




"""simple docstring"""




import os
import warnings
from typing import List, Optional

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


lowerCAmelCase							:			Union[str, Any]     							=							logging.get_logger(__name__)






class     __magic_name__							:






				'''simple docstring'''





				def __init__(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					question_encoder
							lowerCamelCase			      =					generator
							lowerCamelCase			      =					self.question_encoder





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""






							if os.path.isfile(lowerCAmelCase_ ):
										raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file' )
							os.makedirs(lowerCAmelCase_		, exist_ok=lowerCAmelCase_ )
							lowerCamelCase			      =					os.path.join(lowerCAmelCase_		, """question_encoder_tokenizer""" )
							lowerCamelCase			      =					os.path.join(lowerCAmelCase_		, """generator_tokenizer""" )
							self.question_encoder.save_pretrained(lowerCAmelCase_ )
							self.generator.save_pretrained(lowerCAmelCase_ )





				@classmethod
				def    _lowerCAmelCase				(       cls		, _a		, **_a ):





							"""simple docstring"""
							from ..auto.tokenization_auto import AutoTokenizer

							lowerCamelCase			      =					kwargs.pop("""config"""		, lowerCAmelCase_ )

							if config is None:
										lowerCamelCase			      =					RagConfig.from_pretrained(lowerCAmelCase_ )

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





				def __call__(       self		, *_a		, **_a ):





							"""simple docstring"""
							return self.current_tokenizer(*lowerCAmelCase_		, **lowerCAmelCase_ )





				def    _lowerCAmelCase				(       self		, *_a		, **_a ):





							"""simple docstring"""
							return self.generator.batch_decode(*lowerCAmelCase_		, **lowerCAmelCase_ )





				def    _lowerCAmelCase				(       self		, *_a		, **_a ):





							"""simple docstring"""
							return self.generator.decode(*lowerCAmelCase_		, **lowerCAmelCase_ )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.question_encoder





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.generator





				def    _lowerCAmelCase				(       self		, _a		, _a = None		, _a = None		, _a = None		, _a = "longest"		, _a = None		, _a = True		, **_a		, ):





							"""simple docstring"""
							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"""		, lowerCAmelCase_		, )
							if max_length is None:
										lowerCamelCase			      =					self.current_tokenizer.model_max_length
							lowerCamelCase			      =					self(
							    lowerCAmelCase_		, add_special_tokens=lowerCAmelCase_		, return_tensors=lowerCAmelCase_		, max_length=lowerCAmelCase_		, padding=lowerCAmelCase_		, truncation=lowerCAmelCase_		, **lowerCAmelCase_		, )
							if tgt_texts is None:
										return model_inputs
							# Process tgt_texts
							if max_target_length is None:
										lowerCamelCase			      =					self.current_tokenizer.model_max_length
							lowerCamelCase			      =					self(
							    text_target=lowerCAmelCase_		, add_special_tokens=lowerCAmelCase_		, return_tensors=lowerCAmelCase_		, padding=lowerCAmelCase_		, max_length=lowerCAmelCase_		, truncation=lowerCAmelCase_		, **lowerCAmelCase_		, )
							lowerCamelCase			      =					labels["""input_ids"""]
							return model_inputs





'''simple docstring'''







def 				SCREAMING_SNAKE_CASE__	(   __A = 100	)       ->	int:
	_snake_case   						=		n * (n + 1) * (2 * n + 1) / 6
	_snake_case   						=		(n * (n + 1) / 2) ** 2
	return int(square_of_sum - sum_of_squares	)


if __name__ == "__main__":
					print(F'''{solution() = }''')

