





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       :   Any					= logging.get_logger(__name__)


class     A     :
			'''simple docstring'''




			def __init__(     self  :       int				,      __lowerCAmelCase  :       Any				,      __lowerCAmelCase  :       Tuple  )      ->      Tuple:







					"""simple docstring"""






					A__     =  question_encoder
					A__     =  generator
					A__     =  self.question_encoder






			def   a_			(     self  :       List[Any]				,      __lowerCAmelCase  :       Union[str, Any]  )      ->      int:







					"""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  )
					A__     =  os.path.join(__lowerCAmelCase				,      """question_encoder_tokenizer"""  )
					A__     =  os.path.join(__lowerCAmelCase				,      """generator_tokenizer"""  )
					self.question_encoder.save_pretrained(__lowerCAmelCase  )
					self.generator.save_pretrained(__lowerCAmelCase  )






			@classmethod
			def   a_			(     cls  :       Optional[int]				,      __lowerCAmelCase  :       str				,      **__lowerCAmelCase  :       Dict  )      ->      Any:







					"""simple docstring"""






					from ..auto.tokenization_auto import AutoTokenizer

					A__     =  kwargs.pop("""config"""				,      __lowerCAmelCase  )

					if config is None:
							A__     =  RagConfig.from_pretrained(__lowerCAmelCase  )

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






			def __call__(     self  :       Dict				,      *__lowerCAmelCase  :       Optional[int]				,      **__lowerCAmelCase  :       Any  )      ->      Dict:







					"""simple docstring"""






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






			def   a_			(     self  :       str				,      *__lowerCAmelCase  :       Tuple				,      **__lowerCAmelCase  :       int  )      ->      str:







					"""simple docstring"""






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






			def   a_			(     self  :       Optional[Any]				,      *__lowerCAmelCase  :       Tuple				,      **__lowerCAmelCase  :       int  )      ->      Optional[int]:







					"""simple docstring"""






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






			def   a_			(     self  :       Tuple  )      ->      int:







					"""simple docstring"""






					A__     =  self.question_encoder






			def   a_			(     self  :       Tuple  )      ->      Union[str, Any]:







					"""simple docstring"""






					A__     =  self.generator







			def   a_			(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       List[str]				,      __lowerCAmelCase  :       Optional[List[str]] = None				,      __lowerCAmelCase  :       Optional[int] = None				,      __lowerCAmelCase  :       Optional[int] = None				,      __lowerCAmelCase  :       str = "longest"				,      __lowerCAmelCase  :       str = None				,      __lowerCAmelCase  :       bool = True				,      **__lowerCAmelCase  :       List[Any]				,      )      ->      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"""				,      __lowerCAmelCase				,      )
					if max_length is None:
							A__     =  self.current_tokenizer.model_max_length
					A__     =  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:
							A__     =  self.current_tokenizer.model_max_length
					A__     =  self(
					    text_target=__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase				,      return_tensors=__lowerCAmelCase				,      padding=__lowerCAmelCase				,      max_length=__lowerCAmelCase				,      truncation=__lowerCAmelCase				,      **__lowerCAmelCase				,      )
					A__     =  labels["""input_ids"""]
					return model_inputs










def   __lowerCamelCase   (				__a  :int       ,	__a  :int       )						->							Optional[Any]:






		"""simple docstring"""

		if b == 0:
				return 1
		if (b % 2) == 0:
				return actual_power(__a       ,	int(b / 2       )       ) * actual_power(__a       ,	int(b / 2       )       )
		else:
				return a * actual_power(__a       ,	int(b / 2       )       ) * actual_power(__a       ,	int(b / 2       )       )







def   __lowerCamelCase   (				__a  :int       ,	__a  :int       )						->							float:






		"""simple docstring"""

		if b < 0:
				return 1 / actual_power(__a       ,	__a       )
		return actual_power(__a       ,	__a       )


if __name__ == "__main__":
					print(power(-2, -3))



