





'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				[
          """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(lowerCAmelCase				,      lowerCAmelCase    )
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				list(s_dict.keys()    )
      for key in keys:
            if "transformer_layers" in key:
                  _lowerCAmelCase					=				s_dict.pop(lowerCAmelCase    )
            elif "subsample" in key:
                  _lowerCAmelCase					=				s_dict.pop(lowerCAmelCase    )
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase	,				_lowerCAmelCase					=				emb.weight.shape
      _lowerCAmelCase					=				nn.Linear(lowerCAmelCase				,      lowerCAmelCase				,      bias=lowerCAmelCase    )
      _lowerCAmelCase					=				emb.weight.data
      return lin_layer
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				torch.load(lowerCAmelCase				,      map_location="""cpu"""    )
      _lowerCAmelCase					=				mam_aaa["""args"""]
      _lowerCAmelCase					=				mam_aaa["""model"""]
      _lowerCAmelCase					=				state_dict["""decoder.output_projection.weight"""]

      remove_ignore_keys_(lowerCAmelCase    )
      rename_keys(lowerCAmelCase    )

      _lowerCAmelCase					=				state_dict["""decoder.embed_tokens.weight"""].shape[0]

      _lowerCAmelCase					=				args.share_decoder_input_output_embed

      _lowerCAmelCase					=				[int(lowerCAmelCase    ) for i in args.conv_kernel_sizes.split(""","""    )]
      _lowerCAmelCase					=				SpeechaTextConfig(
          vocab_size=lowerCAmelCase				,      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(lowerCAmelCase    )				,      conv_channels=args.conv_channels				,      conv_kernel_sizes=lowerCAmelCase				,      input_feat_per_channel=args.input_feat_per_channel				,      input_channels=args.input_channels				,      tie_word_embeddings=lowerCAmelCase				,      num_beams=5				,      max_length=2_00				,      use_cache=lowerCAmelCase				,      decoder_start_token_id=2				,      early_stopping=lowerCAmelCase				,      )

      _lowerCAmelCase					=				SpeechaTextForConditionalGeneration(lowerCAmelCase    )
      _lowerCAmelCase	,				_lowerCAmelCase					=				model.model.load_state_dict(lowerCAmelCase				,      strict=lowerCAmelCase    )
      if len(lowerCAmelCase    ) > 0 and not set(lowerCAmelCase    ) <= {
          "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:
            _lowerCAmelCase					=				make_linear_from_emb(model.model.decoder.embed_tokens    )
      else:
            _lowerCAmelCase					=				lm_head_weights

      model.save_pretrained(lowerCAmelCase    )


if __name__ == "__main__":
      A__							:  List[Any]    =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.''')
      A__							:  str    =parser.parse_args()
      convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)













'''simple docstring'''






def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      if not isinstance(lowerCAmelCase				,      lowerCAmelCase    ):
            raise ValueError("""Input must be an integer"""    )
      if input_num <= 0:
            raise ValueError("""Input must be positive"""    )
      return sum(
          divisor for divisor in range(1				,      input_num // 2 + 1    ) if input_num % divisor == 0    )


if __name__ == "__main__":
      import doctest

      doctest.testmod()






