




'''simple docstring'''

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


a    :							Optional[Any]										=    {
    '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 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=None      )						->   List[Any]:

					'''simple docstring'''

					snake_case_		     =							XLNetConfig.from_json_file(__UpperCAmelCase      )

					snake_case_		     =							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}"      )
										snake_case_		     =							finetuning_task
										snake_case_		     =							GLUE_TASKS_NUM_LABELS[finetuning_task]
										snake_case_		     =							XLNetForSequenceClassification(__UpperCAmelCase      )
					elif "squad" in finetuning_task:
										snake_case_		     =							finetuning_task
										snake_case_		     =							XLNetForQuestionAnswering(__UpperCAmelCase      )
					else:
										snake_case_		     =							XLNetLMHeadModel(__UpperCAmelCase      )

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

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


if __name__ == "__main__":
							a    :							Union[str, Any]										=    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',
							)
							a    :							List[str]										=    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
							)







'''simple docstring'''

def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					snake_case_		     =							[False] * len(__UpperCAmelCase      )
					snake_case_		     =							[]
					queue.append(__UpperCAmelCase      )
					snake_case_		     =							True

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

					return visited[t]


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Tuple:

					'''simple docstring'''

					snake_case_		     =							[-1] * (len(__UpperCAmelCase      ))
					snake_case_		     =							0
					while bfs(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      ):
										snake_case_		     =							float('''Inf'''      )
										snake_case_		     =							sink

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

										max_flow += path_flow
										snake_case_		     =							sink

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


a    :							List[Any]										=    [
    [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],
]

a						,a    :							int										=    0, 5

print(ford_fulkerson(graph, source, sink))

