


'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration





def lowerCamelCase__     (		__lowerCamelCase			:				Dict				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[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 lowerCamelCase__     (		__lowerCamelCase			:				Union[str, Any]				):







   '''simple docstring'''




   _UpperCAmelCase       ,			_UpperCAmelCase  :  Optional[int]				       =emb.weight.shape
   _UpperCAmelCase  :  Any				       =nn.Linear(__lowerCamelCase			,       __lowerCamelCase			,       bias=__lowerCamelCase				)
   _UpperCAmelCase  :  int				       =emb.weight.data
   return lin_layer





def lowerCamelCase__     (		__lowerCamelCase			:				Optional[int]				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[Any]				       =torch.load(__lowerCamelCase			,       map_location='cpu'				)
   _UpperCAmelCase  :  Tuple				       =mam_aaa['args'] or mam_aaa['cfg']['model']
   _UpperCAmelCase  :  Tuple				       =mam_aaa['model']
   remove_ignore_keys_(__lowerCamelCase				)
   _UpperCAmelCase  :  Optional[Any]				       =state_dict['encoder.embed_tokens.weight'].shape[0]

   _UpperCAmelCase  :  Optional[Any]				       =MaMaaaConfig(
       vocab_size=__lowerCamelCase			,       max_position_embeddings=1_0_2_4			,       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			,       encoder_layerdrop=args.encoder_layerdrop			,       decoder_layerdrop=args.decoder_layerdrop			,       dropout=args.dropout			,       attention_dropout=args.attention_dropout			,       activation_dropout=args.activation_dropout			,       activation_function='relu'			,       )

   _UpperCAmelCase  :  str				       =state_dict['decoder.embed_tokens.weight']
   _UpperCAmelCase  :  Optional[int]				       =MaMaaaForConditionalGeneration(__lowerCamelCase				)
   model.model.load_state_dict(__lowerCamelCase			,       strict=__lowerCamelCase				)
   _UpperCAmelCase  :  Dict				       =make_linear_from_emb(model.model.shared				)

   return model


if __name__ == "__main__":
 lowercase				=argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('fairseq_path', type=str, help='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.')
 lowercase				=parser.parse_args()
 lowercase				=convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)





'''simple docstring'''






def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[Any]				       =set()

   # Replace all the whitespace in our sentence
   _UpperCAmelCase  :  Dict				       =input_str.replace(' '			,       ''				)
   for alpha in input_str:
      if "a" <= alpha.lower() <= "z":
         frequency.add(alpha.lower()				)
   return len(__lowerCamelCase				) == 2_6





def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   _UpperCAmelCase  :  Tuple				       =[False] * 2_6
   for char in input_str:
      if char.islower():
         _UpperCAmelCase  :  Dict				       =True
      elif char.isupper():
         _UpperCAmelCase  :  List[str]				       =True
   return all(__lowerCamelCase				)





def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   return len({char for char in input_str.lower() if char.isalpha()}				) == 2_6





def lowerCamelCase__     (		):







   '''simple docstring'''




   from timeit import timeit

   _UpperCAmelCase  :  List[Any]				       ='from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
   print(timeit('is_pangram()'			,       setup=__lowerCamelCase				)				)
   print(timeit('is_pangram_faster()'			,       setup=__lowerCamelCase				)				)
   print(timeit('is_pangram_fastest()'			,       setup=__lowerCamelCase				)				)
   # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
   # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()

