


"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def      lowercase			(				A_       )->      Optional[int]:
  '''simple docstring'''







  a						:		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(A_  ,   A_       )


def      lowercase			(				A_       )->      Optional[Any]:
  '''simple docstring'''







  a						:		Tuple      							=			list(s_dict.keys()       )
  for key in keys:
    if "transformer_layers" in key:
      a						:		Union[str, Any]      							=			s_dict.pop(A_       )
    elif "subsample" in key:
      a						:		Optional[Any]      							=			s_dict.pop(A_       )


def      lowercase			(				A_       )->      Optional[int]:
  '''simple docstring'''







  a							,		a						:		int      							=			emb.weight.shape
  a						:		List[str]      							=			nn.Linear(A_  ,   A_  ,   bias=A_       )
  a						:		List[Any]      							=			emb.weight.data
  return lin_layer


def      lowercase			(				A_  ,   A_       )->      Dict:
  '''simple docstring'''







  a						:		Optional[Any]      							=			torch.load(A_  ,   map_location="cpu"       )
  a						:		Union[str, Any]      							=			mam_aaa["args"]
  a						:		Tuple      							=			mam_aaa["model"]
  a						:		Tuple      							=			state_dict["decoder.output_projection.weight"]

  remove_ignore_keys_(A_       )
  rename_keys(A_       )

  a						:		str      							=			state_dict["decoder.embed_tokens.weight"].shape[0]

  a						:		List[Any]      							=			args.share_decoder_input_output_embed

  a						:		str      							=			[int(A_       ) for i in args.conv_kernel_sizes.split(","       )]
  a						:		Optional[int]      							=			SpeechaTextConfig(
      vocab_size=A_  ,   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(A_       )  ,   conv_channels=args.conv_channels  ,   conv_kernel_sizes=A_  ,   input_feat_per_channel=args.input_feat_per_channel  ,   input_channels=args.input_channels  ,   tie_word_embeddings=A_  ,   num_beams=5  ,   max_length=200  ,   use_cache=A_  ,   decoder_start_token_id=2  ,   early_stopping=A_  ,   )

  a						:		Dict      							=			SpeechaTextForConditionalGeneration(A_       )
  a							,		a						:		List[Any]      							=			model.model.load_state_dict(A_  ,   strict=A_       )
  if len(A_       ) > 0 and not set(A_       ) <= {
      "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						:		Union[str, Any]      							=			make_linear_from_emb(model.model.decoder.embed_tokens       )
  else:
    a						:		Union[str, Any]      							=			lm_head_weights

  model.save_pretrained(A_       )


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      lowercase			(				A_       )->      str:
  '''simple docstring'''







  if isinstance(A_  ,   A_       ):
    raise TypeError("'float' object cannot be interpreted as an integer"       )
  if isinstance(A_  ,   A_       ):
    raise TypeError("'str' object cannot be interpreted as an integer"       )

  if num == 0:
    return "0b0"

  a						:		Optional[Any]      							=			False

  if num < 0:
    a						:		Tuple      							=			True
    a						:		str      							=			-num

  a						:		list[int]      							=			[]
  while num > 0:
    binary.insert(0  ,   num % 2       )
    num >>= 1

  if negative:
    return "-0b" + "".join(str(A_       ) for e in binary       )

  return "0b" + "".join(str(A_       ) for e in binary       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






