import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				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(lowercase__     ,  lowercase__ )







def     _a				(      lowercase__  :			Optional[int] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		list(s_dict.keys() )
     for key in keys:
          if "transformer_layers" in key:
               SCREAMING_SNAKE_CASE__				:				List[Any]			=		s_dict.pop(lowercase__ )
          elif "subsample" in key:
               SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		s_dict.pop(lowercase__ )







def     _a				(      lowercase__  :			List[str] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Any			=		emb.weight.shape
     SCREAMING_SNAKE_CASE__				:				int			=		nn.Linear(lowercase__     ,  lowercase__     ,  bias=lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Tuple			=		emb.weight.data
     return lin_layer







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.load(lowercase__     ,  map_location='cpu' )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		mam_aaa['args']
     SCREAMING_SNAKE_CASE__				:				Dict			=		mam_aaa['model']
     SCREAMING_SNAKE_CASE__				:				List[str]			=		state_dict['decoder.output_projection.weight']

     remove_ignore_keys_(lowercase__ )
     rename_keys(lowercase__ )

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		state_dict['decoder.embed_tokens.weight'].shape[0]

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		args.share_decoder_input_output_embed

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[int(lowercase__ ) for i in args.conv_kernel_sizes.split(',' )]
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		SpeechaTextConfig(
         vocab_size=lowercase__     ,  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(lowercase__ )     ,  conv_channels=args.conv_channels     ,  conv_kernel_sizes=lowercase__     ,  input_feat_per_channel=args.input_feat_per_channel     ,  input_channels=args.input_channels     ,  tie_word_embeddings=lowercase__     ,  num_beams=5     ,  max_length=2_00     ,  use_cache=lowercase__     ,  decoder_start_token_id=2     ,  early_stopping=lowercase__     ,  )

     SCREAMING_SNAKE_CASE__				:				Any			=		SpeechaTextForConditionalGeneration(lowercase__ )
     SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model.model.load_state_dict(lowercase__     ,  strict=lowercase__ )
     if len(lowercase__ ) > 0 and not set(lowercase__ ) <= {
         "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:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		make_linear_from_emb(model.model.decoder.embed_tokens )
     else:
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		lm_head_weights

     model.save_pretrained(lowercase__ )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:					List[Any]						=					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.")
      SCREAMING_SNAKE_CASE__		:					List[Any]						=					parser.parse_args()
      convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)




from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
      import accelerate







def     _a				(      lowercase__  :			List[str] ):







     '''simple docstring'''


     if not is_accelerate_available():
          return method
     SCREAMING_SNAKE_CASE__				:				str			=		version.parse(accelerate.__version__ ).base_version
     if version.parse(lowercase__ ) < version.parse('0.17.0' ):
          return method

     def wrapper(self  :			Optional[int]     ,  *lowercase__  :			int     ,  **lowercase__  :			Tuple ):
          if hasattr(self     ,  '_hf_hook' ) and hasattr(self._hf_hook     ,  'pre_forward' ):
               self._hf_hook.pre_forward(self )
          return method(self     ,  *lowercase__     ,  **lowercase__ )

     return wrapper



