






"""simple docstring"""






def 					_A			(__a							) ->       list[list]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Dict    						=		current_set.copy()
    for row_index, row in enumerate(__a							):
        SCREAMING_SNAKE_CASE_      :  int    						=		row[0]
        for column_index, column in enumerate(__a							):
            if magnitude == 0:
                SCREAMING_SNAKE_CASE_      :  str    						=		column
                continue
            SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		column / magnitude
    # Subtract to cancel term
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		current_set[0]
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		[first_row]
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		current_set[1::]
    for row in current_set:
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[]
        # If first term is 0, it is already in form we want, so we preserve it
        if row[0] == 0:
            final_set.append(__a							)
            continue
        for column_index in range(len(__a							)							):
            temp_row.append(first_row[column_index] - row[column_index]							)
        final_set.append(__a							)
    # Create next recursion iteration set
    if len(final_set[0]							) != 3:
        SCREAMING_SNAKE_CASE_      :  str    						=		final_set[0]
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		[]
        SCREAMING_SNAKE_CASE_      :  int    						=		[]
        for row in final_set[1::]:
            current_first_column.append(row[0]							)
            next_iteration.append(row[1::]							)
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		simplify(__a							)
        for i in range(len(__a							)							):
            resultant[i].insert(0    ,				current_first_column[i]							)
        resultant.insert(0    ,				__a							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		resultant
    return final_set



def 					_A			(__a							) ->       list:
    """simple docstring"""






    if len(__a							) == 0:
        raise IndexError('''solve_simultaneous() requires n lists of length n+1'''							)
    SCREAMING_SNAKE_CASE_      :  int    						=		len(__a							) + 1
    if any(len(__a							) != _length for item in equations							):
        raise IndexError('''solve_simultaneous() requires n lists of length n+1'''							)
    for row in equations:
        if any(not isinstance(__a    ,				(int, float)							) for column in row							):
            raise ValueError('''solve_simultaneous() requires lists of integers'''							)
    if len(__a							) == 1:
        return [equations[0][-1] / equations[0][0]]
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		equations.copy()
    if any(0 in row for row in data_set							):
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		data_set.copy()
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[]
        for row_index, row in enumerate(__a							):
            if 0 not in row:
                SCREAMING_SNAKE_CASE_      :  Any    						=		data_set.pop(__a							)
                break
        if not full_row:
            raise ValueError('''solve_simultaneous() requires at least 1 full equation'''							)
        data_set.insert(0    ,				__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		data_set.copy()
    SCREAMING_SNAKE_CASE_      :  int    						=		simplify(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		simplified[::-1]
    SCREAMING_SNAKE_CASE_      :  list    						=		[]
    for row in simplified:
        SCREAMING_SNAKE_CASE_      :  Dict    						=		row[-1]
        if not solutions:
            if row[-2] == 0:
                solutions.append(0							)
                continue
            solutions.append(current_solution / row[-2]							)
            continue
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		row.copy()[: len(__a							) - 1 :]
        while temp_row[0] == 0:
            temp_row.pop(0							)
        if len(__a							) == 0:
            solutions.append(0							)
            continue
        SCREAMING_SNAKE_CASE_      :  Any    						=		temp_row[1::]
        SCREAMING_SNAKE_CASE_      :  Dict    						=		temp_row[::-1]
        for column_index, column in enumerate(__a							):
            current_solution -= column * solutions[column_index]
        solutions.append(__a							)
    SCREAMING_SNAKE_CASE_      :  Any    						=		[]
    for item in solutions:
        final.append(float(round(__a    ,				5							)							)							)
    return final[::-1]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  UpperCAmelCase_   : List[Any]   			= [
      [2, 1, 1, 1, 1, 4],
      [1, 2, 1, 1, 1, 5],
      [1, 1, 2, 1, 1, 6],
      [1, 1, 1, 2, 1, 7],
      [1, 1, 1, 1, 2, 8],
  ]
  print(solve_simultaneous(eq))
  print(solve_simultaneous([[4, 2]]))










"""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)


