"""simple docstring"""
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->List[str]:
  a__:					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(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Optional[Any]:
  a__:					Union[str, Any]      							=     list(s_dict.keys()       )
  for key in keys:
    if "transformer_layers" in key:
      a__:					Any      							=     s_dict.pop(_SCREAMING_SNAKE_CASE       )
    elif "subsample" in key:
      a__:					Optional[int]      							=     s_dict.pop(_SCREAMING_SNAKE_CASE       )







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Tuple:
  a__							,   a__:					int      							=     emb.weight.shape
  a__:					Optional[Any]      							=     nn.Linear(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					bias=_SCREAMING_SNAKE_CASE       )
  a__:					List[Any]      							=     emb.weight.data
  return lin_layer







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->List[str]:
  a__:					List[str]      							=     torch.load(_SCREAMING_SNAKE_CASE		,					map_location='cpu'       )
  a__:					str      							=     mam_aaa['args']
  a__:					Dict      							=     mam_aaa['model']
  a__:					Tuple      							=     state_dict['decoder.output_projection.weight']

  remove_ignore_keys_(_SCREAMING_SNAKE_CASE       )
  rename_keys(_SCREAMING_SNAKE_CASE       )

  a__:					Optional[Any]      							=     state_dict['decoder.embed_tokens.weight'].shape[0]

  a__:					Optional[Any]      							=     args.share_decoder_input_output_embed

  a__:					Optional[int]      							=     [int(_SCREAMING_SNAKE_CASE       ) for i in args.conv_kernel_sizes.split(','       )]
  a__:					List[str]      							=     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__:					Tuple      							=     SpeechaTextForConditionalGeneration(_SCREAMING_SNAKE_CASE       )
  a__							,   a__:					Tuple      							=     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__:					int      							=     make_linear_from_emb(model.model.decoder.embed_tokens       )
  else:
    a__:					Dict      							=     lm_head_weights

  model.save_pretrained(_SCREAMING_SNAKE_CASE       )


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



"""simple docstring"""
def 						__a       (     _SCREAMING_SNAKE_CASE = 1000000       )     ->int:
  a__:					Any      							=     limit + 1
  a__:					List[str]      							=     [0] * limit
  for first_term in range(1		,					_SCREAMING_SNAKE_CASE       ):
    for n in range(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ):
      a__:					Any      							=     first_term + n / first_term
      if common_difference % 4:  # d must be divisble by 4
        continue
      else:
        common_difference /= 4
        if (
            first_term > common_difference
            and first_term < 4 * common_difference
        ):  # since x,y,z are positive integers
          frequency[n] += 1  # so z>0 and a>d ,also 4d<a

  a__:					Optional[int]      							=     sum(1 for x in frequency[1:limit] if x == 10       )

  return count


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


