


import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[Any]				)	->		Dict:

       """simple docstring"""

       a_					:		Dict							=  [
           '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(__A  ,    __A				)







def        SCREAMING_SNAKE_CASE_						(			__A :			str				)	->		Optional[int]:

       """simple docstring"""

       a_					:		Optional[int]							=  list(s_dict.keys()				)
       for key in keys:
              if "transformer_layers" in key:
                     a_					:		Tuple							=  s_dict.pop(__A				)
              elif "subsample" in key:
                     a_					:		int							=  s_dict.pop(__A				)







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[Any]				)	->		Dict:

       """simple docstring"""

       a_							,		a_					:		List[Any]							=  emb.weight.shape
       a_					:		Optional[Any]							=  nn.Linear(__A  ,    __A  ,    bias=__A				)
       a_					:		List[Any]							=  emb.weight.data
       return lin_layer







def        SCREAMING_SNAKE_CASE_						(			__A :			List[Any]  ,    __A :			List[str]				)	->		List[Any]:

       """simple docstring"""

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

       remove_ignore_keys_(__A				)
       rename_keys(__A				)

       a_					:		int							=  state_dict['decoder.embed_tokens.weight'].shape[0]

       a_					:		Dict							=  args.share_decoder_input_output_embed

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

       a_					:		Optional[int]							=  SpeechaTextForConditionalGeneration(__A				)
       a_							,		a_					:		Optional[int]							=  model.model.load_state_dict(__A  ,    strict=__A				)
       if len(__A				) > 0 and not set(__A				) <= {
           "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:
              a_					:		Dict							=  make_linear_from_emb(model.model.decoder.embed_tokens				)
       else:
              a_					:		Dict							=  lm_head_weights

       model.save_pretrained(__A				)


if __name__ == "__main__":
   UpperCAmelCase_      : int  					=       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_      : str  					=       parser.parse_args()
   convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)



from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

UpperCamelCase  							=						TypeVar('''T''')



class   snake_case_       (		Generic[T]       ):
   __A	:		deque[T]  # Cache store of keys
   __A	:		set[T]  # References of the keys in cache
   __A	:		int         =				10  # Maximum capacity of cache






   def __init__(      self				:     Union[str, Any]	,						lowercase_				:     int  )					->						None:
       lowercase__ :   int    					=				deque()
       lowercase__ :   str    					=				set()
       if not n:
           lowercase__ :   str    					=				sys.maxsize
       elif n < 0:
           raise ValueError("n should be an integer greater than 0."  )
       else:
           lowercase__ :   List[Any]    					=				n






   def       __UpperCamelCase     (      self				:     Dict	,						lowercase_				:     T  )					->						None:
       if x not in self.key_reference:
           if len(self.dq_store  ) == LRUCache._MAX_CAPACITY:
               lowercase__ :   Dict    					=				self.dq_store.pop()
               self.key_reference.remove(lowercase_  )
       else:
           self.dq_store.remove(lowercase_  )

       self.dq_store.appendleft(lowercase_  )
       self.key_reference.add(lowercase_  )






   def       __UpperCamelCase     (      self				:     Dict  )					->						None:
       for k in self.dq_store:
           print(lowercase_  )







   def __repr__(      self				:     Optional[int]  )					->						str:
       return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store  )}'''


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      UpperCamelCase  							=						LRUCache(4)
      lru_cache.refer('''A''')
      lru_cache.refer(2)
      lru_cache.refer(3)
      lru_cache.refer('''A''')
      lru_cache.refer(4)
      lru_cache.refer(5)
      lru_cache.display()

      print(lru_cache)
      assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"



