





'''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__				:   Dict      =parser.parse_args()
      convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)













import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def    __lowerCamelCase					(							__a   :Dict    )		->				Any:






		"""simple docstring"""

		A__        =   [
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(__a    ,     __a    )







def    __lowerCamelCase					(							__a   :str    )		->				Union[str, Any]:






		"""simple docstring"""

		A__      ,			A__        =   emb.weight.shape
		A__        =   nn.Linear(__a    ,     __a    ,     bias=__a    )
		A__        =   emb.weight.data
		return lin_layer







def    __lowerCamelCase					(							__a   :str    )		->				List[str]:






		"""simple docstring"""

		A__        =   torch.load(__a    ,     map_location="""cpu"""    )
		A__        =   Namespace(**checkpoint["""cfg"""]["""model"""]    )
		A__        =   checkpoint["""model"""]
		remove_ignore_keys_(__a    )
		A__        =   state_dict["""decoder.embed_tokens.weight"""].shape[0]

		A__        =   {key.replace("""decoder"""    ,     """model"""    ): val for key, val in state_dict.items()}

		A__        =   XGLMConfig(
		    vocab_size=__a    ,     max_position_embeddings=args.max_target_positions    ,     num_layers=args.decoder_layers    ,     attention_heads=args.decoder_attention_heads    ,     ffn_dim=args.decoder_ffn_embed_dim    ,     d_model=args.decoder_embed_dim    ,     layerdrop=args.decoder_layerdrop    ,     dropout=args.dropout    ,     attention_dropout=args.attention_dropout    ,     activation_dropout=args.activation_dropout    ,     activation_function="""gelu"""    ,     scale_embedding=not args.no_scale_embedding    ,     tie_word_embeddings=args.share_decoder_input_output_embed    ,     )

		A__        =   XGLMForCausalLM(__a    )
		A__        =   model.load_state_dict(__a    ,     strict=__a    )
		print(__a    )
		A__        =   make_linear_from_emb(model.model.embed_tokens    )

		return model


if __name__ == "__main__":
					A    :					int =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
					parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
					A    :					str =      parser.parse_args()
					A    :					str =      convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)



