



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
__SCREAMING_SNAKE_CASE           =  data_utils.TransfoXLTokenizer
__SCREAMING_SNAKE_CASE           =  data_utils.TransfoXLCorpus
__SCREAMING_SNAKE_CASE           =  data_utils
__SCREAMING_SNAKE_CASE           =  data_utils




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

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

						A							:					str              =						load_tf_weights_in_transfo_xl(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )
						# Save pytorch-model
						A							:					Optional[int]              =						os.path.join(_lowerCamelCase ,    _lowerCamelCase    )
						A							:					Tuple              =						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__":
							__SCREAMING_SNAKE_CASE           =  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.""",
							)
							__SCREAMING_SNAKE_CASE           =  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 __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__SCREAMING_SNAKE_CASE           =  1.0_5_4_5_7_1_8_1_7e-3_4  # unit of ℏ : J * s

__SCREAMING_SNAKE_CASE           =  3e8  # unit of c : m * s^-1




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):

			if (force, area, distance).count(0    ) != 1:
						raise ValueError("One and only one argument must be 0"    )
			if force < 0:
						raise ValueError("Magnitude of force can not be negative"    )
			if distance < 0:
						raise ValueError("Distance can not be negative"    )
			if area < 0:
						raise ValueError("Area can not be negative"    )
			if force == 0:
						A							:					int              =						(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
						    240 * (distance) ** 4
						)
						return {"force": force}
			elif area == 0:
						A							:					Tuple              =						(240 * force * (distance) ** 4) / (
						    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
						)
						return {"area": area}
			elif distance == 0:
						A							:					Dict              =						(
						    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
						) ** (1 / 4)
						return {"distance": distance}
			raise ValueError("One and only one argument must be 0"    )


# Run doctest
if __name__ == "__main__":
							import doctest

							doctest.testmod()