




import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[str]:
   snake_case				:    List[str]           =	[
       """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 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       str:
   snake_case				:    Optional[int]           =	list(s_dict.keys()					)
   for key in keys:
      if "transformer_layers" in key:
         snake_case				:    List[Any]           =	s_dict.pop(lowercase					)
      elif "subsample" in key:
         snake_case				:    int           =	s_dict.pop(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[Any]:
   snake_case     , snake_case				:    Dict           =	emb.weight.shape
   snake_case				:    Dict           =	nn.Linear(lowercase						,lowercase						,bias=lowercase					)
   snake_case				:    Dict           =	emb.weight.data
   return lin_layer




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Tuple:
   snake_case				:    str           =	torch.load(lowercase						,map_location="""cpu"""					)
   snake_case				:    Union[str, Any]           =	mam_aaa["""args"""]
   snake_case				:    int           =	mam_aaa["""model"""]
   snake_case				:    List[Any]           =	state_dict["""decoder.output_projection.weight"""]

   remove_ignore_keys_(lowercase					)
   rename_keys(lowercase					)

   snake_case				:    List[Any]           =	state_dict["""decoder.embed_tokens.weight"""].shape[0]

   snake_case				:    List[str]           =	args.share_decoder_input_output_embed

   snake_case				:    int           =	[int(lowercase					) for i in args.conv_kernel_sizes.split(""","""					)]
   snake_case				:    Optional[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=200						,use_cache=lowercase						,decoder_start_token_id=2						,early_stopping=lowercase						,)

   snake_case				:    Union[str, Any]           =	SpeechaTextForConditionalGeneration(lowercase					)
   snake_case     , snake_case				:    int           =	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:
      snake_case				:    Dict           =	make_linear_from_emb(model.model.decoder.embed_tokens					)
   else:
      snake_case				:    Any           =	lm_head_weights

   model.save_pretrained(lowercase					)


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











def 						SCREAMING_SNAKE_CASE__ (   lowercase = 1000					)  ->       int:
   snake_case				:    Optional[int]           =	3
   snake_case				:    List[Any]           =	0
   while a < n:
      if a % 3 == 0 or a % 5 == 0:
         result += a
      elif a % 15 == 0:
         result -= a
      a += 1
   return result


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





