

'''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


lowercase__         =      logging.get_logger(__name__)




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					int					,      lowercase_			:					Optional[int]					,      lowercase_			:					Union[str, Any]	)						->     Dict:
      UpperCAmelCase	:     Tuple						 =			question_encoder
      UpperCAmelCase	:     Union[str, Any]						 =			generator
      UpperCAmelCase	:     Dict						 =			self.question_encoder


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


     @classmethod
     def        UpperCAmelCase_	(		cls			:					List[Any]					,      lowercase_			:					Any					,      **lowercase_			:					Dict	)						->     Union[str, Any]:
      # dynamically import AutoTokenizer
      from ..auto.tokenization_auto import AutoTokenizer

      UpperCAmelCase	:     str						 =			kwargs.pop('config'					,      lowercase_	)

      if config is None:
       UpperCAmelCase	:     List[str]						 =			RagConfig.from_pretrained(lowercase_	)

      UpperCAmelCase	:     int						 =			AutoTokenizer.from_pretrained(
          lowercase_					,      config=config.question_encoder					,      subfolder='question_encoder_tokenizer'	)
      UpperCAmelCase	:     Any						 =			AutoTokenizer.from_pretrained(
          lowercase_					,      config=config.generator					,      subfolder='generator_tokenizer'	)
      return cls(question_encoder=lowercase_					,      generator=lowercase_	)


     def __call__(		self			:					List[str]					,      *lowercase_			:					Optional[int]					,      **lowercase_			:					str	)						->     int:
      return self.current_tokenizer(*lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[Any]					,      *lowercase_			:					List[str]					,      **lowercase_			:					List[Any]	)						->     Dict:
      return self.generator.batch_decode(*lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					List[Any]					,      *lowercase_			:					Union[str, Any]					,      **lowercase_			:					str	)						->     int:
      return self.generator.decode(*lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					str	)						->     Optional[int]:
      UpperCAmelCase	:     Optional[int]						 =			self.question_encoder


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Union[str, Any]:
      UpperCAmelCase	:     Optional[Any]						 =			self.generator




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



'''simple docstring'''

from __future__ import annotations




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if len(UpperCAmelCase_     ) == 0:
  raise ValueError('find_max() arg is an empty sequence'     )
 if (
     left >= len(UpperCAmelCase_     )
     or left < -len(UpperCAmelCase_     )
     or right >= len(UpperCAmelCase_     )
     or right < -len(UpperCAmelCase_     )
 ):
  raise IndexError('list index out of range'     )
 if left == right:
  return nums[left]
 UpperCAmelCase	:     Optional[int]						 =			(left + right) >> 1  # the middle
 UpperCAmelCase	:     Any						 =			find_max(UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )  # find max in range[left, mid]
 UpperCAmelCase	:     Union[str, Any]						 =			find_max(UpperCAmelCase_  ,	mid + 1  ,	UpperCAmelCase_     )  # find max in range[mid + 1, right]

 return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)




