

'''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


UpperCAmelCase :       Dict			   =     logging.get_logger(__name__)







class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       int					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       Tuple			)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      question_encoder
      __SCREAMING_SNAKE_CASE     =      generator
      __SCREAMING_SNAKE_CASE     =      self.question_encoder
 def       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]			)  -> Any:


      """simple docstring"""




      if os.path.isfile(__SCREAMING_SNAKE_CASE			):
           raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'			)
      os.makedirs(__SCREAMING_SNAKE_CASE					,							exist_ok=__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      os.path.join(__SCREAMING_SNAKE_CASE					,							"""question_encoder_tokenizer"""			)
      __SCREAMING_SNAKE_CASE     =      os.path.join(__SCREAMING_SNAKE_CASE					,							"""generator_tokenizer"""			)
      self.question_encoder.save_pretrained(__SCREAMING_SNAKE_CASE			)
      self.generator.save_pretrained(__SCREAMING_SNAKE_CASE			)
 @classmethod
 def       UpperCAmelCase__		(		cls					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							**__SCREAMING_SNAKE_CASE					:       int			)  -> List[Any]:


      """simple docstring"""




      from ..auto.tokenization_auto import AutoTokenizer

      __SCREAMING_SNAKE_CASE     =      kwargs.pop("""config"""					,							__SCREAMING_SNAKE_CASE			)

      if config is None:
           __SCREAMING_SNAKE_CASE     =      RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE			)

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


      """simple docstring"""




      return self.current_tokenizer(*__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       List[Any]					,							*__SCREAMING_SNAKE_CASE					:       List[Any]					,							**__SCREAMING_SNAKE_CASE					:       str			)  -> List[str]:


      """simple docstring"""




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


      """simple docstring"""




      return self.generator.decode(*__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.question_encoder
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.generator


 def       UpperCAmelCase__		(		self					:       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					,							)  -> BatchEncoding:


      """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"""					,							__SCREAMING_SNAKE_CASE					,							)
      if max_length is None:
           __SCREAMING_SNAKE_CASE     =      self.current_tokenizer.model_max_length
      __SCREAMING_SNAKE_CASE     =      self(
          __SCREAMING_SNAKE_CASE					,							add_special_tokens=__SCREAMING_SNAKE_CASE					,							return_tensors=__SCREAMING_SNAKE_CASE					,							max_length=__SCREAMING_SNAKE_CASE					,							padding=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE					,							)
      if tgt_texts is None:
           return model_inputs
      # Process tgt_texts
      if max_target_length is None:
           __SCREAMING_SNAKE_CASE     =      self.current_tokenizer.model_max_length
      __SCREAMING_SNAKE_CASE     =      self(
          text_target=__SCREAMING_SNAKE_CASE					,							add_special_tokens=__SCREAMING_SNAKE_CASE					,							return_tensors=__SCREAMING_SNAKE_CASE					,							padding=__SCREAMING_SNAKE_CASE					,							max_length=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE					,							)
      __SCREAMING_SNAKE_CASE     =      labels["""input_ids"""]
      return model_inputs









'''simple docstring'''



class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       int			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      n
      __SCREAMING_SNAKE_CASE     =      [None] * self.n
      __SCREAMING_SNAKE_CASE     =      0  # index of the first element
      __SCREAMING_SNAKE_CASE     =      0
      __SCREAMING_SNAKE_CASE     =      0
 def __len__(		self					:       Tuple			)  -> int:


      """simple docstring"""




      return self.size
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> bool:


      """simple docstring"""




      return self.size == 0
 def       UpperCAmelCase__		(		self					:       Any			)  -> int:


      """simple docstring"""




      return False if self.is_empty() else self.array[self.front]
 def       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]			)  -> Dict:


      """simple docstring"""




      if self.size >= self.n:
           raise Exception("""QUEUE IS FULL"""			)

      __SCREAMING_SNAKE_CASE     =      data
      __SCREAMING_SNAKE_CASE     =      (self.rear + 1) % self.n
      self.size += 1
      return self


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


      """simple docstring"""




      if self.size == 0:
           raise Exception("""UNDERFLOW"""			)

      __SCREAMING_SNAKE_CASE     =      self.array[self.front]
      __SCREAMING_SNAKE_CASE     =      None
      __SCREAMING_SNAKE_CASE     =      (self.front + 1) % self.n
      self.size -= 1
      return temp






