






"""simple docstring"""



import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str,						UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					Any      )		->int:
      # Initialise PyTorch model
      A__						:  Optional[int]		  =  FunnelConfig.from_json_file(UpperCAmelCase__      )
      print(f'Building PyTorch model from configuration: {config}'      )
      A__						:  List[str]		  =  FunnelBaseModel(UpperCAmelCase__      ) if base_model else FunnelModel(UpperCAmelCase__      )

      # Load weights from tf checkpoint
      load_tf_weights_in_funnel(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )

      # Save pytorch-model
      print(f'Save PyTorch model to {pytorch_dump_path}'      )
      torch.save(model.state_dict(),						UpperCAmelCase__      )


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 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.'''
      )
      parser.add_argument(
          '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
      )
      A_    =							parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
      )














"""simple docstring"""



from __future__ import annotations

A_    =							10




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[int]      )		->list[int]:
						A__						:  Any		  =  1
						A__						:  Optional[int]		  =  max(UpperCAmelCase__      )
						while placement <= max_digit:
												# declare and initialize empty buckets
												A__						:  list[list]		  =  [[] for _ in range(UpperCAmelCase__      )]
												# split list_of_ints between the buckets
												for i in list_of_ints:
																		A__						:  List[Any]		  =  int((i / placement) % RADIX      )
																		buckets[tmp].append(UpperCAmelCase__      )
												# put each buckets' contents into list_of_ints
												A__						:  Union[str, Any]		  =  0
												for b in range(UpperCAmelCase__      ):
																		for i in buckets[b]:
																								A__						:  str		  =  i
																								a += 1
        # move to next
												placement *= RADIX
						return list_of_ints


if __name__ == "__main__":
						import doctest

						doctest.testmod()






