





import argparse

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


logging.set_verbosity_info()

def      a    (   _lowercase :  int			,    _lowercase :  Dict			,    _lowercase :  Union[str, Any]			,    _lowercase :  Optional[int]			,    _lowercase :  str							)  ->Optional[Any]:
		# Initialise PyTorch model
		UpperCamelCase__								=						BertConfig.from_pretrained(
		    """bert-large-cased"""			,    vocab_size=_lowercase			,    max_position_embeddings=5_1_2			,    is_decoder=_lowercase			,    add_cross_attention=_lowercase			,    )
		UpperCamelCase__								=						bert_config.to_dict()
		del bert_config_dict["type_vocab_size"]
		UpperCamelCase__								=						BertGenerationConfig(**_lowercase							)
		if is_encoder:
				UpperCamelCase__								=						BertGenerationEncoder(_lowercase							)
		else:
				UpperCamelCase__								=						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__":
				__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=50_358, 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,
				)












from __future__ import annotations

def      a    (   _lowercase :  float | int			,    _lowercase :  float | int			,    _lowercase :  float | int			,    )  ->list[float | int]:
		if not all((nth_term, start_term_a, common_ratio_r)							):
				return []
		UpperCamelCase__								=						[]
		UpperCamelCase__								=						1
		UpperCamelCase__								=						common_ratio_r
		for _ in range(int(_lowercase							)							):
				if not series:
						series.append(_lowercase							)
				else:
						power += 1
						series.append(float(start_term_a * multiple							)							)
						UpperCamelCase__								=						pow(float(_lowercase							)			,    _lowercase							)
		return series


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__lowerCAmelCase 	=      float(input("Enter the last number (n term) of the Geometric Series"))
				__lowerCAmelCase 	=      float(input("Enter the starting term (a) of the Geometric Series"))
				__lowerCAmelCase 	=      float(
				    input("Enter the common ratio between two terms (r) of the Geometric Series")
				)
				print("Formula of Geometric Series => a + ar + ar^2 ... +ar^n")
				print(geometric_series(nth_term, start_term_a, common_ratio_r))





