






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      	=  logging.get_logger(__name__)





class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			question_encoder
							_UpperCAmelCase									=			generator
							_UpperCAmelCase									=			self.question_encoder

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							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    )
							_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    'question_encoder_tokenizer'    )
							_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    'generator_tokenizer'    )
							self.question_encoder.save_pretrained(UpperCAmelCase    )
							self.generator.save_pretrained(UpperCAmelCase    )

	@classmethod
	def        UpperCamelCase    (							cls       ,    UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							from ..auto.tokenization_auto import AutoTokenizer

							_UpperCAmelCase									=			kwargs.pop('config'       ,    UpperCAmelCase    )

							if config is None:
													_UpperCAmelCase									=			RagConfig.from_pretrained(UpperCAmelCase    )

							_UpperCAmelCase									=			AutoTokenizer.from_pretrained(
							    UpperCAmelCase       ,    config=config.question_encoder       ,    subfolder='question_encoder_tokenizer'    )
							_UpperCAmelCase									=			AutoTokenizer.from_pretrained(
							    UpperCAmelCase       ,    config=config.generator       ,    subfolder='generator_tokenizer'    )
							return cls(question_encoder=UpperCAmelCase       ,    generator=UpperCAmelCase    )

	def __call__(							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.question_encoder

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.generator

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = "longest"       ,    UpperCAmelCase = None       ,    UpperCAmelCase = True       ,    **UpperCAmelCase       ,    ):
							"""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'       ,    UpperCAmelCase       ,    )
							if max_length is None:
													_UpperCAmelCase									=			self.current_tokenizer.model_max_length
							_UpperCAmelCase									=			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:
													_UpperCAmelCase									=			self.current_tokenizer.model_max_length
							_UpperCAmelCase									=			self(
							    text_target=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    padding=UpperCAmelCase       ,    max_length=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    **UpperCAmelCase       ,    )
							_UpperCAmelCase									=			labels['input_ids']
							return model_inputs








from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

lowerCamelCase	:							int				=TypeVar('''T''')





class 			__a  (    Generic[T]       ):
 _lowerCAmelCase       :       deque[T]  # Cache store of keys
 _lowerCAmelCase       :       set[T]  # References of the keys in cache
 _lowerCAmelCase       :       int        = 1_0  # Maximum capacity of cache







 def __init__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  deque()
      UpperCamelCase__			:   Optional[int]       				=  set()
      if not n:
           UpperCamelCase__			:   Tuple       				=  sys.maxsize
      elif n < 0:
           raise ValueError("n should be an integer greater than 0."						)
      else:
           UpperCamelCase__			:   Dict       				=  n







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   T						):


      '''simple docstring'''






      if x not in self.key_reference:
           if len(self.dq_store						) == LRUCache._MAX_CAPACITY:
                UpperCamelCase__			:   int       				=  self.dq_store.pop()
                self.key_reference.remove(SCREAMING_SNAKE_CASE						)
      else:
           self.dq_store.remove(SCREAMING_SNAKE_CASE						)

      self.dq_store.appendleft(SCREAMING_SNAKE_CASE						)
      self.key_reference.add(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      for k in self.dq_store:
           print(SCREAMING_SNAKE_CASE						)







 def __repr__(		self					:   Union[str, Any]						):


      '''simple docstring'''






      return F'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store						)}'


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase	:							LRUCache[str | int]				=LRUCache(4)
 lru_cache.refer('''A''')
 lru_cache.refer(2)
 lru_cache.refer(3)
 lru_cache.refer('''A''')
 lru_cache.refer(4)
 lru_cache.refer(5)
 lru_cache.display()

 print(lru_cache)
 assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"