






import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= BigBirdConfig.from_json_file(_lowercase )
					print(f"""Building PyTorch model from configuration: {config}""" )

					if is_trivia_qa:
										_A								= BigBirdForQuestionAnswering(_lowercase )
					else:
										_A								= BigBirdForPreTraining(_lowercase )

					# Load weights from tf checkpoint
					load_tf_weights_in_big_bird(_lowercase					,			_lowercase					,			is_trivia_qa=_lowercase )

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


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(
				    '--big_bird_config_file',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        'The config json file corresponding to the pre-trained BERT 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.'
				)
				parser.add_argument(
				    '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
				)
				__A      =				parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
				)











def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= [0] * len(_lowercase )
					_A								= []
					_A								= []
					_A								= 0

					for values in graph.values():
										for i in values:
															indegree[i] += 1

					for i in range(len(_lowercase ) ):
										if indegree[i] == 0:
															queue.append(_lowercase )

					while queue:
										_A								= queue.pop(0 )
										cnt += 1
										topo.append(_lowercase )
										for x in graph[vertex]:
															indegree[x] -= 1
															if indegree[x] == 0:
																				queue.append(_lowercase )

					if cnt != len(_lowercase ):
										print('''Cycle exists''' )
					else:
										print(_lowercase )


# Adjacency List of Graph
__A      =				{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}


topological_sort(graph)



