






import os
import warnings
from typing import List, Optional

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


snake_case     					=		logging.get_logger(__name__)


class 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


     def __init__(					self							:						Optional[Any]       ,			UpperCAmelCase_							:						List[str]       ,			UpperCAmelCase_							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     question_encoder
       SCREAMING_SNAKE_CASE							:   str						    =     generator
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.question_encoder


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Any   ):
       if os.path.isfile(UpperCAmelCase_   ):
         raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''   )
       os.makedirs(UpperCAmelCase_       ,			exist_ok=UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     os.path.join(UpperCAmelCase_       ,			"question_encoder_tokenizer"   )
       SCREAMING_SNAKE_CASE							:   Any						    =     os.path.join(UpperCAmelCase_       ,			"generator_tokenizer"   )
       self.question_encoder.save_pretrained(UpperCAmelCase_   )
       self.generator.save_pretrained(UpperCAmelCase_   )


     @classmethod
     def 					_A							(					cls							:						List[Any]       ,			UpperCAmelCase_							:						List[Any]       ,			**UpperCAmelCase_							:						List[str]   ):
       # dynamically import AutoTokenizer
       from ..auto.tokenization_auto import AutoTokenizer

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     kwargs.pop("config"       ,			UpperCAmelCase_   )

       if config is None:
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     RagConfig.from_pretrained(UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     AutoTokenizer.from_pretrained(
           UpperCAmelCase_       ,			config=config.question_encoder       ,			subfolder="question_encoder_tokenizer"   )
       SCREAMING_SNAKE_CASE							:   str						    =     AutoTokenizer.from_pretrained(
           UpperCAmelCase_       ,			config=config.generator       ,			subfolder="generator_tokenizer"   )
       return cls(question_encoder=UpperCAmelCase_       ,			generator=UpperCAmelCase_   )


     def __call__(					self							:						Optional[Any]       ,			*UpperCAmelCase_							:						int       ,			**UpperCAmelCase_							:						List[str]   ):
       return self.current_tokenizer(*UpperCAmelCase_       ,			**UpperCAmelCase_   )


     def 					_A							(					self							:						Optional[int]       ,			*UpperCAmelCase_							:						Union[str, Any]       ,			**UpperCAmelCase_							:						Tuple   ):
       return self.generator.batch_decode(*UpperCAmelCase_       ,			**UpperCAmelCase_   )


     def 					_A							(					self							:						Union[str, Any]       ,			*UpperCAmelCase_							:						Optional[int]       ,			**UpperCAmelCase_							:						int   ):
       return self.generator.decode(*UpperCAmelCase_       ,			**UpperCAmelCase_   )


     def 					_A							(					self							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.question_encoder


     def 					_A							(					self							:						Dict   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.generator






     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						List[str]       ,			UpperCAmelCase_							:						Optional[List[str]] = None       ,			UpperCAmelCase_							:						Optional[int] = None       ,			UpperCAmelCase_							:						Optional[int] = None       ,			UpperCAmelCase_							:						str = "longest"       ,			UpperCAmelCase_							:						str = None       ,			UpperCAmelCase_							:						bool = True       ,			**UpperCAmelCase_							:						Optional[Any]       ,			):
       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"       ,			UpperCAmelCase_       ,			)
       if max_length is None:
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.current_tokenizer.model_max_length
       SCREAMING_SNAKE_CASE							:   List[str]						    =     self(
           UpperCAmelCase_       ,			add_special_tokens=UpperCAmelCase_       ,			return_tensors=UpperCAmelCase_       ,			max_length=UpperCAmelCase_       ,			padding=UpperCAmelCase_       ,			truncation=UpperCAmelCase_       ,			**UpperCAmelCase_       ,			)
       if tgt_texts is None:
         return model_inputs
       # Process tgt_texts
       if max_target_length is None:
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.current_tokenizer.model_max_length
       SCREAMING_SNAKE_CASE							:   str						    =     self(
           text_target=UpperCAmelCase_       ,			add_special_tokens=UpperCAmelCase_       ,			return_tensors=UpperCAmelCase_       ,			padding=UpperCAmelCase_       ,			max_length=UpperCAmelCase_       ,			truncation=UpperCAmelCase_       ,			**UpperCAmelCase_       ,			)
       SCREAMING_SNAKE_CASE							:   str						    =     labels["input_ids"]
       return model_inputs











'''simple docstring'''
def A__       (	UpperCAmelCase_ = 6_0_0_8_5_1_4_7_5_1_4_3				):

						try:
												_UpperCamelCase   :						str												=  int(UpperCAmelCase_				)
						except (TypeError, ValueError):
												raise TypeError('Parameter n must be int or castable to int.'				)
						if n <= 0:
												raise ValueError('Parameter n must be greater than or equal to one.'				)
						_UpperCamelCase   :						int												=  1
						_UpperCamelCase   :						Optional[Any]												=  2
						while i * i <= n:
												while n % i == 0:
																		_UpperCamelCase   :						Union[str, Any]												=  i
																		n //= i
												i += 1
						if n > 1:
												_UpperCamelCase   :						Dict												=  n
						return int(UpperCAmelCase_				)


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


