






'''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__			=      logging.get_logger(__name__)


class 							_UpperCAmelCase    :




    def __init__(      self	:							Tuple	,  a	:							Any	,  a	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  question_encoder
       lowercase_						:     List[Any]							     =  generator
       lowercase_						:     Union[str, Any]							     =  self.question_encoder




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]	,  a	:							Union[str, Any]      ):






       '''simple docstring'''






       if os.path.isfile(a      ):
          raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""      )
       os.makedirs(a	,  exist_ok=a      )
       lowercase_						:     Tuple							     =  os.path.join(a	,  "question_encoder_tokenizer"      )
       lowercase_						:     Dict							     =  os.path.join(a	,  "generator_tokenizer"      )
       self.question_encoder.save_pretrained(a      )
       self.generator.save_pretrained(a      )




    @classmethod
    def 					lowerCAmelCase__					(      cls	:							Optional[int]	,  a	:							Optional[Any]	,  **a	:							List[Any]      ):






       '''simple docstring'''






       from ..auto.tokenization_auto import AutoTokenizer

       lowercase_						:     Any							     =  kwargs.pop("config"	,  a      )

       if config is None:
          lowercase_						:     int							     =  RagConfig.from_pretrained(a      )

       lowercase_						:     Optional[Any]							     =  AutoTokenizer.from_pretrained(
           a	,  config=config.question_encoder	,  subfolder="question_encoder_tokenizer"      )
       lowercase_						:     str							     =  AutoTokenizer.from_pretrained(
           a	,  config=config.generator	,  subfolder="generator_tokenizer"      )
       return cls(question_encoder=a	,  generator=a      )




    def __call__(      self	:							str	,  *a	:							Optional[int]	,  **a	:							Optional[int]      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Optional[Any]	,  *a	:							str	,  **a	:							Any      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Tuple	,  *a	:							Union[str, Any]	,  **a	:							str      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  self.question_encoder




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  self.generator





    def 					lowerCAmelCase__					(      self	:							int	,  a	:							List[str]	,  a	:							Optional[List[str]] = None	,  a	:							Optional[int] = None	,  a	:							Optional[int] = None	,  a	:							str = "longest"	,  a	:							str = None	,  a	:							bool = True	,  **a	:							Dict	,  ):






       '''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"	,  a	,  )
       if max_length is None:
          lowercase_						:     Optional[Any]							     =  self.current_tokenizer.model_max_length
       lowercase_						:     Union[str, Any]							     =  self(
           a	,  add_special_tokens=a	,  return_tensors=a	,  max_length=a	,  padding=a	,  truncation=a	,  **a	,  )
       if tgt_texts is None:
          return model_inputs
       # Process tgt_texts
       if max_target_length is None:
          lowercase_						:     Any							     =  self.current_tokenizer.model_max_length
       lowercase_						:     Any							     =  self(
           text_target=a	,  add_special_tokens=a	,  return_tensors=a	,  padding=a	,  max_length=a	,  truncation=a	,  **a	,  )
       lowercase_						:     Dict							     =  labels["input_ids"]
       return model_inputs












'''simple docstring'''
UpperCamelCase__			=      {
    "joule": 1.0,
    "kilojoule": 1000,
    "megajoule": 1000000,
    "gigajoule": 1000000000,
    "wattsecond": 1.0,
    "watthour": 3600,
    "kilowatthour": 3600000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4186.8,
    "kilocalorie_nutr": 4186800.00,
    "electronvolt": 1.602176634e-19,
    "britishthermalunit_it": 1055.05585,
    "footpound": 1.35_58_18,
}




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
      lowercase_						:     Union[str, Any]							     =  (
          F"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
          F"""Valid values are: {", ".join(_UpperCamelCase				)}"""
      )
      raise ValueError(_UpperCamelCase				)
   return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




