import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def   a__			(		__UpperCamelCase  ,   __UpperCamelCase  ,   __UpperCamelCase					):

      if openai_config_file == "":
            SCREAMING_SNAKE_CASE_    =	OpenAIGPTConfig()
      else:
            SCREAMING_SNAKE_CASE_    =	OpenAIGPTConfig.from_json_file(lowercase_					)
      SCREAMING_SNAKE_CASE_    =	OpenAIGPTModel(lowercase_					)

      # Load weights from numpy
      load_tf_weights_in_openai_gpt(lowercase_  ,   lowercase_  ,   lowercase_					)

      # Save pytorch-model
      SCREAMING_SNAKE_CASE_    =	pytorch_dump_folder_path + "/" + WEIGHTS_NAME
      SCREAMING_SNAKE_CASE_    =	pytorch_dump_folder_path + "/" + CONFIG_NAME
      print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''					)
      torch.save(model.state_dict()  ,   lowercase_					)
      print(F'''Save configuration file to {pytorch_config_dump_path}'''					)
      with open(lowercase_  ,   "w"  ,   encoding="utf-8"					) as f:
            f.write(config.to_json_string()					)


if __name__ == "__main__":
      A  :	Any         =							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--openai_checkpoint_folder_path",
          default=None,
          type=str,
          required=True,
          help="Path to the TensorFlow checkpoint path.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--openai_config_file",
          default="",
          type=str,
          help=(
              "An optional config json file corresponding to the pre-trained OpenAI model. \n"
              "This specifies the model architecture."
          ),
      )
      A  :	Optional[Any]         =							parser.parse_args()
      convert_openai_checkpoint_to_pytorch(
          args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
      )








from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_lowerCamelCase      :						str							=  299792458

# Symbols
_lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase      :						int							=  symbols("""ct x y z""")







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			float:



       """simple docstring"""

       if velocity > c:
              raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!'''		)
       elif velocity < 1:
              # Usually the speed should be much higher than 1 (c order of magnitude)
              raise ValueError('''Speed must be greater than or equal to 1!'''		)

       return velocity / c







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			float:



       """simple docstring"""

       return 1 / sqrt(1 - beta(lowercase_		) ** 2		)







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			np.ndarray:



       """simple docstring"""

       return np.array(
           [
               [gamma(lowercase_		), -gamma(lowercase_		) * beta(lowercase_		), 0, 0],
               [-gamma(lowercase_		) * beta(lowercase_		), gamma(lowercase_		), 0, 0],
               [0, 0, 1, 0],
               [0, 0, 0, 1],
           ]		)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_ = None		)  ->			np.ndarray:



       """simple docstring"""

       if event is None:
              A__ 					=       np.array([ct, x, y, z]		)  # Symbolic four vector
       else:
              event[0] *= c  # x0 is ct (speed of light * time)

       return transformation_matrix(lowercase_		) @ event


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # Example of symbolic vector:
  _lowerCamelCase      :						Tuple							=  transform(29979245)
  print("""Example of four vector: """)
  print(F'''ct\' = {four_vector[0]}''')
  print(F'''x\' = {four_vector[1]}''')
  print(F'''y\' = {four_vector[2]}''')
  print(F'''z\' = {four_vector[3]}''')

  # Substitute symbols with numerical values
  _lowerCamelCase      :						int							=  {ct: c, x: 1, y: 1, z: 1}
  _lowerCamelCase      :						Any							=  [four_vector[i].subs(sub_dict) for i in range(4)]

  print(F'''\n{numerical_vector}''')


