'''simple docstring'''

class     _lowercase					:


 '''simple docstring'''






 def __init__(				self       :     List[str]							)  ->   None:
  # numbermap keys are the number and its values are lists of indexes sorted
  # in ascending order
  __lowerCAmelCase    							=						{}
  # indexmap keys are an index and it's values are the number at that index
  __lowerCAmelCase    							=						{}
 def a (				self       :     int				,	SCREAMING_SNAKE_CASE__       :     list | str | range				,	SCREAMING_SNAKE_CASE__       :     int							)  ->   list[int]:
  if isinstance(SCREAMING_SNAKE_CASE__				,	(range, str)							):
   __lowerCAmelCase    							=						list(SCREAMING_SNAKE_CASE__							)
  elif not isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							):
   raise TypeError(
       """binary_search_delete() only accepts either a list, range or str"""							)

  __lowerCAmelCase    							=						0
  __lowerCAmelCase    							=						len(SCREAMING_SNAKE_CASE__							) - 1

  while low <= high:
   __lowerCAmelCase    							=						(low + high) // 2
   if array[mid] == item:
    array.pop(SCREAMING_SNAKE_CASE__							)
    return array
   elif array[mid] < item:
    __lowerCAmelCase    							=						mid + 1
   else:
    __lowerCAmelCase    							=						mid - 1
  raise ValueError(
      """Either the item is not in the array or the array was unsorted"""							)
 def a (				self       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     list | str | range				,	SCREAMING_SNAKE_CASE__       :     int							)  ->   list[int]:
  if isinstance(SCREAMING_SNAKE_CASE__				,	(range, str)							):
   __lowerCAmelCase    							=						list(SCREAMING_SNAKE_CASE__							)
  elif not isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							):
   raise TypeError(
       """binary_search_insert() only accepts either a list, range or str"""							)

  __lowerCAmelCase    							=						0
  __lowerCAmelCase    							=						len(SCREAMING_SNAKE_CASE__							) - 1

  while low <= high:
   __lowerCAmelCase    							=						(low + high) // 2
   if array[mid] == index:
    # If the item already exists in the array,
    # insert it after the existing item
    array.insert(mid + 1				,	SCREAMING_SNAKE_CASE__							)
    return array
   elif array[mid] < index:
    __lowerCAmelCase    							=						mid + 1
   else:
    __lowerCAmelCase    							=						mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
  array.insert(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
  return array
 def a (				self       :     Tuple				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     int							)  ->   None:
  # Remove previous index
  if index in self.indexmap:
   __lowerCAmelCase    							=						self.indexmap[index]
   if len(self.numbermap[n]							) == 1:
    del self.numbermap[n]
   else:
    __lowerCAmelCase    							=						self.binary_search_delete(self.numbermap[n]				,	SCREAMING_SNAKE_CASE__							)

        # Set new index
  __lowerCAmelCase    							=						number

  # Number not seen before or empty so insert number value
  if number not in self.numbermap:
   __lowerCAmelCase    							=						[index]

  # Here we need to perform a binary search insertion in order to insert
  # The item in the correct place
  else:
   __lowerCAmelCase    							=						self.binary_search_insert(
       self.numbermap[number]				,	SCREAMING_SNAKE_CASE__							)





 def a (				self       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     int							)  ->   int:
  return self.numbermap.get(SCREAMING_SNAKE_CASE__				,	[-1]							)[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()







'''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 					UpperCamelCase_						( snake_case_	:	List[str]						,		snake_case_	:	Optional[Any]				) ->					Tuple:






 '''simple docstring'''

 __lowerCAmelCase    							=						CanineConfig()
 __lowerCAmelCase    							=						CanineModel(snake_case_				)
 model.eval()

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

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

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

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


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      :				Dict 				=    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






