





"""simple docstring"""


import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						# Initialise PyTorch model
						UpperCamelCase				:		Dict				   =			FunnelConfig.from_json_file(SCREAMING_SNAKE_CASE     )
						print(f"""Building PyTorch model from configuration: {config}"""     )
						UpperCamelCase				:		Optional[int]				   =			FunnelBaseModel(SCREAMING_SNAKE_CASE     ) if base_model else FunnelModel(SCREAMING_SNAKE_CASE     )

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

						# Save pytorch-model
						print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
						torch.save(model.state_dict()							,     SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
		__magic_name__       :					Union[str, Any]						=			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 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."""
		)
		parser.add_argument(
		    """--base_model""", action="""store_true""", help="""Whether you want just the base model (no decoder) or not."""
		)
		__magic_name__       :					List[Any]						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
		)










"""simple docstring"""


import math

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						return math.pow(SCREAMING_SNAKE_CASE							,     2     ) - a

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return 2 * x

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Union[str, Any]				   =			2.0

						while start <= a:
												UpperCamelCase				:		Optional[Any]				   =			math.pow(SCREAMING_SNAKE_CASE							,     2     )

						return start

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE = 9999							,     SCREAMING_SNAKE_CASE = 0.00_00_00_00_00_00_01     ):

						if a < 0:
												raise ValueError("""math domain error"""     )

						UpperCamelCase				:		Optional[Any]				   =			get_initial_point(SCREAMING_SNAKE_CASE     )

						for _ in range(SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		List[str]				   =			value
												UpperCamelCase				:		Tuple				   =			value - fx(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) / fx_derivative(SCREAMING_SNAKE_CASE     )
												if abs(prev_value - value     ) < tolerance:
																		return value

						return value


if __name__ == "__main__":
		from doctest import testmod

		testmod()



