"""simple docstring"""




import argparse

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


logging.set_verbosity_info()






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				BertConfig.from_pretrained(
							    'bert-large-cased'							,     vocab_size=a__							,     max_position_embeddings=512							,     is_decoder=a__							,     add_cross_attention=a__							,     )
							A__						  =				bert_config.to_dict()
							del bert_config_dict["type_vocab_size"]
							A__						  =				BertGenerationConfig(**a__					)
							if is_encoder:
														A__						  =				BertGenerationEncoder(a__					)
							else:
														A__						  =				BertGenerationDecoder(a__					)
							print(F'''Building PyTorch model from configuration: {config}'''					)

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

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


if __name__ == "__main__":
				__lowerCamelCase									=	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_03_58, type=int, help="Vocab size of model")
				__lowerCamelCase									=	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,
				)











'''simple docstring'''
def 							snake_case_    (  a__						:      List[str]       ,a__						:      Optional[int]     ):



    """simple docstring"""




    __lowercase     							=		(boundary[1] - boundary[0]) / steps
    __lowercase     							=		boundary[0]
    __lowercase     							=		boundary[1]
    __lowercase     							=		make_points(a__       ,a__       ,a__     )
    __lowercase     							=		0.0
    y += (h / 3.0) * f(a__     )
    __lowercase     							=		2
    for i in x_i:
        y += (h / 3) * (4 - 2 * (cnt % 2)) * f(a__     )
        cnt += 1
    y += (h / 3.0) * f(a__     )
    return y
def 							snake_case_    (  a__						:      Optional[Any]       ,a__						:      List[str]       ,a__						:      Optional[Any]     ):



    """simple docstring"""




    __lowercase     							=		a + h
    while x < (b - h):
        yield x
        __lowercase     							=		x + h
def 							snake_case_    (  a__						:      List[Any]     ):  # enter your function here



    """simple docstring"""




    __lowercase     							=		(x - 0) * (x - 0)
    return y
def 							snake_case_    (  ):



    """simple docstring"""




    __lowercase     							=		0.0  # Lower bound of integration
    __lowercase     							=		1.0  # Upper bound of integration
    __lowercase     							=		1_0.0  # define number of steps or resolution
    __lowercase     							=		[a, b]  # define boundary of integration
    __lowercase     							=		method_a(a__       ,a__     )
    print(f'y = {y}'     )


if __name__ == "__main__":
  main()




