





from ...processing_utils import ProcessorMixin





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    'SpeechT5FeatureExtractor'
       __snake_case 			=    'SpeechT5Tokenizer'




       def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     Optional[Any]:
            super().__init__(__lowerCamelCase    ,						__lowerCamelCase							)




       def __call__(      self    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)			->     List[Any]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							kwargs.pop("audio"    ,						__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							kwargs.pop("text"    ,						__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							kwargs.pop("text_target"    ,						__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							kwargs.pop("audio_target"    ,						__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							kwargs.pop("sampling_rate"    ,						__lowerCamelCase							)

            if audio is not None and text is not None:
                 raise ValueError(
                     "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"							)
            if audio_target is not None and text_target is not None:
                 raise ValueError(
                     "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"							)
            if audio is None and audio_target is None and text is None and text_target is None:
                 raise ValueError(
                     "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."							)

            if audio is not None:
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.feature_extractor(__lowerCamelCase    ,						*__lowerCamelCase    ,						sampling_rate=__lowerCamelCase    ,						**__lowerCamelCase							)
            elif text is not None:
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.tokenizer(__lowerCamelCase    ,						**__lowerCamelCase							)
            else:
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							None

            if audio_target is not None:
                 _SCREAMING_SNAKE_CASE       :	Any						    =							self.feature_extractor(audio_target=__lowerCamelCase    ,						*__lowerCamelCase    ,						sampling_rate=__lowerCamelCase    ,						**__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	str						    =							targets["input_values"]
            elif text_target is not None:
                 _SCREAMING_SNAKE_CASE       :	Any						    =							self.tokenizer(__lowerCamelCase    ,						**__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							targets["input_ids"]
            else:
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							None

            if inputs is None:
                 return targets

            if targets is not None:
                 _SCREAMING_SNAKE_CASE       :	int						    =							labels

                 _SCREAMING_SNAKE_CASE       :	Dict						    =							targets.get("attention_mask"							)
                 if decoder_attention_mask is not None:
                      _SCREAMING_SNAKE_CASE       :	Any						    =							decoder_attention_mask

            return inputs




       def     UpperCamelCase_			(      self    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							kwargs.pop("input_values"    ,						__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							kwargs.pop("input_ids"    ,						__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							kwargs.pop("labels"    ,						__lowerCamelCase							)

            if input_values is not None and input_ids is not None:
                 raise ValueError("Cannot process both `input_values` and `input_ids` inputs."							)
            if input_values is None and input_ids is None and labels is None:
                 raise ValueError(
                     "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."							)

            if input_values is not None:
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							self.feature_extractor.pad(__lowerCamelCase    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)
            elif input_ids is not None:
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.tokenizer.pad(__lowerCamelCase    ,						**__lowerCamelCase							)
            else:
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							None

            if labels is not None:
                 if "input_ids" in labels or (isinstance(__lowerCamelCase    ,						__lowerCamelCase							) and "input_ids" in labels[0]):
                      _SCREAMING_SNAKE_CASE       :	int						    =							self.tokenizer.pad(__lowerCamelCase    ,						**__lowerCamelCase							)
                      _SCREAMING_SNAKE_CASE       :	str						    =							targets["input_ids"]
                 else:
                      _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.feature_extractor.feature_size
                      _SCREAMING_SNAKE_CASE       :	Dict						    =							self.feature_extractor.num_mel_bins
                      _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.feature_extractor.pad(__lowerCamelCase    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)
                      _SCREAMING_SNAKE_CASE       :	Any						    =							feature_size_hack
                      _SCREAMING_SNAKE_CASE       :	str						    =							targets["input_values"]
            else:
                 _SCREAMING_SNAKE_CASE       :	Any						    =							None

            if inputs is None:
                 return targets

            if targets is not None:
                 _SCREAMING_SNAKE_CASE       :	str						    =							labels

                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							targets.get("attention_mask"							)
                 if decoder_attention_mask is not None:
                      _SCREAMING_SNAKE_CASE       :	Any						    =							decoder_attention_mask

            return inputs




       def     UpperCamelCase_			(      self    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)			->     List[Any]:
            return self.tokenizer.batch_decode(*__lowerCamelCase    ,						**__lowerCamelCase							)







       def     UpperCamelCase_			(      self    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)			->     List[Any]:
            return self.tokenizer.decode(*__lowerCamelCase    ,						**__lowerCamelCase							)






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     # Construct model
     if gpta_config_file == "":
          _SCREAMING_SNAKE_CASE       :	str						    =							GPTaConfig()
     else:
          _SCREAMING_SNAKE_CASE       :	int						    =							GPTaConfig.from_json_file(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							GPTaModel(__lowerCamelCase	)

     # Load weights from numpy
     load_tf_weights_in_gpta(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	)

     # Save pytorch-model
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							pytorch_dump_folder_path + "/" + WEIGHTS_NAME
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							pytorch_dump_folder_path + "/" + CONFIG_NAME
     print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""	)
     torch.save(model.state_dict(),       __lowerCamelCase	)
     print(f"""Save configuration file to {pytorch_config_dump_path}"""	)
     with open(__lowerCamelCase,       "w",       encoding="utf-8"	) as f:
          f.write(config.to_json_string()	)


if __name__ == "__main__":
       UpperCamelCase__      =argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       parser.add_argument(
           '--gpt2_config_file',
           default='',
           type=str,
           help=(
               'An optional config json file corresponding to the pre-trained OpenAI model. \n'
               'This specifies the model architecture.'
           ),
       )
       UpperCamelCase__      =parser.parse_args()
       convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)