





"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[Any]    ,						SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							__lowerCamelCase      :      str         =TaConfig.from_json_file(SCREAMING_SNAKE_CASE   )
							print(F'Building PyTorch model from configuration: {config}'   )
							__lowerCamelCase      :      Any         =TaForConditionalGeneration(SCREAMING_SNAKE_CASE   )

							# Load weights from tf checkpoint
							load_tf_weights_in_ta(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}'   )
							model.save_pretrained(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
							_UpperCamelCase							=      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
							    ),
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							_UpperCamelCase							=      parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)











"""simple docstring"""





def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							list    ,						SCREAMING_SNAKE_CASE       :							list    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[Any]         =len(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Union[str, Any]         =[[0] * n for i in range(SCREAMING_SNAKE_CASE   )]
							for i in range(SCREAMING_SNAKE_CASE   ):
														__lowerCamelCase      :      Union[str, Any]         =y_points[i]

							for i in range(2    ,						SCREAMING_SNAKE_CASE   ):
														for j in range(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      List[Any]         =(
																					    (xa - x_points[j - i + 1]) * q[j][i - 1]
																					    - (xa - x_points[j]) * q[j - 1][i - 1]
																					) / (x_points[j] - x_points[j - i + 1])

							return [q[n - 1][n - 1], q]


if __name__ == "__main__":
							import doctest

							doctest.testmod()




