






import os
import warnings
from typing import List, Optional

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


lowerCAmelCase_           = logging.get_logger(__name__)
class      UpperCamelCase :







		"""simple docstring"""



		def __init__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   str:


						'''simple docstring'''
						A	 =      question_encoder
						A	 =      generator
						A	 =      self.question_encoder
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       Any		)    ->   List[str]:


						'''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_		)
						A	 =      os.path.join(UpperCAmelCase_						,'question_encoder_tokenizer'		)
						A	 =      os.path.join(UpperCAmelCase_						,'generator_tokenizer'		)
						self.question_encoder.save_pretrained(UpperCAmelCase_		)
						self.generator.save_pretrained(UpperCAmelCase_		)
		@classmethod
		def       A(  cls		:       str						,_SCREAMING_SNAKE_CASE		:       Any						,**_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   List[Any]:


						'''simple docstring'''
						# dynamically import AutoTokenizer
						from ..auto.tokenization_auto import AutoTokenizer

						A	 =      kwargs.pop('config'						,UpperCAmelCase_		)

						if config is None:
										A	 =      RagConfig.from_pretrained(UpperCAmelCase_		)

						A	 =      AutoTokenizer.from_pretrained(
						    UpperCAmelCase_						,config=config.question_encoder						,subfolder='question_encoder_tokenizer'		)
						A	 =      AutoTokenizer.from_pretrained(
						    UpperCAmelCase_						,config=config.generator						,subfolder='generator_tokenizer'		)
						return cls(question_encoder=UpperCAmelCase_						,generator=UpperCAmelCase_		)
		def __call__(  self		:       str						,*_SCREAMING_SNAKE_CASE		:       Optional[Any]						,**_SCREAMING_SNAKE_CASE		:       Any		)    ->   Optional[int]:


						'''simple docstring'''
						return self.current_tokenizer(*UpperCAmelCase_						,**UpperCAmelCase_		)
		def       A(  self		:       str						,*_SCREAMING_SNAKE_CASE		:       List[Any]						,**_SCREAMING_SNAKE_CASE		:       str		)    ->   List[str]:


						'''simple docstring'''
						return self.generator.batch_decode(*UpperCAmelCase_						,**UpperCAmelCase_		)
		def       A(  self		:       Tuple						,*_SCREAMING_SNAKE_CASE		:       Optional[Any]						,**_SCREAMING_SNAKE_CASE		:       Dict		)    ->   str:


						'''simple docstring'''
						return self.generator.decode(*UpperCAmelCase_						,**UpperCAmelCase_		)
		def       A(  self		:       Any		)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      self.question_encoder
		def       A(  self		:       str		)    ->   List[str]:


						'''simple docstring'''
						A	 =      self.generator







		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       List[str]						,_SCREAMING_SNAKE_CASE		:       Optional[List[str]] = None						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       str = "longest"						,_SCREAMING_SNAKE_CASE		:       str = None						,_SCREAMING_SNAKE_CASE		:       bool = True						,**_SCREAMING_SNAKE_CASE		:       Tuple						,)    ->   Tuple:


						'''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:
										A	 =      self.current_tokenizer.model_max_length
						A	 =      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:
										A	 =      self.current_tokenizer.model_max_length
						A	 =      self(
						    text_target=UpperCAmelCase_						,add_special_tokens=UpperCAmelCase_						,return_tensors=UpperCAmelCase_						,padding=UpperCAmelCase_						,max_length=UpperCAmelCase_						,truncation=UpperCAmelCase_						,**UpperCAmelCase_						,)
						A	 =      labels['input_ids']
						return model_inputs














from collections import deque

from .hash_table import HashTable
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def __init__(  self		:       Any						,*_SCREAMING_SNAKE_CASE		:       Optional[int]						,**_SCREAMING_SNAKE_CASE		:       Optional[Any]		)    ->   int:


						'''simple docstring'''
						super().__init__(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   List[str]:


						'''simple docstring'''
						A	 =      deque([]		) if self.values[key] is None else self.values[key]
						self.values[key].appendleft(_SCREAMING_SNAKE_CASE		)
						A	 =      self.values[key]
		def       A(  self		:       Optional[Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						return (
						    sum(self.charge_factor - len(_SCREAMING_SNAKE_CASE		) for slot in self.values		)
						    / self.size_table
						    * self.charge_factor
						)







		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       str=None		)    ->   Union[str, Any]:


						'''simple docstring'''
						if not (
						    len(self.values[key]		) == self.charge_factor and self.values.count(_SCREAMING_SNAKE_CASE		) == 0
						):
										return key
						return super()._collision_resolution(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)





