





'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=[
      '''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   ):


  """simple docstring"""

  _UpperCamelCase       			=list(s_dict.keys()   )
  for key in keys:
    if "transformer_layers" in key:
      _UpperCamelCase       			=s_dict.pop(__SCREAMING_SNAKE_CASE   )
    elif "subsample" in key:
      _UpperCamelCase       			=s_dict.pop(__SCREAMING_SNAKE_CASE   )


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase			,     _UpperCamelCase       			=emb.weight.shape
  _UpperCamelCase       			=nn.Linear(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      bias=__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=emb.weight.data
  return lin_layer


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=torch.load(__SCREAMING_SNAKE_CASE  ,      map_location='''cpu'''   )
  _UpperCamelCase       			=mam_aaa['''args''']
  _UpperCamelCase       			=mam_aaa['''model''']
  _UpperCamelCase       			=state_dict['''decoder.output_projection.weight''']

  remove_ignore_keys_(__SCREAMING_SNAKE_CASE   )
  rename_keys(__SCREAMING_SNAKE_CASE   )

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

  _UpperCamelCase       			=args.share_decoder_input_output_embed

  _UpperCamelCase       			=[int(__SCREAMING_SNAKE_CASE   ) for i in args.conv_kernel_sizes.split(''','''   )]
  _UpperCamelCase       			=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  ,      )

  _UpperCamelCase       			=SpeechaTextForConditionalGeneration(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase			,     _UpperCamelCase       			=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:
    _UpperCamelCase       			=make_linear_from_emb(model.model.decoder.embed_tokens   )
  else:
    _UpperCamelCase       			=lm_head_weights

  model.save_pretrained(__SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      __lowerCamelCase						: Optional[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.')
      __lowerCamelCase						: int		=   parser.parse_args()
      convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)













'''simple docstring'''






def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  return 1 if digit in (0, 1) else (digit * factorial(digit - 1   ))


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=0
  _UpperCamelCase       			=number
  while duplicate > 0:
    _UpperCamelCase			,     _UpperCamelCase       			=divmod(__SCREAMING_SNAKE_CASE  ,      10   )
    fact_sum += factorial(__SCREAMING_SNAKE_CASE   )
  return fact_sum == number


if __name__ == "__main__":
      print('Program to check whether a number is a Krisnamurthy Number or not.')
      __lowerCamelCase						: Optional[Any]		=   int(input('Enter number: ').strip())
      print(
          F"""{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number."""
      )






