


import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


_lowercase							:   Optional[Any]						=						logging.get_logger(__name__)






class    _UpperCamelCase     :

				"""simple docstring"""







				def __init__(    self					, a__					, a__ )						->					Optional[Any]:
					A             =					question_encoder
					A             =					generator
					A             =					self.question_encoder

				def   _UpperCAmelCase       (    self					, a__ )						->					Optional[int]:
					if os.path.isfile(a__ ):
						raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file' )
					os.makedirs(a__					, exist_ok=a__ )
					A             =					os.path.join(a__					, """question_encoder_tokenizer""" )
					A             =					os.path.join(a__					, """generator_tokenizer""" )
					self.question_encoder.save_pretrained(a__ )
					self.generator.save_pretrained(a__ )

				@classmethod
				def   _UpperCAmelCase       (    cls					, a__					, **a__ )						->					Union[str, Any]:
					# dynamically import AutoTokenizer
					from ..auto.tokenization_auto import AutoTokenizer

					A             =					kwargs.pop("""config"""					, a__ )

					if config is None:
						A             =					RagConfig.from_pretrained(a__ )

					A             =					AutoTokenizer.from_pretrained(
					    a__					, config=config.question_encoder					, subfolder="""question_encoder_tokenizer""" )
					A             =					AutoTokenizer.from_pretrained(
					    a__					, config=config.generator					, subfolder="""generator_tokenizer""" )
					return cls(question_encoder=a__					, generator=a__ )

				def __call__(    self					, *a__					, **a__ )						->					Optional[int]:
					return self.current_tokenizer(*a__					, **a__ )

				def   _UpperCAmelCase       (    self					, *a__					, **a__ )						->					List[Any]:
					return self.generator.batch_decode(*a__					, **a__ )

				def   _UpperCAmelCase       (    self					, *a__					, **a__ )						->					List[Any]:
					return self.generator.decode(*a__					, **a__ )

				def   _UpperCAmelCase       (    self )						->					int:
					A             =					self.question_encoder

				def   _UpperCAmelCase       (    self )						->					List[str]:
					A             =					self.generator

				def   _UpperCAmelCase       (    self					, a__					, a__ = None					, a__ = None					, a__ = None					, a__ = "longest"					, a__ = None					, a__ = True					, **a__					, )						->					BatchEncoding:
					warnings.warn(
					    """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
					    """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
					    """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
					    """details"""					, a__					, )
					if max_length is None:
						A             =					self.current_tokenizer.model_max_length
					A             =					self(
					    a__					, add_special_tokens=a__					, return_tensors=a__					, max_length=a__					, padding=a__					, truncation=a__					, **a__					, )
					if tgt_texts is None:
						return model_inputs
					# Process tgt_texts
					if max_target_length is None:
						A             =					self.current_tokenizer.model_max_length
					A             =					self(
					    text_target=a__					, add_special_tokens=a__					, return_tensors=a__					, padding=a__					, max_length=a__					, truncation=a__					, **a__					, )
					A             =					labels["""input_ids"""]
					return model_inputs







from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def 	_lowerCAmelCase					( )			-> str:





	"""simple docstring"""







	A             =					HfArgumentParser(UpperCamelCase__    )
	A             =					parser.parse_args_into_dataclasses()[0]
	A             =					TensorFlowBenchmark(args=UpperCamelCase__    )
	try:
		A             =					parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		A             =					"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
		A             =					""" """.join(str(UpperCamelCase__    ).split(""" """    )[:-1]    )
		A             =					""""""
		A             =					eval(str(UpperCamelCase__    ).split(""" """    )[-1]    )
		A             =					[]
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in TensorFlowBenchmark.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]    )
			else:
				wrong_args.append(UpperCamelCase__    )
		if len(UpperCamelCase__    ) > 0:
			A             =					full_error_msg + begin_error_msg + str(UpperCamelCase__    )
		raise ValueError(UpperCamelCase__    )
	benchmark.run()


if __name__ == "__main__":
				main()



