

'''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


A_	= logging.get_logger(__name__)


class  UpperCAmelCase :

		'''simple docstring'''






		def __init__(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      question_encoder
							lowerCamelCase_  				=      generator
							lowerCamelCase_  				=      self.question_encoder
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     List[str]:






							'''simple docstring'''



							if os.path.isfile(SCREAMING_SNAKE_CASE_		):
												raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''		)
							os.makedirs(SCREAMING_SNAKE_CASE_   ,			exist_ok=SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      os.path.join(SCREAMING_SNAKE_CASE_   ,			'question_encoder_tokenizer'		)
							lowerCamelCase_  				=      os.path.join(SCREAMING_SNAKE_CASE_   ,			'generator_tokenizer'		)
							self.question_encoder.save_pretrained(SCREAMING_SNAKE_CASE_		)
							self.generator.save_pretrained(SCREAMING_SNAKE_CASE_		)
		@classmethod
		def 			UpperCamelCase(					cls   ,			SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)  ->     Dict:






							'''simple docstring'''



							from ..auto.tokenization_auto import AutoTokenizer

							lowerCamelCase_  				=      kwargs.pop('config'   ,			SCREAMING_SNAKE_CASE_		)

							if config is None:
												lowerCamelCase_  				=      RagConfig.from_pretrained(SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      AutoTokenizer.from_pretrained(
							    SCREAMING_SNAKE_CASE_   ,			config=config.question_encoder   ,			subfolder='question_encoder_tokenizer'		)
							lowerCamelCase_  				=      AutoTokenizer.from_pretrained(
							    SCREAMING_SNAKE_CASE_   ,			config=config.generator   ,			subfolder='generator_tokenizer'		)
							return cls(question_encoder=SCREAMING_SNAKE_CASE_   ,			generator=SCREAMING_SNAKE_CASE_		)
		def __call__(					self   ,			*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)  ->     Optional[Any]:






							'''simple docstring'''



							return self.current_tokenizer(*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self   ,			*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)  ->     Dict:






							'''simple docstring'''



							return self.generator.batch_decode(*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self   ,			*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)  ->     Dict:






							'''simple docstring'''



							return self.generator.decode(*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_  				=      self.question_encoder
		def 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.generator






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = "longest"   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = True   ,			**SCREAMING_SNAKE_CASE_   ,			)  ->     BatchEncoding:






							'''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'   ,			SCREAMING_SNAKE_CASE_   ,			)
							if max_length is None:
												lowerCamelCase_  				=      self.current_tokenizer.model_max_length
							lowerCamelCase_  				=      self(
							    SCREAMING_SNAKE_CASE_   ,			add_special_tokens=SCREAMING_SNAKE_CASE_   ,			return_tensors=SCREAMING_SNAKE_CASE_   ,			max_length=SCREAMING_SNAKE_CASE_   ,			padding=SCREAMING_SNAKE_CASE_   ,			truncation=SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_   ,			)
							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=SCREAMING_SNAKE_CASE_   ,			add_special_tokens=SCREAMING_SNAKE_CASE_   ,			return_tensors=SCREAMING_SNAKE_CASE_   ,			padding=SCREAMING_SNAKE_CASE_   ,			max_length=SCREAMING_SNAKE_CASE_   ,			truncation=SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_   ,			)
							lowerCamelCase_  				=      labels['input_ids']
							return model_inputs









'''simple docstring'''



def       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  int ,						_lowerCamelCase				:  int						)-> float:




     '''simple docstring'''





     __snake_case            =	(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
     # formula for sum of series
     return total







def       _UpperCamelCase			()-> str:




     '''simple docstring'''





     print(sum_of_series(1 ,						1 ,						10						)						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






