

"""simple docstring"""
class        _UpperCamelCase	:


  """simple docstring"""





  def __init__( self					: Any      )	->			None:


         '''simple docstring'''





         __magic_name__			:		dict[int, list[int]]        =     {}
         # indexmap keys are an index and it's values are the number at that index
         __magic_name__			:		dict[int, int]        =     {}



  def 							_UpperCAmelCase      ( self					: int       ,       snake_case					: list | str | range       ,       snake_case					: int      )	->			list[int]:


         '''simple docstring'''





         if isinstance(snake_case       ,       (range, str)      ):
                __magic_name__			:		int        =     list(snake_case      )
         elif not isinstance(snake_case       ,       snake_case      ):
                raise TypeError(
                    '''binary_search_delete() only accepts either a list, range or str'''      )

         __magic_name__			:		Optional[Any]        =     0
         __magic_name__			:		Optional[Any]        =     len(snake_case      ) - 1

         while low <= high:
                __magic_name__			:		int        =     (low + high) // 2
                if array[mid] == item:
                       array.pop(snake_case      )
                       return array
                elif array[mid] < item:
                       __magic_name__			:		int        =     mid + 1
                else:
                       __magic_name__			:		List[str]        =     mid - 1
         raise ValueError(
             '''Either the item is not in the array or the array was unsorted'''      )



  def 							_UpperCAmelCase      ( self					: Union[str, Any]       ,       snake_case					: list | str | range       ,       snake_case					: int      )	->			list[int]:


         '''simple docstring'''





         if isinstance(snake_case       ,       (range, str)      ):
                __magic_name__			:		str        =     list(snake_case      )
         elif not isinstance(snake_case       ,       snake_case      ):
                raise TypeError(
                    '''binary_search_insert() only accepts either a list, range or str'''      )

         __magic_name__			:		str        =     0
         __magic_name__			:		Union[str, Any]        =     len(snake_case      ) - 1

         while low <= high:
                __magic_name__			:		int        =     (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       ,       snake_case      )
                       return array
                elif array[mid] < index:
                       __magic_name__			:		Union[str, Any]        =     mid + 1
                else:
                       __magic_name__			:		Any        =     mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
         array.insert(snake_case       ,       snake_case      )
         return array



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: int       ,       snake_case					: int      )	->			None:


         '''simple docstring'''





         if index in self.indexmap:
                __magic_name__			:		Optional[Any]        =     self.indexmap[index]
                if len(self.numbermap[n]      ) == 1:
                       del self.numbermap[n]
                else:
                       __magic_name__			:		Tuple        =     self.binary_search_delete(self.numbermap[n]       ,       snake_case      )

        # Set new index
         __magic_name__			:		Optional[int]        =     number

         # Number not seen before or empty so insert number value
         if number not in self.numbermap:
                __magic_name__			:		Union[str, Any]        =     [index]

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




  def 							_UpperCAmelCase      ( self					: Any       ,       snake_case					: int      )	->			int:


         '''simple docstring'''





         return self.numbermap.get(snake_case       ,       [-1]      )[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()



"""simple docstring"""
import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					List[str]   )						->     Optional[int]:



       """simple docstring"""







       __magic_name__			:		int        =     TaConfig.from_json_file(lowerCamelCase   )
       print(f"""Building PyTorch model from configuration: {config}"""   )
       __magic_name__			:		Union[str, Any]        =     TaForConditionalGeneration(lowerCamelCase   )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""   )
       model.save_pretrained(lowerCamelCase   )


if __name__ == "__main__":
 A									=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 A									=						parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
