
import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		List[Any]:
			# Initialise PyTorch model.
			# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
			# TapasConfig to False.

			# initialize configuration from json file
			lowerCAmelCase__      :      Optional[int]         =		TapasConfig.from_json_file(lowerCAmelCase_     )
			# set absolute/relative position embeddings parameter
			lowerCAmelCase__      :      Optional[Any]         =		reset_position_index_per_cell

			# set remaining parameters of TapasConfig as well as the model based on the task
			if task == "SQA":
						lowerCAmelCase__      :      Union[str, Any]         =		TapasForQuestionAnswering(config=lowerCAmelCase_     )
			elif task == "WTQ":
						# run_task_main.py hparams
						lowerCAmelCase__      :      List[Any]         =		4
						lowerCAmelCase__      :      Union[str, Any]         =		True
						# hparam_utils.py hparams
						lowerCAmelCase__      :      Any         =		0.664694
						lowerCAmelCase__      :      Tuple         =		0.207951
						lowerCAmelCase__      :      Dict         =		0.121194
						lowerCAmelCase__      :      List[str]         =		True
						lowerCAmelCase__      :      str         =		True
						lowerCAmelCase__      :      Dict         =		False
						lowerCAmelCase__      :      Tuple         =		0.0352513

						lowerCAmelCase__      :      List[Any]         =		TapasForQuestionAnswering(config=lowerCAmelCase_     )
			elif task == "WIKISQL_SUPERVISED":
						# run_task_main.py hparams
						lowerCAmelCase__      :      Optional[int]         =		4
						lowerCAmelCase__      :      int         =		False
						# hparam_utils.py hparams
						lowerCAmelCase__      :      Tuple         =		36.4519
						lowerCAmelCase__      :      str         =		0.903421
						lowerCAmelCase__      :      List[Any]         =		222.088
						lowerCAmelCase__      :      Union[str, Any]         =		True
						lowerCAmelCase__      :      Tuple         =		True
						lowerCAmelCase__      :      Union[str, Any]         =		True
						lowerCAmelCase__      :      Optional[Any]         =		0.763141

						lowerCAmelCase__      :      str         =		TapasForQuestionAnswering(config=lowerCAmelCase_     )
			elif task == "TABFACT":
						lowerCAmelCase__      :      List[Any]         =		TapasForSequenceClassification(config=lowerCAmelCase_     )
			elif task == "MLM":
						lowerCAmelCase__      :      Optional[int]         =		TapasForMaskedLM(config=lowerCAmelCase_     )
			elif task == "INTERMEDIATE_PRETRAINING":
						lowerCAmelCase__      :      Dict         =		TapasModel(config=lowerCAmelCase_     )
			else:
						raise ValueError(F'''Task {task} not supported.'''     )

			print(F'''Building PyTorch model from configuration: {config}'''     )
			# Load weights from tf checkpoint
			load_tf_weights_in_tapas(lowerCAmelCase_       ,							lowerCAmelCase_       ,							lowerCAmelCase_     )

			# Save pytorch-model (weights and configuration)
			print(F'''Save PyTorch model to {pytorch_dump_path}'''     )
			model.save_pretrained(lowerCAmelCase_     )

			# Save tokenizer files
			print(F'''Save tokenizer files to {pytorch_dump_path}'''     )
			lowerCAmelCase__      :      Any         =		TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'       ,							model_max_length=512     )
			tokenizer.save_pretrained(lowerCAmelCase_     )

			print('Used relative position embeddings:'       ,							model.config.reset_position_index_per_cell     )


if __name__ == "__main__":
					lowerCamelCase__          =		argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
					)
					parser.add_argument(
					    """--reset_position_index_per_cell""",
					    default=False,
					    action="""store_true""",
					    help="""Whether to use relative position embeddings or not. Defaults to True.""",
					)
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--tapas_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained TAPAS 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."""
					)
					lowerCamelCase__          =		parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.task,
					    args.reset_position_index_per_cell,
					    args.tf_checkpoint_path,
					    args.tapas_config_file,
					    args.pytorch_dump_path,
					)







import math

class 						lowerCAmelCase   :

					'''simple docstring'''

					def __init__(     self :		Tuple						,   __a :		int=0							)	->     Optional[Any]:  # a graph with Node 0,1,...,N-1


												"""simple docstring"""

												__lowercase					:							Any			=   n
												__lowercase					:							Optional[int]			=   [
												    [math.inf for j in range(0						,   __a							)] for i in range(0						,   __a							)
												]  # adjacency matrix for weight
												__lowercase					:							Dict			=   [
												    [math.inf for j in range(0						,   __a							)] for i in range(0						,   __a							)
												]  # dp[i][j] stores minimum distance from i to j



					def 					lowerCAmelCase (     self :		int						,   __a :		Optional[int]						,   __a :		Tuple						,   __a :		Dict							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							Any			=   w



					def 					lowerCAmelCase (     self :		List[str]							)	->     Tuple:


												"""simple docstring"""

												for k in range(0						,   self.n							):
																			for i in range(0						,   self.n							):
																										for j in range(0						,   self.n							):
																																	__lowercase					:							List[str]			=   min(self.dp[i][j]						,   self.dp[i][k] + self.dp[k][j]							)



					def 					lowerCAmelCase (     self :		Any						,   __a :		Any						,   __a :		str							)	->     int:


												"""simple docstring"""

												return self.dp[u][v]


if __name__ == "__main__":
	lowerCamelCase       :       int        =				Graph(5)
	graph.add_edge(0, 2, 9)
	graph.add_edge(0, 4, 10)
	graph.add_edge(1, 3, 5)
	graph.add_edge(2, 3, 7)
	graph.add_edge(3, 0, 10)
	graph.add_edge(3, 1, 2)
	graph.add_edge(3, 2, 1)
	graph.add_edge(3, 4, 6)
	graph.add_edge(4, 1, 3)
	graph.add_edge(4, 2, 4)
	graph.add_edge(4, 3, 9)
	graph.floyd_warshall()
	graph.show_min(1, 4)
	graph.show_min(0, 3)