





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def      UpperCAmelCase_    (    _UpperCAmelCase							: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(_UpperCAmelCase  ,						_UpperCAmelCase				)

def      UpperCAmelCase_    (    _UpperCAmelCase							:Any				)	->				Tuple:
     '''simple docstring'''


     A_   ,    A_          =       emb.weight.shape
     A_          =       nn.Linear(_UpperCAmelCase  ,						_UpperCAmelCase  ,						bias=_UpperCAmelCase				)
     A_          =       emb.weight.data
     return lin_layer

def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[Any]				)	->				List[str]:
     '''simple docstring'''


     A_          =       torch.load(_UpperCAmelCase  ,						map_location='''cpu'''				)
     A_          =       Namespace(**checkpoint['''cfg''']['''model''']				)
     A_          =       checkpoint['''model''']
     remove_ignore_keys_(_UpperCAmelCase				)
     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=_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  ,						)

     A_          =       XGLMForCausalLM(_UpperCAmelCase				)
     A_          =       model.load_state_dict(_UpperCAmelCase  ,						strict=_UpperCAmelCase				)
     print(_UpperCAmelCase				)
     A_          =       make_linear_from_emb(model.model.embed_tokens				)

     return model


if __name__ == "__main__":
      a__      :     List[str]         =		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__      :     Dict         =		parser.parse_args()
      a__      :     Optional[int]         =		convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
      model.save_pretrained(args.pytorch_dump_folder_path)










def      UpperCAmelCase_    (    _UpperCAmelCase							:list				)	->				list:
     '''simple docstring'''


     if len(_UpperCAmelCase				) < 2:
          return collection

     def circle_sort_util(_UpperCAmelCase							:list  ,						_UpperCAmelCase							:int  ,						_UpperCAmelCase							:int				) -> bool:
          A_          =       False

          if low == high:
               return swapped

          A_          =       low
          A_          =       high

          while left < right:
               if collection[left] > collection[right]:
                    A_   ,    A_          =       (
                        collection[right],
                        collection[left],
                    )
                    A_          =       True

               left += 1
               right -= 1

          if left == right and collection[left] > collection[right + 1]:
               A_   ,    A_          =       (
                   collection[right + 1],
                   collection[left],
               )

               A_          =       True

          A_          =       low + int((high - low) / 2				)
          A_          =       circle_sort_util(_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)
          A_          =       circle_sort_util(_UpperCAmelCase  ,						mid + 1  ,						_UpperCAmelCase				)

          return swapped or left_swap or right_swap

     A_          =       True

     while is_not_sorted is True:
          A_          =       circle_sort_util(_UpperCAmelCase  ,						0  ,						len(_UpperCAmelCase				) - 1				)

     return collection


if __name__ == "__main__":
      a__      :     Optional[Any]         =		input('Enter numbers separated by a comma:\n').strip()
      a__      :     List[str]         =		[int(item) for item in user_input.split(',')]
      print(circle_sort(unsorted))



