


'''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
__A	=data_utils.TransfoXLTokenizer
__A	=data_utils.TransfoXLCorpus
__A	=data_utils
__A	=data_utils







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

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

      UpperCAmelCase__  :					Optional[int]        =     load_tf_weights_in_transfo_xl(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
      # Save pytorch-model
      UpperCAmelCase__  :					Tuple        =     os.path.join(UpperCamelCase__ ,      UpperCamelCase__					)
      UpperCAmelCase__  :					Tuple        =     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,
  )



'''simple docstring'''



from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean







def     _UpperCamelCase      (		UpperCamelCase__					):

			# A local function to see if a dot lands in the circle.
			def is_in_circle(UpperCamelCase__ ,      UpperCamelCase__					) -> bool:
						UpperCAmelCase__  :					List[str]        =     sqrt((x**2) + (y**2)					)
						# Our circle has a radius of 1, so a distance
						# greater than 1 would land outside the circle.
						return distance_from_centre <= 1

			# The proportion of guesses that landed in the circle
			UpperCAmelCase__  :					Optional[Any]        =     mean(
			    int(is_in_circle(uniform(-1.0 ,      1.0					) ,      uniform(-1.0 ,      1.0					)					)					)
			    for _ in range(UpperCamelCase__					)					)
			# The ratio of the area for circle to square is pi/4.
			UpperCAmelCase__  :					int        =     proportion * 4
			print(f'''The estimated value of pi is {pi_estimate}'''					)
			print(f'''The numpy value of pi is {pi}'''					)
			print(f'''The total error is {abs(pi - pi_estimate					)}'''					)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ = 0.0 ,      UpperCamelCase__ = 1.0 ,      ):
			return mean(
			    function_to_integrate(uniform(UpperCamelCase__ ,      UpperCamelCase__					)					) for _ in range(UpperCamelCase__					)					) * (max_value - min_value)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ = 0.0 ,      UpperCamelCase__ = 1.0					):

			def identity_function(UpperCamelCase__					) -> float:
						return x

			UpperCAmelCase__  :					List[str]        =     area_under_curve_estimator(
			    UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
			UpperCAmelCase__  :					Optional[int]        =     (max_value * max_value - min_value * min_value) / 2

			print("""******************"""					)
			print(f'''Estimating area under y=x where x varies from {min_value} to {max_value}'''					)
			print(f'''Estimated value is {estimated_value}'''					)
			print(f'''Expected value is {expected_value}'''					)
			print(f'''Total error is {abs(estimated_value - expected_value					)}'''					)
			print("""******************"""					)







def     _UpperCamelCase      (		UpperCamelCase__					):

			def function_to_integrate(UpperCamelCase__					) -> float:
						return sqrt(4.0 - x * x					)

			UpperCAmelCase__  :					Dict        =     area_under_curve_estimator(
			    UpperCamelCase__ ,      UpperCamelCase__ ,      0.0 ,      2.0					)

			print("""******************"""					)
			print("""Estimating pi using area_under_curve_estimator"""					)
			print(f'''Estimated value is {estimated_value}'''					)
			print(f'''Expected value is {pi}'''					)
			print(f'''Total error is {abs(estimated_value - pi					)}'''					)
			print("""******************"""					)


if __name__ == "__main__":
		import doctest

		doctest.testmod()