


'''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


_a   :   Tuple												=   logging.get_logger(__name__)




class 			_lowercase :
   def __init__(						self       :      int       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]      ) ->							List[str]:
        __snake_case           =						question_encoder
        __snake_case           =						generator
        __snake_case           =						self.question_encoder
   def a  (						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]      ) ->							int:
        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_      )
        __snake_case           =						os.path.join(SCREAMING_SNAKE_CASE_       ,   'question_encoder_tokenizer'      )
        __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 a  (						cls       :      Any       ,   SCREAMING_SNAKE_CASE_       :      str       ,   **SCREAMING_SNAKE_CASE_       :      Optional[Any]      ) ->							str:
        # dynamically import AutoTokenizer
        from ..auto.tokenization_auto import AutoTokenizer

        __snake_case           =						kwargs.pop('config'       ,   SCREAMING_SNAKE_CASE_      )

        if config is None:
             __snake_case           =						RagConfig.from_pretrained(SCREAMING_SNAKE_CASE_      )

        __snake_case           =						AutoTokenizer.from_pretrained(
            SCREAMING_SNAKE_CASE_       ,   config=config.question_encoder       ,   subfolder='question_encoder_tokenizer'      )
        __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       :      str       ,   *SCREAMING_SNAKE_CASE_       :      Dict       ,   **SCREAMING_SNAKE_CASE_       :      Any      ) ->							str:
        return self.current_tokenizer(*SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Any       ,   *SCREAMING_SNAKE_CASE_       :      Tuple       ,   **SCREAMING_SNAKE_CASE_       :      Optional[int]      ) ->							Union[str, Any]:
        return self.generator.batch_decode(*SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      List[str]       ,   *SCREAMING_SNAKE_CASE_       :      str       ,   **SCREAMING_SNAKE_CASE_       :      Dict      ) ->							Dict:
        return self.generator.decode(*SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Union[str, Any]      ) ->							Tuple:
        __snake_case           =						self.question_encoder
   def a  (						self       :      Optional[int]      ) ->							List[str]:
        __snake_case           =						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_       :      List[str]       ,   ) ->							BatchEncoding:
        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:
             __snake_case           =						self.current_tokenizer.model_max_length
        __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:
             __snake_case           =						self.current_tokenizer.model_max_length
        __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_       ,   )
        __snake_case           =						labels['input_ids']
        return model_inputs












from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate




@dataclass(frozen=snake_case_							)
class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					field(default='''language-modeling'''			,   metadata={'''include_in_asdict_even_if_is_default''': True}							)

	lowerCAmelCase						=					Features({'''text''': Value('''string'''							)}							)
	lowerCAmelCase						=					Features({}							)
	lowerCAmelCase						=					"text"




	@property
	def 				_UpperCamelCase     (			self   )			->       Dict[str, str]:
								return {self.text_column: "text"}





