



import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def 						UpperCamelCase      (  snake_case__			:       Optional[int]      )      ->	int:
   UpperCamelCase       :    Optional[Any]		      =      [
       '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(_lowerCamelCase			,	_lowerCamelCase      )
def 						UpperCamelCase      (  snake_case__			:       List[str]      )      ->	Optional[int]:
   UpperCamelCase       :    Any		      =      list(s_dict.keys()      )
   for key in keys:
      if "transformer_layers" in key:
         UpperCamelCase       :    str		      =      s_dict.pop(_lowerCamelCase      )
      elif "subsample" in key:
         UpperCamelCase       :    Dict		      =      s_dict.pop(_lowerCamelCase      )
def 						UpperCamelCase      (  snake_case__			:       int      )      ->	Dict:
   UpperCamelCase      ,	UpperCamelCase       :    Union[str, Any]		      =      emb.weight.shape
   UpperCamelCase       :    Optional[Any]		      =      nn.Linear(_lowerCamelCase			,	_lowerCamelCase			,	bias=_lowerCamelCase      )
   UpperCamelCase       :    Union[str, Any]		      =      emb.weight.data
   return lin_layer
def 						UpperCamelCase      (  snake_case__			:       Optional[int]			,	snake_case__			:       Optional[Any]      )      ->	Any:
   UpperCamelCase       :    Union[str, Any]		      =      torch.load(_lowerCamelCase			,	map_location='cpu'      )
   UpperCamelCase       :    Dict		      =      mam_aaa['args']
   UpperCamelCase       :    Optional[Any]		      =      mam_aaa['model']
   UpperCamelCase       :    Union[str, Any]		      =      state_dict['decoder.output_projection.weight']

   remove_ignore_keys_(_lowerCamelCase      )
   rename_keys(_lowerCamelCase      )

   UpperCamelCase       :    Dict		      =      state_dict['decoder.embed_tokens.weight'].shape[0]

   UpperCamelCase       :    Optional[int]		      =      args.share_decoder_input_output_embed

   UpperCamelCase       :    Dict		      =      [int(_lowerCamelCase      ) for i in args.conv_kernel_sizes.split(','      )]
   UpperCamelCase       :    Any		      =      SpeechaTextConfig(
       vocab_size=_lowerCamelCase			,	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(_lowerCamelCase      )			,	conv_channels=args.conv_channels			,	conv_kernel_sizes=_lowerCamelCase			,	input_feat_per_channel=args.input_feat_per_channel			,	input_channels=args.input_channels			,	tie_word_embeddings=_lowerCamelCase			,	num_beams=5			,	max_length=200			,	use_cache=_lowerCamelCase			,	decoder_start_token_id=2			,	early_stopping=_lowerCamelCase			,	)

   UpperCamelCase       :    List[str]		      =      SpeechaTextForConditionalGeneration(_lowerCamelCase      )
   UpperCamelCase      ,	UpperCamelCase       :    str		      =      model.model.load_state_dict(_lowerCamelCase			,	strict=_lowerCamelCase      )
   if len(_lowerCamelCase      ) > 0 and not set(_lowerCamelCase      ) <= {
       "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:
      UpperCamelCase       :    Dict		      =      make_linear_from_emb(model.model.decoder.embed_tokens      )
   else:
      UpperCamelCase       :    List[Any]		      =      lm_head_weights

   model.save_pretrained(_lowerCamelCase      )


if __name__ == "__main__":
    __UpperCAmelCase			  =			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.''')
    __UpperCAmelCase			  =			parser.parse_args()
    convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)










'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""image_processor""", """tokenizer"""]
 __lowercase  						=							"""CLIPImageProcessor"""
 __lowercase  						=							("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__(							self :List[Any]		,			lowercase_ :List[str]=None		,			lowercase_ :Any=None		,			**lowercase_ :Union[str, Any]				)->				Optional[Any]:
     A__							=     None
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."		,			lowercase_		,			)
         A__							=     kwargs.pop("feature_extractor"				)

     A__							=     image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."				)
     if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."				)

     super().__init__(lowercase_		,			lowercase_				)
 def __call__(							self :Optional[Any]		,			lowercase_ :Optional[int]=None		,			lowercase_ :Dict=None		,			lowercase_ :List[Any]=None		,			**lowercase_ :Optional[Any]				)->				Union[str, Any]:

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

     if text is not None:
         A__							=     self.tokenizer(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if images is not None:
         A__							=     self.image_processor(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if text is not None and images is not None:
         A__							=     image_features.pixel_values
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**lowercase_				)		,			tensor_type=lowercase_				)
 def 	UpperCAmelCase_				(							self :int		,			*lowercase_ :Tuple		,			**lowercase_ :Any				)->				Dict:
     return self.tokenizer.batch_decode(*lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :Any		,			*lowercase_ :Any		,			**lowercase_ :Any				)->				List[str]:
     return self.tokenizer.decode(*lowercase_		,			**lowercase_				)
 @property
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[int]:
     A__							=     self.tokenizer.model_input_names
     A__							=     self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)
 @property
 def 	UpperCAmelCase_				(							self :List[Any]				)->				str:
     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,			lowercase_		,			)
     return self.image_processor_class



 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				List[Any]:
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,			lowercase_		,			)
     return self.image_processor



