





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_									=       data_utils.TransfoXLTokenizer
UpperCAmelCase_									=       data_utils.TransfoXLCorpus
UpperCAmelCase_									=       data_utils
UpperCAmelCase_									=       data_utils





def   lowerCamelCase__       (       UpperCamelCase__     :    Optional[int]    ,      UpperCamelCase__     :    Any    ,      UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    str  )     ->	Any:



					'''simple docstring'''




					if transfo_xl_dataset_file:
										# Convert a pre-processed corpus (see original TensorFlow repo)
										with open(UpperCamelCase__    ,      'rb'  ) as fp:
															_snake_case													=			pickle.load(UpperCamelCase__    ,      encoding='latin1'  )
										# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
										_snake_case													=			pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
										print(F'''Save vocabulary to {pytorch_vocab_dump_path}'''  )
										_snake_case													=			corpus.vocab.__dict__
										torch.save(UpperCamelCase__    ,      UpperCamelCase__  )

										_snake_case													=			corpus.__dict__
										corpus_dict_no_vocab.pop('vocab'    ,      UpperCamelCase__  )
										_snake_case													=			pytorch_dump_folder_path + '/' + CORPUS_NAME
										print(F'''Save dataset to {pytorch_dataset_dump_path}'''  )
										torch.save(UpperCamelCase__    ,      UpperCamelCase__  )

					if tf_checkpoint_path:
										# Convert a pre-trained TensorFlow model
										_snake_case													=			os.path.abspath(UpperCamelCase__  )
										_snake_case													=			os.path.abspath(UpperCamelCase__  )

										print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''  )
										# Initialise PyTorch model
										if transfo_xl_config_file == "":
															_snake_case													=			TransfoXLConfig()
										else:
															_snake_case													=			TransfoXLConfig.from_json_file(UpperCamelCase__  )
										print(F'''Building PyTorch model from configuration: {config}'''  )
										_snake_case													=			TransfoXLLMHeadModel(UpperCamelCase__  )

										_snake_case													=			load_tf_weights_in_transfo_xl(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__":
					UpperCAmelCase_									=       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_									=       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,
					)










from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst





def   lowerCamelCase__       (       )     ->	List[str]:



					'''simple docstring'''




					_snake_case							,       _snake_case													=			9, 14  # noqa: F841
					_snake_case													=			[
					    [0, 1, 4],
					    [0, 7, 8],
					    [1, 2, 8],
					    [7, 8, 7],
					    [7, 6, 1],
					    [2, 8, 2],
					    [8, 6, 6],
					    [2, 3, 7],
					    [2, 5, 4],
					    [6, 5, 2],
					    [3, 5, 14],
					    [3, 4, 9],
					    [5, 4, 10],
					    [1, 7, 11],
					]

					_snake_case													=			defaultdict(UpperCamelCase__  )
					for nodea, nodea, cost in edges:
										adjancency[nodea].append([nodea, cost]  )
										adjancency[nodea].append([nodea, cost]  )

					_snake_case													=			mst(UpperCamelCase__  )

					_snake_case													=			[
					    [7, 6, 1],
					    [2, 8, 2],
					    [6, 5, 2],
					    [0, 1, 4],
					    [2, 5, 4],
					    [2, 3, 7],
					    [0, 7, 8],
					    [3, 4, 9],
					]

					for answer in expected:
										_snake_case													=			tuple(answer[:2]  )
										_snake_case													=			tuple(edge[::-1]  )
										assert edge in result or reverse in result



