"""simple docstring"""




_a      :		Any           =   {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

_a      :		int           =   {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       dict[int, list[int]]			,_lowerCamelCase						:       int			,_lowerCamelCase						:       list[bool]	)       ->      list[int]:
  _lowerCAmelCase						:       Union[str, Any]  			=     True
  _lowerCAmelCase						:       List[str]  			=     []

  for neighbour in graph[vert]:
    if not visited[neighbour]:
      order += topology_sort(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)

  order.append(_lowerCamelCase	)

  return order





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       dict[int, list[int]]			,_lowerCamelCase						:       int			,_lowerCamelCase						:       list[bool]	)       ->      list[int]:
  _lowerCAmelCase						:       int  			=     True
  _lowerCAmelCase						:       Union[str, Any]  			=     [vert]

  for neighbour in reversed_graph[vert]:
    if not visited[neighbour]:
      component += find_components(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)

  return component






def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       dict[int, list[int]]	)       ->      list[list[int]]:
  _lowerCAmelCase						:       Union[str, Any]  			=     len(_lowerCamelCase	) * [False]
  _lowerCAmelCase						:       dict[int, list[int]]  			=     {vert: [] for vert in range(len(_lowerCamelCase	)	)}

  for vert, neighbours in graph.items():
    for neighbour in neighbours:
      reversed_graph[neighbour].append(_lowerCamelCase	)

  _lowerCAmelCase						:       str  			=     []
  for i, was_visited in enumerate(_lowerCamelCase	):
    if not was_visited:
      order += topology_sort(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)

  _lowerCAmelCase						:       Union[str, Any]  			=     []
  _lowerCAmelCase						:       str  			=     len(_lowerCamelCase	) * [False]

  for i in range(len(_lowerCamelCase	)	):
    _lowerCAmelCase						:       Tuple  			=     order[len(_lowerCamelCase	) - i - 1]
    if not visited[vert]:
      _lowerCAmelCase						:       int  			=     find_components(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
      components_list.append(_lowerCamelCase	)

  return components_list









import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   # Initialise PyTorch model
   UpperCamelCase    :  Any							=	MobileBertConfig.from_json_file(snake_case__	)
   print(F"""Building PyTorch model from configuration: {config}"""	)
   UpperCamelCase    :  Tuple							=	MobileBertForPreTraining(snake_case__	)
   # Load weights from tf checkpoint
   UpperCamelCase    :  int							=	load_tf_weights_in_mobilebert(snake_case__       ,     snake_case__       ,     snake_case__	)
   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""	)
   torch.save(model.state_dict()       ,     snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       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(
        '''--mobilebert_config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained MobileBERT model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __UpperCAmelCase          =       parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



