





"""simple docstring"""


import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Dict:

 '''simple docstring'''






 snake_case__    							=				CanineConfig()
 snake_case__    							=				CanineModel(_UpperCAmelCase	)
 model.eval()

 print(f"Building PyTorch model from configuration: {config}"	)

 # Load weights from tf checkpoint
 load_tf_weights_in_canine(_UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)

 # Save pytorch-model (weights and configuration)
 print(f"Save PyTorch model to {pytorch_dump_path}"	)
 model.save_pretrained(_UpperCAmelCase	)

 # Save tokenizer files
 snake_case__    							=				CanineTokenizer()
 print(f"Save tokenizer files to {pytorch_dump_path}"	)
 tokenizer.save_pretrained(_UpperCAmelCase	)


if __name__ == "__main__":
   a__					:     int							=				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""",
       default=None,
       type=str,
       required=True,
       help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
   )
   parser.add_argument(
       """--pytorch_dump_path""",
       default=None,
       type=str,
       required=True,
       help="""Path to a folder where the PyTorch model will be placed.""",
   )
   a__					:     List[str]							=				parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)








"""simple docstring"""


class      lowercase_				:

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				data
        snake_case__    							=				42
        snake_case__    							=				42





def 							a__			(  _UpperCAmelCase	)    ->  None:

 '''simple docstring'''






 snake_case__    							=				0
 snake_case__    							=				x





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  None:

 '''simple docstring'''






 snake_case__							,   snake_case__    							=				find_set(_UpperCAmelCase	), find_set(_UpperCAmelCase	)
 if x == y:
  return

 elif x.rank > y.rank:
  snake_case__    							=				x
 else:
  snake_case__    							=				y
  if x.rank == y.rank:
   y.rank += 1





def 							a__			(  _UpperCAmelCase	)    ->  Node:

 '''simple docstring'''






 if x != x.parent:
  snake_case__    							=				find_set(x.parent	)
 return x.parent





def 							a__			(  _UpperCAmelCase	)    ->  set:

 '''simple docstring'''






 snake_case__    							=				({0, 1, 2}, {3, 4, 5})
 for s in sets:
  if node.data in s:
   return s
 snake_case__    							=				f"{node.data} is not in {sets}"
 raise ValueError(_UpperCAmelCase	)





def 							a__			(  )    ->  None:

 '''simple docstring'''






 snake_case__    							=				[Node(_UpperCAmelCase	) for i in range(6	)]
 for v in vertex:
  make_set(_UpperCAmelCase	)

 union_set(vertex[0]	,     vertex[1]	)
 union_set(vertex[1]	,     vertex[2]	)
 union_set(vertex[3]	,     vertex[4]	)
 union_set(vertex[3]	,     vertex[5]	)

 for nodea in vertex:
  for nodea in vertex:
   if find_python_set(_UpperCAmelCase	).isdisjoint(find_python_set(_UpperCAmelCase	)	):
    assert find_set(_UpperCAmelCase	) != find_set(_UpperCAmelCase	)
   else:
    assert find_set(_UpperCAmelCase	) == find_set(_UpperCAmelCase	)


if __name__ == "__main__":
   test_disjoint_set()

