import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]     )							-> Dict:







  """simple docstring"""





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



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				emb.weight.shape
  SCREAMING_SNAKE_CASE__      							=				nn.Linear(__UpperCamelCase			,					__UpperCamelCase			,					bias=__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				emb.weight.data
  return lin_layer



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Union[str, Any]     )							-> Optional[Any]:







  """simple docstring"""





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

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

  SCREAMING_SNAKE_CASE__      							=				XGLMConfig(
      vocab_size=__UpperCamelCase			,					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			,					)

  SCREAMING_SNAKE_CASE__      							=				XGLMForCausalLM(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				model.load_state_dict(__UpperCamelCase			,					strict=__UpperCamelCase     )
  print(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				make_linear_from_emb(model.model.embed_tokens     )

  return model


if __name__ == "__main__":
   __lowerCamelCase      :			Union[str, Any]							       =		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.''')
   __lowerCamelCase      :			Dict							       =		parser.parse_args()
   __lowerCamelCase      :			Optional[Any]							       =		convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
   model.save_pretrained(args.pytorch_dump_folder_path)

import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


__lowerCamelCase      :			str							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):




   def __init__(   self      :    List[str]	,     *_lowercase      :    Union[str, Any]	,     **_lowercase      :    Union[str, Any]				):



     """simple docstring"""






     warnings.warn(
         """The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use DonutImageProcessor instead."""	,     _lowercase	,     )
     super().__init__(*_lowercase	,     **_lowercase				)
