



"""simple docstring"""







import os
from collections.abc import Iterator

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Union[str, Any] = "."						)						->   List[str]:

  """simple docstring"""




  for dir_path, dir_names, filenames in os.walk(_A						):
    lowerCAmelCase_   :				Dict								= [d for d in dir_names if d != 'scripts' and d[0] not in '._']
    for filename in filenames:
      if filename == "__init__.py":
        continue
      if os.path.splitext(_A						)[1] in (".py", ".ipynb"):
        yield os.path.join(_A    ,   _A						).lstrip('./'						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[str]						)						->   List[Any]:

  """simple docstring"""




  return f"{i * '  '}*" if i else "\n##"

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				Optional[Any]						)						->   Optional[int]:

  """simple docstring"""




  lowerCAmelCase_   :				List[Any]								= old_path.split(os.sep						)
  for i, new_part in enumerate(new_path.split(os.sep						)						):
    if (i + 1 > len(_A						) or old_parts[i] != new_part) and new_part:
      print(f"{md_prefix(_A						)} {new_part.replace('_'    ,   ' '						).title()}"						)
  return new_path

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int = "."						)						->   Any:

  """simple docstring"""




  lowerCAmelCase_   :				Tuple								= ''
  for filepath in sorted(good_file_paths(_A						)						):
    lowerCAmelCase_						,lowerCAmelCase_   :				Optional[Any]								= os.path.split(_A						)
    if filepath != old_path:
      lowerCAmelCase_   :				Optional[int]								= print_path(_A    ,   _A						)
    lowerCAmelCase_   :				Optional[Any]								= (filepath.count(os.sep						) + 1) if filepath else 0
    lowerCAmelCase_   :				int								= f"{filepath}/{filename}".replace(' '    ,   '%20'						)
    lowerCAmelCase_   :				Dict								= os.path.splitext(filename.replace('_'    ,   ' '						).title()						)[0]
    print(f"{md_prefix(_A						)} [{filename}]({url})"						)


if __name__ == "__main__":
     print_directory_md(""".""")











import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  [
						    '''decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(_A ,  _A  )





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_			,					lowerCAmelCase_      			=  emb.weight.shape
						lowerCAmelCase_      			=  nn.Linear(_A ,  _A ,  bias=_A  )
						lowerCAmelCase_      			=  emb.weight.data
						return lin_layer





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  torch.load(_A ,  map_location='''cpu'''  )
						lowerCAmelCase_      			=  Namespace(**checkpoint['''cfg''']['''model''']  )
						lowerCAmelCase_      			=  checkpoint['''model''']
						remove_ignore_keys_(_A  )
						lowerCAmelCase_      			=  state_dict['''decoder.embed_tokens.weight'''].shape[0]

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

						lowerCAmelCase_      			=  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 ,  )

						lowerCAmelCase_      			=  XGLMForCausalLM(_A  )
						lowerCAmelCase_      			=  model.load_state_dict(_A ,  strict=_A  )
						print(_A  )
						lowerCAmelCase_      			=  make_linear_from_emb(model.model.embed_tokens  )

						return model


if __name__ == "__main__":
						_A     =			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     =			parser.parse_args()
						_A     =			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)




