





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     List[str]:
  lowercase__			    =						[
      'encoder.version',
      'decoder.version',
      'model.encoder.version',
      'model.decoder.version',
      'decoder.output_projection.weight',
      '_float_tensor',
      'encoder.embed_positions._float_tensor',
      'decoder.embed_positions._float_tensor',
  ]
  for k in ignore_keys:
    state_dict.pop(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Tuple:
  lowercase__			    =						list(s_dict.keys()	)
  for key in keys:
    if "transformer_layers" in key:
      lowercase__			    =						s_dict.pop(_SCREAMING_SNAKE_CASE	)
    elif "subsample" in key:
      lowercase__			    =						s_dict.pop(_SCREAMING_SNAKE_CASE	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     List[str]:
  lowercase__    ,					lowercase__			    =						emb.weight.shape
  lowercase__			    =						nn.Linear(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		bias=_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						emb.weight.data
  return lin_layer




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Optional[Any]:
  lowercase__			    =						torch.load(_SCREAMING_SNAKE_CASE	,		map_location='cpu'	)
  lowercase__			    =						mam_aaa['args']
  lowercase__			    =						mam_aaa['model']
  lowercase__			    =						state_dict['decoder.output_projection.weight']

  remove_ignore_keys_(_SCREAMING_SNAKE_CASE	)
  rename_keys(_SCREAMING_SNAKE_CASE	)

  lowercase__			    =						state_dict['decoder.embed_tokens.weight'].shape[0]

  lowercase__			    =						args.share_decoder_input_output_embed

  lowercase__			    =						[int(_SCREAMING_SNAKE_CASE	) for i in args.conv_kernel_sizes.split(','	)]
  lowercase__			    =						SpeechaTextConfig(
      vocab_size=_SCREAMING_SNAKE_CASE	,		max_source_positions=args.max_source_positions	,		max_target_positions=args.max_target_positions	,		encoder_layers=args.encoder_layers	,		decoder_layers=args.decoder_layers	,		encoder_attention_heads=args.encoder_attention_heads	,		decoder_attention_heads=args.decoder_attention_heads	,		encoder_ffn_dim=args.encoder_ffn_embed_dim	,		decoder_ffn_dim=args.decoder_ffn_embed_dim	,		d_model=args.encoder_embed_dim	,		dropout=args.dropout	,		attention_dropout=args.attention_dropout	,		activation_dropout=args.activation_dropout	,		activation_function='relu'	,		num_conv_layers=len(_SCREAMING_SNAKE_CASE	)	,		conv_channels=args.conv_channels	,		conv_kernel_sizes=_SCREAMING_SNAKE_CASE	,		input_feat_per_channel=args.input_feat_per_channel	,		input_channels=args.input_channels	,		tie_word_embeddings=_SCREAMING_SNAKE_CASE	,		num_beams=5	,		max_length=200	,		use_cache=_SCREAMING_SNAKE_CASE	,		decoder_start_token_id=2	,		early_stopping=_SCREAMING_SNAKE_CASE	,		)

  lowercase__			    =						SpeechaTextForConditionalGeneration(_SCREAMING_SNAKE_CASE	)
  lowercase__    ,					lowercase__			    =						model.model.load_state_dict(_SCREAMING_SNAKE_CASE	,		strict=_SCREAMING_SNAKE_CASE	)
  if len(_SCREAMING_SNAKE_CASE	) > 0 and not set(_SCREAMING_SNAKE_CASE	) <= {
      "encoder.embed_positions.weights",
      "decoder.embed_positions.weights",
  }:
    raise ValueError(
        'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
        F""" but all the following weights are missing {missing}"""	)

  if tie_embeds:
    lowercase__			    =						make_linear_from_emb(model.model.decoder.embed_tokens	)
  else:
    lowercase__			    =						lm_head_weights

  model.save_pretrained(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      lowercase_					     =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("""--fairseq_path""", type=str, help="""Path to the fairseq model (.pt) file.""")
      parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
      lowercase_					     =	parser.parse_args()
      convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)









from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Tuple				      = 'ClapFeatureExtractor'
   _UpperCamelCase					:	Union[str, Any]				      = ('RobertaTokenizer', 'RobertaTokenizerFast')


   def __init__(					self    :       List[Any]    ,  a    :       int    ,  a    :       str )->     Any:





     """simple docstring"""







     super().__init__(a    ,  a )


   def __call__(					self    :       Any    ,  a    :       Tuple=None    ,  a    :       Optional[int]=None    ,  a    :       int=None    ,  **a    :       Optional[int] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						kwargs.pop('sampling_rate'    ,  a )

     if text is None and audios is None:
       raise ValueError('You have to specify either text or audios. Both cannot be none.' )

     if text is not None:
       lowercase__			    =						self.tokenizer(a    ,  return_tensors=a    ,  **a )

     if audios is not None:
       lowercase__			    =						self.feature_extractor(
           a    ,  sampling_rate=a    ,  return_tensors=a    ,  **a )

     if text is not None and audios is not None:
       lowercase__			    =						audio_features.input_features
       return encoding
     elif text is not None:
       return encoding
     else:
       return BatchEncoding(data=dict(**a )    ,  tensor_type=a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  *a    :       Dict    ,  **a    :       int )->     Optional[int]:





     """simple docstring"""







     return self.tokenizer.batch_decode(*a    ,  **a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  *a    :       int    ,  **a    :       Dict )->     Dict:





     """simple docstring"""







     return self.tokenizer.decode(*a    ,  **a )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						self.tokenizer.model_input_names
     lowercase__			    =						self.feature_extractor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )


