





import os
import warnings
from typing import List, Optional

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


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



class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			question_encoder
        SCREAMING_SNAKE_CASE__											=			generator
        SCREAMING_SNAKE_CASE__											=			self.question_encoder



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        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_     )
        SCREAMING_SNAKE_CASE__											=			os.path.join(A_   ,				'''question_encoder_tokenizer'''     )
        SCREAMING_SNAKE_CASE__											=			os.path.join(A_   ,				'''generator_tokenizer'''     )
        self.question_encoder.save_pretrained(A_     )
        self.generator.save_pretrained(A_     )



    @classmethod
    def  lowercase_					(       cls   ,				A_   ,				**A_     ):





        '''simple docstring'''

        from ..auto.tokenization_auto import AutoTokenizer

        SCREAMING_SNAKE_CASE__											=			kwargs.pop('''config'''   ,				A_     )

        if config is None:
            SCREAMING_SNAKE_CASE__											=			RagConfig.from_pretrained(A_     )

        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained(
            A_   ,				config=config.question_encoder   ,				subfolder='''question_encoder_tokenizer'''     )
        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained(
            A_   ,				config=config.generator   ,				subfolder='''generator_tokenizer'''     )
        return cls(question_encoder=A_   ,				generator=A_     )



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





        '''simple docstring'''

        return self.current_tokenizer(*A_   ,				**A_     )



    def  lowercase_					(       self   ,				*A_   ,				**A_     ):





        '''simple docstring'''

        return self.generator.batch_decode(*A_   ,				**A_     )



    def  lowercase_					(       self   ,				*A_   ,				**A_     ):





        '''simple docstring'''

        return self.generator.decode(*A_   ,				**A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.question_encoder



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.generator




    def  lowercase_					(       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'''   ,				A_   ,				)
        if max_length is None:
            SCREAMING_SNAKE_CASE__											=			self.current_tokenizer.model_max_length
        SCREAMING_SNAKE_CASE__											=			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:
            SCREAMING_SNAKE_CASE__											=			self.current_tokenizer.model_max_length
        SCREAMING_SNAKE_CASE__											=			self(
            text_target=A_   ,				add_special_tokens=A_   ,				return_tensors=A_   ,				padding=A_   ,				max_length=A_   ,				truncation=A_   ,				**A_   ,				)
        SCREAMING_SNAKE_CASE__											=			labels["input_ids"]
        return model_inputs








'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase = 100				):


   """simple docstring"""




   lowercase_						:     Dict							     =  n * (n + 1) * (2 * n + 1) / 6
   lowercase_						:     int							     =  (n * (n + 1) / 2) ** 2
   return int(square_of_sum - sum_of_squares				)


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




