import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


lowerCamelCase_    :		Tuple							=	{
    """cola""": 2,
    """mnli""": 3,
    """mrpc""": 2,
    """sst-2""": 2,
    """sts-b""": 1,
    """qqp""": 2,
    """qnli""": 2,
    """rte""": 2,
    """wnli""": 2,
}


logging.set_verbosity_info()
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase=None						):
					# Initialise PyTorch model
					__a		 =       XLNetConfig.from_json_file(__lowerCamelCase						)

					__a		 =       finetuning_task.lower() if finetuning_task is not None else ''
					if finetuning_task in GLUE_TASKS_NUM_LABELS:
										print(f'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}'''						)
										__a		 =       finetuning_task
										__a		 =       GLUE_TASKS_NUM_LABELS[finetuning_task]
										__a		 =       XLNetForSequenceClassification(__lowerCamelCase						)
					elif "squad" in finetuning_task:
										__a		 =       finetuning_task
										__a		 =       XLNetForQuestionAnswering(__lowerCamelCase						)
					else:
										__a		 =       XLNetLMHeadModel(__lowerCamelCase						)

					# Load weights from tf checkpoint
					load_tf_weights_in_xlnet(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						)

					# Save pytorch-model
					__a		 =       os.path.join(__lowerCamelCase				,    __lowerCamelCase						)
					__a		 =       os.path.join(__lowerCamelCase				,    __lowerCamelCase						)
					print(f'''Save PyTorch model to {os.path.abspath(__lowerCamelCase						)}'''						)
					torch.save(model.state_dict()				,    __lowerCamelCase						)
					print(f'''Save configuration file to {os.path.abspath(__lowerCamelCase						)}'''						)
					with open(__lowerCamelCase				,    'w'				,    encoding='utf-8'						) as f:
										f.write(config.to_json_string()						)


if __name__ == "__main__":
		lowerCamelCase_    :		int							=	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(
		    """--xlnet_config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        """The config json file corresponding to the pre-trained XLNet model. \n"""
		        """This specifies the model architecture."""
		    ),
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""",
		    default=None,
		    type=str,
		    required=True,
		    help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
		)
		parser.add_argument(
		    """--finetuning_task""",
		    default=None,
		    type=str,
		    help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
		)
		lowerCamelCase_    :		Dict							=	parser.parse_args()
		print(args)

		convert_xlnet_checkpoint_to_pytorch(
		    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
		)





def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# Return True if there is node that has not iterated.
					__a		 =       [False] * len(__lowerCamelCase						)
					__a		 =       []
					queue.append(__lowerCamelCase						)
					__a		 =       True

					while queue:
										__a		 =       queue.pop(0						)
										for ind in range(len(graph[u]						)						):
															if visited[ind] is False and graph[u][ind] > 0:
																				queue.append(__lowerCamelCase						)
																				__a		 =       True
																				__a		 =       u

					return visited[t]
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# This array is filled by BFS and to store path
					__a		 =       [-1] * (len(__lowerCamelCase						))
					__a		 =       0
					while bfs(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
										__a		 =       float('Inf'						)
										__a		 =       sink

										while s != source:
															# Find the minimum value in select path
															__a		 =       min(__lowerCamelCase				,    graph[parent[s]][s]						)
															__a		 =       parent[s]

										max_flow += path_flow
										__a		 =       sink

										while v != source:
															__a		 =       parent[v]
															graph[u][v] -= path_flow
															graph[v][u] += path_flow
															__a		 =       parent[v]
					return max_flow


lowerCamelCase_    :		str							=	[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

lowerCamelCase_						,  lowerCamelCase_    :		Union[str, Any]							=	0, 5



print(ford_fulkerson(graph, source, sink))




