

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
a_						     =    data_utils.TransfoXLTokenizer
a_						     =    data_utils.TransfoXLCorpus
a_						     =    data_utils
a_						     =    data_utils

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

		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       corpus.__dict__
		corpus_dict_no_vocab.pop("vocab"							,			UpperCamelCase__)
		SCREAMING_SNAKE_CASE   :      Tuple				     =       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
		SCREAMING_SNAKE_CASE   :      str				     =       os.path.abspath(UpperCamelCase__)
		SCREAMING_SNAKE_CASE   :      List[str]				     =       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 == "":
			SCREAMING_SNAKE_CASE   :      str				     =       TransfoXLConfig()
		else:
			SCREAMING_SNAKE_CASE   :      Any				     =       TransfoXLConfig.from_json_file(UpperCamelCase__)
		print(f"Building PyTorch model from configuration: {config}")
		SCREAMING_SNAKE_CASE   :      int				     =       TransfoXLLMHeadModel(UpperCamelCase__)

		SCREAMING_SNAKE_CASE   :      Dict				     =       load_tf_weights_in_transfo_xl(UpperCamelCase__							,			UpperCamelCase__							,			UpperCamelCase__)
		# Save pytorch-model
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       os.path.join(UpperCamelCase__							,			UpperCamelCase__)
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       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_						     =    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.',
			)
			a_						     =    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 numpy as np






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Dict	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       (0, 0)
				SCREAMING_SNAKE_CASE   :      Any				     =       None
				SCREAMING_SNAKE_CASE   :      Any				     =       0
				SCREAMING_SNAKE_CASE   :      List[str]				     =       0
				SCREAMING_SNAKE_CASE   :      Any				     =       0
			def __eq__(			self							: List[str]      ,      a							: Any	)							->       Tuple:

				"""simple docstring"""

				return self.position == cell.position






			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       str:

				"""simple docstring"""

				print(self.position	)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: List[Any]      ,      a							: List[Any]=(5, 5)	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.zeros(a	)
				SCREAMING_SNAKE_CASE   :      str				     =       world_size[0]
				SCREAMING_SNAKE_CASE   :      int				     =       world_size[1]
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[int]:

				"""simple docstring"""

				print(self.w	)






			def 							__UpperCamelCase (			self							: Dict      ,      a							: Dict	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [
				    (-1, -1),
				    (-1, 0),
				    (-1, 1),
				    (0, -1),
				    (0, 1),
				    (1, -1),
				    (1, 0),
				    (1, 1),
				]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       cell.position[0]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       cell.position[1]
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
				for n in neughbour_cord:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       current_x + n[0]
					SCREAMING_SNAKE_CASE   :      int				     =       current_y + n[1]
					if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
						SCREAMING_SNAKE_CASE   :      int				     =       Cell()
						SCREAMING_SNAKE_CASE   :      str				     =       (x, y)
						SCREAMING_SNAKE_CASE   :      Any				     =       cell
						neighbours.append(a	)
				return neighbours

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	_open.append(_a)

	while _open:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.argmin([n.f for n in _open])
		SCREAMING_SNAKE_CASE   :      List[str]				     =       _open[min_f]
		_closed.append(_open.pop(_a))
		if current == goal:
			break
		for n in world.get_neigbours(_a):
			for c in _closed:
				if c == n:
					continue
			SCREAMING_SNAKE_CASE   :      Tuple				     =       current.g + 1
			SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       n.position
			SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      str				     =       goal.position
			SCREAMING_SNAKE_CASE   :      Any				     =       (ya - ya) ** 2 + (xa - xa) ** 2
			SCREAMING_SNAKE_CASE   :      List[str]				     =       n.h + n.g

			for c in _open:
				if c == n and c.f < n.f:
					continue
			_open.append(_a)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	while current.parent is not None:
		path.append(current.position)
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       current.parent
	path.append(current.position)
	return path[::-1]


if __name__ == "__main__":
			a_						     =    Gridworld()
			# Start position and goal
			a_						     =    Cell()
			a_						     =    (0, 0)
			a_						     =    Cell()
			a_						     =    (4, 4)
			print(F'''path from {start.position} to {goal.position}''')
			a_						     =    astar(world, start, goal)
			#   Just for visual reasons.
			for i in s:
						a_						     =    1
			print(world.w)