






"""simple docstring"""






from __future__ import annotations

from collections.abc import MutableSequence




class 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  List[str]						,	lowercase_     :  int						,	lowercase_     :  MutableSequence[float]):




          '''simple docstring'''


          if len(lowercase_) != degree + 1:
              raise ValueError(
                  '''The number of coefficients should be equal to the degree + 1.''')

          SCREAMING_SNAKE_CASE_      :  list[float]    						=		list(lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		degree
      def __add__(						self     :  int						,	lowercase_     :  Polynomial):




          '''simple docstring'''


          if self.degree > polynomial_a.degree:
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.coefficients[:]
              for i in range(polynomial_a.degree + 1):
                  coefficients[i] += polynomial_a.coefficients[i]
              return Polynomial(self.degree						,	lowercase_)
          else:
              SCREAMING_SNAKE_CASE_      :  int    						=		polynomial_a.coefficients[:]
              for i in range(self.degree + 1):
                  coefficients[i] += self.coefficients[i]
              return Polynomial(polynomial_a.degree						,	lowercase_)
      def __sub__(						self     :  str						,	lowercase_     :  Polynomial):




          '''simple docstring'''


          return self + polynomial_a * Polynomial(0						,	[-1])
      def __neg__(						self     :  str):




          '''simple docstring'''


          return Polynomial(self.degree						,	[-c for c in self.coefficients])
      def __mul__(						self     :  List[Any]						,	lowercase_     :  Polynomial):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  list[float]    						=		[0] * (self.degree + polynomial_a.degree + 1)
          for i in range(self.degree + 1):
              for j in range(polynomial_a.degree + 1):
                  coefficients[i + j] += (
                      self.coefficients[i] * polynomial_a.coefficients[j]
                  )

          return Polynomial(self.degree + polynomial_a.degree						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple						,	lowercase_     :  int | float):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int | float    						=		0
          for i in range(self.degree + 1):
              result += self.coefficients[i] * (substitution**i)
          return result
      def __str__(						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		''''''
          for i in range(self.degree						,	-1						,	-1):
              if self.coefficients[i] == 0:
                  continue
              elif self.coefficients[i] > 0:
                  if polynomial:
                      polynomial += " + "
              else:
                  polynomial += " - "

              if i == 0:
                  polynomial += str(abs(self.coefficients[i]))
              elif i == 1:
                  polynomial += str(abs(self.coefficients[i])) + "x"
              else:
                  polynomial += str(abs(self.coefficients[i])) + "x^" + str(lowercase_)

          return polynomial
      def __repr__(						self     :  Tuple):




          '''simple docstring'''


          return self.__str__()
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  list[float]    						=		[0] * self.degree
          for i in range(self.degree):
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.coefficients[i + 1] * (i + 1)
          return Polynomial(self.degree - 1						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  int | float = 0):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  list[float]    						=		[0] * (self.degree + 2)
          SCREAMING_SNAKE_CASE_      :  Any    						=		constant
          for i in range(self.degree + 1):
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.coefficients[i] / (i + 1)
          return Polynomial(self.degree + 1						,	lowercase_)
      def __eq__(						self     :  str						,	lowercase_     :  object):




          '''simple docstring'''


          if not isinstance(lowercase_						,	lowercase_):
              return False

          if self.degree != polynomial_a.degree:
              return False

          for i in range(self.degree + 1):
              if self.coefficients[i] != polynomial_a.coefficients[i]:
                  return False

          return True



      def __ne__(						self     :  Union[str, Any]						,	lowercase_     :  object):




          '''simple docstring'''


          return not self.__eq__(lowercase_)










"""simple docstring"""






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 					_A			(__a    ,				__a    ,				__a							) ->       Dict:
    """simple docstring"""






    if gpta_config_file == "":
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		GPTaConfig()
    else:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		GPTaConfig.from_json_file(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		GPTaModel(__a							)

    # Load weights from numpy
    load_tf_weights_in_gpta(__a    ,				__a    ,				__a							)

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


if __name__ == "__main__":
  UpperCAmelCase_   : str   			= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--gpt2_checkpoint_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(
      """--gpt2_config_file""",
      default="""""",
      type=str,
      help=(
          """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
          """This specifies the model architecture."""
      ),
  )
  UpperCAmelCase_   : Union[str, Any]   			= parser.parse_args()
  convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


