





import os
import warnings
from typing import List, Optional

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


UpperCamelCase_									=logging.get_logger(__name__)




class 			_a						:






				def __init__(			self		:   str,			lowerCAmelCase__		:   Dict,			lowerCAmelCase__		:   List[str]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   question_encoder
										_UpperCamelCase      : Any         =   generator
										_UpperCamelCase      : Union[str, Any]         =   self.question_encoder






				def     snake_case     (			self		:   str,			lowerCAmelCase__		:   Union[str, Any]							)     ->				Union[str, Any]:




										'''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							)
										_UpperCamelCase      : Optional[Any]         =   os.path.join(_a,			'''question_encoder_tokenizer'''							)
										_UpperCamelCase      : Dict         =   os.path.join(_a,			'''generator_tokenizer'''							)
										self.question_encoder.save_pretrained(_a							)
										self.generator.save_pretrained(_a							)






				@classmethod
				def     snake_case     (			cls		:   Any,			lowerCAmelCase__		:   Any,			**lowerCAmelCase__		:   Union[str, Any]							)     ->				Tuple:




										'''simple docstring'''



										from ..auto.tokenization_auto import AutoTokenizer

										_UpperCamelCase      : Any         =   kwargs.pop('''config''',			_a							)

										if config is None:
																_UpperCamelCase      : str         =   RagConfig.from_pretrained(_a							)

										_UpperCamelCase      : List[Any]         =   AutoTokenizer.from_pretrained(
										    _a,			config=config.question_encoder,			subfolder='''question_encoder_tokenizer'''							)
										_UpperCamelCase      : Optional[int]         =   AutoTokenizer.from_pretrained(
										    _a,			config=config.generator,			subfolder='''generator_tokenizer'''							)
										return cls(question_encoder=_a,			generator=_a							)






				def __call__(			self		:   str,			*lowerCAmelCase__		:   Optional[int],			**lowerCAmelCase__		:   Union[str, Any]							)     ->				Optional[int]:




										'''simple docstring'''



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






				def     snake_case     (			self		:   List[str],			*lowerCAmelCase__		:   List[str],			**lowerCAmelCase__		:   List[str]							)     ->				Any:




										'''simple docstring'''



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






				def     snake_case     (			self		:   Tuple,			*lowerCAmelCase__		:   Dict,			**lowerCAmelCase__		:   Optional[int]							)     ->				str:




										'''simple docstring'''



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






				def     snake_case     (			self		:   str							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   self.question_encoder






				def     snake_case     (			self		:   Tuple							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   self.generator






				def     snake_case     (			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],			)     ->				str:




										'''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:
																_UpperCamelCase      : int         =   self.current_tokenizer.model_max_length
										_UpperCamelCase      : Tuple         =   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:
																_UpperCamelCase      : List[Any]         =   self.current_tokenizer.model_max_length
										_UpperCamelCase      : Union[str, Any]         =   self(
										    text_target=_a,			add_special_tokens=_a,			return_tensors=_a,			padding=_a,			max_length=_a,			truncation=_a,			**_a,			)
										_UpperCamelCase      : Dict         =   labels["input_ids"]
										return model_inputs










"""simple docstring"""




from __future__ import annotations

import math





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):

						if depth < 0:
												raise ValueError('''Depth cannot be less than 0'''       )

						if len(_lowercase       ) == 0:
												raise ValueError('''Scores cannot be empty'''       )

						if depth == height:
												return scores[node_index]

						if is_max:
												return max(
												    minimax(depth + 1	,						node_index * 2	,						_lowercase	,						_lowercase	,						_lowercase       )	,						minimax(depth + 1	,						node_index * 2 + 1	,						_lowercase	,						_lowercase	,						_lowercase       )	,						)

						return min(
						    minimax(depth + 1	,						node_index * 2	,						_lowercase	,						_lowercase	,						_lowercase       )	,						minimax(depth + 1	,						node_index * 2 + 1	,						_lowercase	,						_lowercase	,						_lowercase       )	,						)





def 	a_    (							):
						_UpperCamelCase      : Dict         =   [90, 23, 6, 33, 21, 65, 123, 3_4423]
						_UpperCamelCase      : int         =   math.log(len(_lowercase       )	,						2       )
						print('''Optimal value : '''	,						end=''''''       )
						print(minimax(0	,						0	,						_lowercase	,						_lowercase	,						_lowercase       )       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()



