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	: List[str]    		=  logging.get_logger(__name__)





class       lowercase__		:


						def __init__( self			:       Dict					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							question_encoder
												SCREAMING_SNAKE_CASE							:					Any           =							generator
												SCREAMING_SNAKE_CASE							:					List[str]           =							self.question_encoder


						def  __A ( self			:       str					,	UpperCamelCase__			:       Dict		):





												'''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		)
												SCREAMING_SNAKE_CASE							:					int           =							os.path.join(_lowerCamelCase					,	'''question_encoder_tokenizer'''		)
												SCREAMING_SNAKE_CASE							:					Any           =							os.path.join(_lowerCamelCase					,	'''generator_tokenizer'''		)
												self.question_encoder.save_pretrained(_lowerCamelCase		)
												self.generator.save_pretrained(_lowerCamelCase		)


						@classmethod
						def  __A ( cls			:       List[str]					,	UpperCamelCase__			:       List[str]					,	**UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												from ..auto.tokenization_auto import AutoTokenizer

												SCREAMING_SNAKE_CASE							:					str           =							kwargs.pop('''config'''					,	_lowerCamelCase		)

												if config is None:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							RagConfig.from_pretrained(_lowerCamelCase		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							AutoTokenizer.from_pretrained(
												    _lowerCamelCase					,	config=config.question_encoder					,	subfolder='''question_encoder_tokenizer'''		)
												SCREAMING_SNAKE_CASE							:					Dict           =							AutoTokenizer.from_pretrained(
												    _lowerCamelCase					,	config=config.generator					,	subfolder='''generator_tokenizer'''		)
												return cls(question_encoder=_lowerCamelCase					,	generator=_lowerCamelCase		)


						def __call__( self			:       Optional[int]					,	*UpperCamelCase__			:       Optional[int]					,	**UpperCamelCase__			:       Tuple		):





												'''simple docstring'''





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


						def  __A ( self			:       Dict					,	*UpperCamelCase__			:       Union[str, Any]					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





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


						def  __A ( self			:       int					,	*UpperCamelCase__			:       Optional[int]					,	**UpperCamelCase__			:       Optional[int]		):





												'''simple docstring'''





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


						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.question_encoder


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.generator



						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Dict = None					,	UpperCamelCase__			:       str = None					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       str = "longest"					,	UpperCamelCase__			:       Dict = None					,	UpperCamelCase__			:       Optional[int] = True					,	**UpperCamelCase__			:       Any					,	):





												'''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:
																		SCREAMING_SNAKE_CASE							:					Any           =							self.current_tokenizer.model_max_length
												SCREAMING_SNAKE_CASE							:					Tuple           =							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:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							self.current_tokenizer.model_max_length
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self(
												    text_target=_lowerCamelCase					,	add_special_tokens=_lowerCamelCase					,	return_tensors=_lowerCamelCase					,	padding=_lowerCamelCase					,	max_length=_lowerCamelCase					,	truncation=_lowerCamelCase					,	**_lowerCamelCase					,	)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							labels['''input_ids''']
												return model_inputs




import random
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Tuple           =							[], [], []
						for element in data:
												if element < pivot:
																		less.append(_lowercase		)
												elif element > pivot:
																		greater.append(_lowercase		)
												else:
																		equal.append(_lowercase		)
						return less, equal, greater



def    A			(					_lowercase	,						_lowercase		):
						# index = len(items) // 2 when trying to find the median
						#   (value of index when items is sorted)

						# invalid input
						if index >= len(_lowercase		) or index < 0:
												return None

						SCREAMING_SNAKE_CASE							:					Dict           =							items[random.randint(0	,						len(_lowercase		) - 1		)]
						SCREAMING_SNAKE_CASE							:					int           =							0
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[str]           =							_partition(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							len(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							len(_lowercase		)

						# index is the pivot
						if m <= index < m + count:
												return pivot
						# must be in smaller
						elif m > index:
												return quick_select(_lowercase	,						_lowercase		)
						# must be in larger
						else:
												return quick_select(_lowercase	,						index - (m + count)		)





