





import random







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[Any]:
				UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_:							str	=						[], [], []
				for element in data:
								if element < pivot:
												less.append(_UpperCAmelCase						)
								elif element > pivot:
												greater.append(_UpperCAmelCase						)
								else:
												equal.append(_UpperCAmelCase						)
				return less, equal, greater







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	List[Any]:
				# index = len(items) // 2 when trying to find the median
				#   (value of index when items is sorted)

				# invalid input
				if index >= len(_UpperCAmelCase						) or index < 0:
								return None

				UpperCamelCase_:							List[str]	=						items[random.randint(0     ,    len(_UpperCAmelCase						) - 1						)]
				UpperCamelCase_:							Dict	=						0
				UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_:							int	=						_partition(_UpperCAmelCase     ,    _UpperCAmelCase						)
				UpperCamelCase_:							str	=						len(_UpperCAmelCase						)
				UpperCamelCase_:							Tuple	=						len(_UpperCAmelCase						)

				# index is the pivot
				if m <= index < m + count:
								return pivot
				# must be in smaller
				elif m > index:
								return quick_select(_UpperCAmelCase     ,    _UpperCAmelCase						)
				# must be in larger
				else:
								return quick_select(_UpperCAmelCase     ,    index - (m + count)						)








import argparse

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


logging.set_verbosity_info()







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
				# Initialise PyTorch model
				UpperCamelCase_:							List[Any]	=						TaConfig.from_json_file(lowerCamelCase						)
				print(F'''Building PyTorch model from configuration: {config}'''						)
				UpperCamelCase_:							Any	=						TaForConditionalGeneration(lowerCamelCase						)

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

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


if __name__ == "__main__":
							lowerCamelCase_						: Tuple         =  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_						: Union[str, Any]         =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






