


"""simple docstring"""






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
_lowerCamelCase     =  data_utils.TransfoXLTokenizer
_lowerCamelCase     =  data_utils.TransfoXLCorpus
_lowerCamelCase     =  data_utils
_lowerCamelCase     =  data_utils


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

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

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

								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__     							=				TransfoXLConfig()
								else:
												SCREAMING_SNAKE_CASE__     							=				TransfoXLConfig.from_json_file(lowerCamelCase_  )
								print(F'''Building PyTorch model from configuration: {config}'''  )
								SCREAMING_SNAKE_CASE__     							=				TransfoXLLMHeadModel(lowerCamelCase_  )

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


if __name__ == "__main__":
	_lowerCamelCase     =  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.',
	)
	_lowerCamelCase     =  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,
	)




"""simple docstring"""






def __lowercase     (      lowerCamelCase_  :      Tuple  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				{
				    "^": 3,
				    "*": 2,
				    "/": 2,
				    "%": 2,
				    "+": 1,
				    "-": 1,
				}  # Priority of each operator
				SCREAMING_SNAKE_CASE__     							=				len(lowerCamelCase_  ) if (len(lowerCamelCase_  ) > 7) else 7

				# Print table header for output
				print(
				    "Symbol".center(8  )						,					"Stack".center(lowerCamelCase_  )						,					"Postfix".center(lowerCamelCase_  )						,					sep=" | "						,					)
				print("-" * (print_width * 3 + 7)  )

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(lowerCamelCase_  )  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(lowerCamelCase_  )  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop()  )  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(lowerCamelCase_  ) == 0:
																stack.append(lowerCamelCase_  )  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(lowerCamelCase_  ) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop()  )  # pop stack & add to Postfix
																stack.append(lowerCamelCase_  )  # push x to stack

								print(
								    x.center(8  )						,					("".join(lowerCamelCase_  )).ljust(lowerCamelCase_  )						,					("".join(lowerCamelCase_  )).ljust(lowerCamelCase_  )						,					sep=" | "						,					)  # Output in tabular format

				while len(lowerCamelCase_  ) > 0:  # while stack is not empty
								post_fix.append(stack.pop()  )  # pop stack & add to Postfix
								print(
								    " ".center(8  )						,					("".join(lowerCamelCase_  )).ljust(lowerCamelCase_  )						,					("".join(lowerCamelCase_  )).ljust(lowerCamelCase_  )						,					sep=" | "						,					)  # Output in tabular format

				return "".join(lowerCamelCase_  )  # return Postfix as str


def __lowercase     (      lowerCamelCase_  :      str  ):
				SCREAMING_SNAKE_CASE__     							=				list(infix[::-1]  )  # reverse the infix equation

				for i in range(len(lowerCamelCase_  )  ):
								if infix[i] == "(":
												SCREAMING_SNAKE_CASE__     							=				")"  # change "(" to ")"
								elif infix[i] == ")":
												SCREAMING_SNAKE_CASE__     							=				"("  # change ")" to "("

				return (infix_2_postfix("".join(lowerCamelCase_  )  ))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
	_lowerCamelCase     =  input('\nEnter an Infix Equation = ')  # Input an Infix equation
	_lowerCamelCase     =  ''.join(Infix.split())  # Remove spaces from the input
	print('\n\t', Infix, '(Infix) -> ', infix_2_prefix(Infix), '(Prefix)')
