





"""simple docstring"""


from ...processing_utils import ProcessorMixin



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[int]						  =       "SpeechT5FeatureExtractor"
  SCREAMING_SNAKE_CASE__				:str						  =       "SpeechT5Tokenizer"

  def __init__(		self :							str       , __a :							List[str]       , __a :							Optional[Any]						)		->					Optional[int]:
        super().__init__(__a       , __a						)

  def __call__(		self :							List[Any]       , *__a :							str       , **__a :							List[str]						)		->					str:
        _UpperCamelCase   :						Tuple    =							kwargs.pop("audio"       , __a						)
        _UpperCamelCase   :						List[Any]    =							kwargs.pop("text"       , __a						)
        _UpperCamelCase   :						Optional[Any]    =							kwargs.pop("text_target"       , __a						)
        _UpperCamelCase   :						int    =							kwargs.pop("audio_target"       , __a						)
        _UpperCamelCase   :						Union[str, Any]    =							kwargs.pop("sampling_rate"       , __a						)

        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:
              _UpperCamelCase   :						Tuple    =							self.feature_extractor(__a       , *__a       , sampling_rate=__a       , **__a						)
        elif text is not None:
              _UpperCamelCase   :						Tuple    =							self.tokenizer(__a       , **__a						)
        else:
              _UpperCamelCase   :						Union[str, Any]    =							None

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

        if inputs is None:
              return targets

        if targets is not None:
              _UpperCamelCase   :						Optional[int]    =							labels

              _UpperCamelCase   :						Optional[int]    =							targets.get("attention_mask"						)
              if decoder_attention_mask is not None:
                    _UpperCamelCase   :						int    =							decoder_attention_mask

        return inputs

  def  __SCREAMING_SNAKE_CASE					(		self :							Dict       , *__a :							List[str]       , **__a :							Union[str, Any]						)		->					List[Any]:
        _UpperCamelCase   :						Optional[Any]    =							kwargs.pop("input_values"       , __a						)
        _UpperCamelCase   :						List[Any]    =							kwargs.pop("input_ids"       , __a						)
        _UpperCamelCase   :						str    =							kwargs.pop("labels"       , __a						)

        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:
              _UpperCamelCase   :						Any    =							self.feature_extractor.pad(__a       , *__a       , **__a						)
        elif input_ids is not None:
              _UpperCamelCase   :						Tuple    =							self.tokenizer.pad(__a       , **__a						)
        else:
              _UpperCamelCase   :						Optional[Any]    =							None

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

        if inputs is None:
              return targets

        if targets is not None:
              _UpperCamelCase   :						Dict    =							labels

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

        return inputs

  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]       , *__a :							Optional[int]       , **__a :							Tuple						)		->					Optional[int]:
        return self.tokenizer.batch_decode(*__a       , **__a						)


  def  __SCREAMING_SNAKE_CASE					(		self :							int       , *__a :							Optional[Any]       , **__a :							Union[str, Any]						)		->					Dict:
        return self.tokenizer.decode(*__a       , **__a						)







"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def        lowercase__     (       lowercase_      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							[
          "encoder.version",
          "decoder.version",
          "model.encoder.version",
          "model.decoder.version",
          "_float_tensor",
          "decoder.output_projection.weight",
      ]
      for k in ignore_keys:
            state_dict.pop(lowercase_		,lowercase_      )




def        lowercase__     (       lowercase_      )       ->		List[Any]:




      """simple docstring"""

      _UpperCamelCase,	_UpperCamelCase   :						Union[str, Any]    =							emb.weight.shape
      _UpperCamelCase   :						str    =							nn.Linear(lowercase_		,lowercase_		,bias=lowercase_      )
      _UpperCamelCase   :						List[Any]    =							emb.weight.data
      return lin_layer




def        lowercase__     (       lowercase_		,lowercase_="facebook/mbart-large-en-ro"		,lowercase_=False		,lowercase_=False      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						int    =							torch.load(lowercase_		,map_location="cpu"      )["model"]
      remove_ignore_keys_(lowercase_      )
      _UpperCamelCase   :						str    =							state_dict["encoder.embed_tokens.weight"].shape[0]

      _UpperCamelCase   :						int    =							MBartConfig.from_pretrained(lowercase_		,vocab_size=lowercase_      )
      if mbart_aa and finetuned:
            _UpperCamelCase   :						Union[str, Any]    =							"relu"

      _UpperCamelCase   :						Optional[int]    =							state_dict["decoder.embed_tokens.weight"]
      _UpperCamelCase   :						List[str]    =							MBartForConditionalGeneration(lowercase_      )
      model.model.load_state_dict(lowercase_      )

      if finetuned:
            _UpperCamelCase   :						Optional[Any]    =							make_linear_from_emb(model.model.shared      )

      return model


if __name__ == "__main__":
  lowerCamelCase__						= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
  )
  parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument(
      "--hf_config",
      default="facebook/mbart-large-cc25",
      type=str,
      help="Which huggingface architecture to use: mbart-large",
  )
  parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
  parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
  lowerCamelCase__						= parser.parse_args()
  lowerCamelCase__						= convert_fairseq_mbart_checkpoint_from_disk(
      args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
  )
  model.save_pretrained(args.pytorch_dump_folder_path)
