




'''simple docstring'''

import argparse

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


logging.set_verbosity_info()


def __UpperCAmelCase	(   _UpperCAmelCase		:   Tuple		,				_UpperCAmelCase		:   Optional[Any]		,				_UpperCAmelCase		:   Any						)      ->      Any:
			# Initialise PyTorch model
			__snake_case			=		TaConfig.from_json_file(_UpperCAmelCase						)
			print(F'''Building PyTorch model from configuration: {config}'''						)
			__snake_case			=		TaForConditionalGeneration(_UpperCAmelCase						)

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

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


if __name__ == "__main__":
						a       :						List[str]								=		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       :						Any								=		parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)












def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       # "extended trapezoidal rule"
       # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
       _lowerCamelCase				:  int						=		(boundary[1] - boundary[0]) / steps
       _lowerCamelCase				:  Any						=		boundary[0]
       _lowerCamelCase				:  Tuple						=		boundary[1]
       _lowerCamelCase				:  Optional[Any]						=		make_points(lowercase_			,     lowercase_			,     lowercase_   )
       _lowerCamelCase				:  Dict						=		0.0
       y += (h / 2.0) * f(lowercase_   )
       for i in x_i:
              # print(i)
              y += h * f(lowercase_   )
       y += (h / 2.0) * f(lowercase_   )
       return y




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  Dict						=		a + h
       while x < (b - h):
              yield x
              _lowerCamelCase				:  Dict						=		x + h




def  __UpperCAmelCase(   lowercase_   ):  # enter your function here
       _lowerCamelCase				:  List[str]						=		(x - 0) * (x - 0)
       return y




def  __UpperCAmelCase(   ):
       _lowerCamelCase				:  List[Any]						=		0.0  # Lower bound of integration
       _lowerCamelCase				:  Union[str, Any]						=		1.0  # Upper bound of integration
       _lowerCamelCase				:  Dict						=		1_0.0  # define number of steps or resolution
       _lowerCamelCase				:  Optional[int]						=		[a, b]  # define boundary of integration
       _lowerCamelCase				:  str						=		method_a(lowercase_			,     lowercase_   )
       print(F"""y = {y}"""   )


if __name__ == "__main__":
       main()

