'''simple docstring'''






from __future__ import annotations






def    __UpperCAmelCase			(  a_:       List[Any]							):
   _UpperCAmelCase							:	Any							=   len(A_							)
   # We need to create solution object to save path.
   _UpperCAmelCase							:	Tuple							=   [[0 for _ in range(A_							)] for _ in range(A_							)]
   _UpperCAmelCase							:	Tuple							=   run_maze(A_,					0,					0,					A_							)
   if solved:
      print("\n".join(str(A_							) for row in solutions							)							)
   else:
      print("No solution exists!"							)
   return solved






def    __UpperCAmelCase			(  a_:       int,					a_:       str,					a_:       Optional[int],					a_:       str							):
   _UpperCAmelCase							:	List[str]							=   len(A_							)
   # Final check point.
   if i == j == (size - 1):
      _UpperCAmelCase							:	Optional[int]							=   1
      return True

   _UpperCAmelCase							:	Union[str, Any]							=   (not i < 0) and (not j < 0)  # Check lower bounds
   _UpperCAmelCase							:	Dict							=   (i < size) and (j < size)  # Check upper bounds

   if lower_flag and upper_flag:
      # check for already visited and block points.
      _UpperCAmelCase							:	Any							=   (not solutions[i][j]) and (not maze[i][j])
      if block_flag:
         # check visited
         _UpperCAmelCase							:	List[Any]							=   1

         # check for directions
         if (
             run_maze(A_,					i + 1,					A_,					A_							)
             or run_maze(A_,					A_,					j + 1,					A_							)
             or run_maze(A_,					i - 1,					A_,					A_							)
             or run_maze(A_,					A_,					j - 1,					A_							)
         ):
            return True

         _UpperCAmelCase							:	Dict							=   0
         return False
   return False


if __name__ == "__main__":
     import doctest

     doctest.testmod()

import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def a__    (       A_					):






  '''simple docstring'''





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

def a__    (       A_					):






  '''simple docstring'''





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

def a__    (       A_					):






  '''simple docstring'''





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

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

  __magic_name__				     =       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,     )

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

  return model


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

