


from __future__ import annotations

from collections.abc import Callable
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Callable[[int | float], int | float]      ,						lowercase__  :int | float      ,						lowercase__  :int | float      ,						lowercase__  :int = 1_0_0      ,						)       ->  float:
						__magic_name__				:							Tuple				=      x_start
						__magic_name__				:							int				=      fnc(lowercase__				)
						__magic_name__				:							Union[str, Any]				=      0.0

						for _ in range(lowercase__				):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												__magic_name__				:							List[Any]				=      (x_end - x_start) / steps + xa
												__magic_name__				:							Dict				=      fnc(lowercase__				)
												area += abs(fxa + fxa				) * (xa - xa) / 2

												# Increment step
												__magic_name__				:							Any				=      xa
												__magic_name__				:							Union[str, Any]				=      fxa
						return area


if __name__ == "__main__":
			def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]				)       ->  Tuple:
									return x**3

			print('f(x) = x^3')
			print('The area between the curve, x = -10, x = 10 and the x axis is:')
			snake_case__     							= 10
			while i <= 10_0000:
						snake_case__     							= trapezoidal_area(f, -5, 5, i)
						print(F"""with {i} steps: {area}""")
						i *= 10






import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :List[Any]      ,						lowercase__  :str      ,						lowercase__  :List[str]      ,						lowercase__  :List[Any]				)       ->  List[Any]:
						# Initialise PyTorch model
						__magic_name__				:							Any				=      BertConfig.from_pretrained(
						    'bert-large-cased'      ,						vocab_size=lowercase__      ,						max_position_embeddings=5_1_2      ,						is_decoder=lowercase__      ,						add_cross_attention=lowercase__      ,						)
						__magic_name__				:							Optional[int]				=      bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						__magic_name__				:							Optional[int]				=      BertGenerationConfig(**lowercase__				)
						if is_encoder:
												__magic_name__				:							Dict				=      BertGenerationEncoder(lowercase__				)
						else:
												__magic_name__				:							str				=      BertGenerationDecoder(lowercase__				)
						print(f"Building PyTorch model from configuration: {config}"				)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    lowercase__      ,						lowercase__      ,						model_class='bert'      ,						is_encoder_named_decoder=lowercase__      ,						is_encoder=lowercase__      ,						)

						# Save pytorch-model
						print(f"Save PyTorch model and config to {pytorch_dump_path}"				)
						model.save_pretrained(lowercase__				)


if __name__ == "__main__":
			snake_case__     							= argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
			)
			parser.add_argument(
			    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			parser.add_argument(
			    '--is_encoder_named_decoder',
			    action='store_true',
			    help='If decoder has to be renamed to encoder in PyTorch model.',
			)
			parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
			parser.add_argument('--vocab_size', default=5_0358, type=int, help='Vocab size of model')
			snake_case__     							= parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.tf_hub_path,
			    args.pytorch_dump_path,
			    args.is_encoder_named_decoder,
			    args.vocab_size,
			    is_encoder=args.is_encoder,
			)


