchaudio.load(inputs, normalize=True)
        # #input_features = self.processor.feature_extractor(inputs, sampling_rate=16000).input_features[0]

        #input_ids = self.processor.tokenizer(input_features, return_tensors="pt").input_ids
        #generated_ids = self.model.generate(input_ids)

        # #transcription = self.pipe(inputs, generate_kwargs  = {"task":"transcribe", "language":"<|ko|>"})
        # #transcription = self.pipe(inputs)
        # #print(input)
        # inputs = self.processor(inputs, retun_tensors="pt")
        # #input_features = {key: value.to(device) for key, value in input_features.items()}
        # input_features = inputs.input_features
        
        # generated_ids = self.model.generate(input_features)
        # #generated_ids = self.model.generate(inputs=input_features)
        # #self.model.generate = partial(self.model.generate, language="korean", task="transcribe")
        # #generated_ids = self.model.generate(inputs = input_features)

        #transcription = self.processor.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
        #transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

        return result