"""simple docstring"""





from __future__ import annotations


def    lowercase_   (   _lowerCamelCase:	int | float | str						,   _lowerCamelCase:	int | float | str      ) ->      list[str]:



							'''simple docstring'''

							if nth_term == "":
														return [""]
							__lowerCamelCase					:		str		   =     int(_lowerCamelCase      )
							__lowerCamelCase					:		int		   =     int(_lowerCamelCase      )
							__lowerCamelCase					:		list[str]		   =     []
							for temp in range(int(_lowerCamelCase      )      ):
														series.append(F"""1 / {pow(temp + 1						,   int(_lowerCamelCase      )      )}""" if series else "1"      )
							return series


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__A  =				int(input('''Enter the last number (nth term) of the P-Series'''))
				__A  =				int(input('''Enter the power for  P-Series'''))
				print('''Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p''')
				print(p_series(nth_term, power))
"""simple docstring"""





import argparse

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


logging.set_verbosity_info()


def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	str						,   _lowerCamelCase:	Dict      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     TaConfig.from_json_file(_lowerCamelCase      )
							print(F"""Building PyTorch model from configuration: {config}"""      )
							__lowerCamelCase					:		Optional[int]		   =     TaForConditionalGeneration(_lowerCamelCase      )

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

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


if __name__ == "__main__":
				__A  =				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.'''
				)
				__A  =				parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)