

import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
UpperCAmelCase_							:					Union[str, Any]		 =   data_utils.TransfoXLTokenizer
UpperCAmelCase_							:					Optional[Any]		 =   data_utils.TransfoXLCorpus
UpperCAmelCase_							:					Union[str, Any]		 =   data_utils
UpperCAmelCase_							:					int		 =   data_utils
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   List[str]			,a__    :   List[Any]			,a__    :   Optional[int]       )					->				str:
	if transfo_xl_dataset_file:
		# Convert a pre-processed corpus (see original TensorFlow repo)
		with open(a__			,"""rb"""       ) as fp:
			__A :			str		    =			pickle.load(a__			,encoding="""latin1"""       )
		# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
		__A :			Optional[int]		    =			pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""pretrained_vocab_file"""]
		print(f"""Save vocabulary to {pytorch_vocab_dump_path}"""       )
		__A :			List[Any]		    =			corpus.vocab.__dict__
		torch.save(a__			,a__       )

		__A :			List[Any]		    =			corpus.__dict__
		corpus_dict_no_vocab.pop("""vocab"""			,a__       )
		__A :			Optional[Any]		    =			pytorch_dump_folder_path + """/""" + CORPUS_NAME
		print(f"""Save dataset to {pytorch_dataset_dump_path}"""       )
		torch.save(a__			,a__       )

	if tf_checkpoint_path:
		# Convert a pre-trained TensorFlow model
		__A :			Optional[Any]		    =			os.path.abspath(a__       )
		__A :			List[str]		    =			os.path.abspath(a__       )

		print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""       )
		# Initialise PyTorch model
		if transfo_xl_config_file == "":
			__A :			str		    =			TransfoXLConfig()
		else:
			__A :			str		    =			TransfoXLConfig.from_json_file(a__       )
		print(f"""Building PyTorch model from configuration: {config}"""       )
		__A :			Union[str, Any]		    =			TransfoXLLMHeadModel(a__       )

		__A :			List[Any]		    =			load_tf_weights_in_transfo_xl(a__			,a__			,a__       )
		# Save pytorch-model
		__A :			int		    =			os.path.join(a__			,a__       )
		__A :			Tuple		    =			os.path.join(a__			,a__       )
		print(f"""Save PyTorch model to {os.path.abspath(a__       )}"""       )
		torch.save(model.state_dict()			,a__       )
		print(f"""Save configuration file to {os.path.abspath(a__       )}"""       )
		with open(a__			,"""w"""			,encoding="""utf-8"""       ) as f:
			f.write(config.to_json_string()       )


if __name__ == "__main__":
			UpperCAmelCase_							:					Optional[int]		 =   argparse.ArgumentParser()
			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(
			    '''--tf_checkpoint_path''',
			    default='''''',
			    type=str,
			    help='''An optional path to a TensorFlow checkpoint path to be converted.''',
			)
			parser.add_argument(
			    '''--transfo_xl_config_file''',
			    default='''''',
			    type=str,
			    help=(
			        '''An optional config json file corresponding to the pre-trained BERT model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--transfo_xl_dataset_file''',
			    default='''''',
			    type=str,
			    help='''An optional dataset file to be converted in a vocabulary.''',
			)
			UpperCAmelCase_							:					str		 =   parser.parse_args()
			convert_transfo_xl_checkpoint_to_pytorch(
			    args.tf_checkpoint_path,
			    args.transfo_xl_config_file,
			    args.pytorch_dump_folder_path,
			    args.transfo_xl_dataset_file,
			)








import math






class      lowerCamelCase_      :




	def __init__(     self						:     Union[str, Any]					,      __A						:     List[str]=0							):  # a graph with Node 0,1,...,N-1
		__A :			List[str]		    =			n
		__A :			List[str]		    =			[
		    [math.inf for j in range(0					,      __A							)] for i in range(0					,      __A							)
		]  # adjacency matrix for weight
		__A :			str		    =			[
		    [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						:     str					,      __A						:     Union[str, Any]					,      __A						:     Any					,      __A						:     Optional[int]							):
		__A :			List[Any]		    =			w




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		for k in range(0					,      self.n							):
			for i in range(0					,      self.n							):
				for j in range(0					,      self.n							):
					__A :			List[Any]		    =			min(self.dp[i][j]					,      self.dp[i][k] + self.dp[k][j]							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     List[str]					,      __A						:     List[str]							):
		return self.dp[u][v]


if __name__ == "__main__":
			UpperCAmelCase_							:					Tuple		 =   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)





