






'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[str]     )	->      str:

   """simple docstring"""
   __a					  =     [
       '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__:						Any     )	->      Dict:

   """simple docstring"""
   __a					  =     list(s_dict.keys()     )
   for key in keys:
      if "transformer_layers" in key:
         __a					  =     s_dict.pop(SCREAMING_SNAKE_CASE__     )
      elif "subsample" in key:
         __a					  =     s_dict.pop(SCREAMING_SNAKE_CASE__     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Any     )	->      Optional[int]:

   """simple docstring"""
   __a  ,      __a					  =     emb.weight.shape
   __a					  =     nn.Linear(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       bias=SCREAMING_SNAKE_CASE__     )
   __a					  =     emb.weight.data
   return lin_layer



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Union[str, Any],       SCREAMING_SNAKE_CASE__:						List[str]     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     torch.load(SCREAMING_SNAKE_CASE__,       map_location='cpu'     )
   __a					  =     mam_aaa['args']
   __a					  =     mam_aaa['model']
   __a					  =     state_dict['decoder.output_projection.weight']

   remove_ignore_keys_(SCREAMING_SNAKE_CASE__     )
   rename_keys(SCREAMING_SNAKE_CASE__     )

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

   __a					  =     args.share_decoder_input_output_embed

   __a					  =     [int(SCREAMING_SNAKE_CASE__     ) for i in args.conv_kernel_sizes.split(','     )]
   __a					  =     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__,       )

   __a					  =     SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE__     )
   __a  ,      __a					  =     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:
      __a					  =     make_linear_from_emb(model.model.decoder.embed_tokens     )
   else:
      __a					  =     lm_head_weights

   model.save_pretrained(SCREAMING_SNAKE_CASE__     )


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   :							int         =   parser.parse_args()
 convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)







'''simple docstring'''



from math import isqrt



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int     )	->      bool:

   """simple docstring"""
   return all(number % divisor != 0 for divisor in range(2,       isqrt(SCREAMING_SNAKE_CASE__     ) + 1     )     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int = 10**6     )	->      int:

   """simple docstring"""
   __a					  =     0
   __a					  =     1
   __a					  =     7
   while prime_candidate < max_prime:
      primes_count += is_prime(SCREAMING_SNAKE_CASE__     )

      cube_index += 1
      prime_candidate += 6 * cube_index

   return primes_count


if __name__ == "__main__":
 print(f"""{solution() = }""")