






"""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						(		snake_case__ , snake_case__ , snake_case__       ):
	# Initialise PyTorch model
	A_		:    List[str]						     = TaConfig.from_json_file(snake_case__       )
	print(F"""Building PyTorch model from configuration: {config}"""       )
	A_		:    List[str]						     = TaForConditionalGeneration(snake_case__       )

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

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


if __name__ == "__main__":
							_lowerCAmelCase       =				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."
							)
							_lowerCAmelCase       =				parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)









"""simple docstring"""
from __future__ import annotations






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	if len(snake_case__       ) == 0:
		return False
	A_		:    Union[str, Any]						     = len(snake_case__       ) // 2
	if a_list[midpoint] == item:
		return True
	if item < a_list[midpoint]:
		return binary_search(a_list[:midpoint] , snake_case__       )
	else:
		return binary_search(a_list[midpoint + 1 :] , snake_case__       )


if __name__ == "__main__":
							_lowerCAmelCase       =				input("Enter numbers separated by comma:\n").strip()
							_lowerCAmelCase       =				[int(item.strip()) for item in user_input.split(",")]
							_lowerCAmelCase       =				int(input("Enter the number to be found in the list:\n").strip())
							_lowerCAmelCase       =				"" if binary_search(sequence, target) else "not "
							print(F'{target} was {not_str}found in {sequence}')

