





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       :     Dict   =     logging.get_logger(__name__)



class 					A__   :





      def __init__( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   question_encoder
       UpperCamelCase	:					int       	=   generator
       UpperCamelCase	:					Optional[int]       	=   self.question_encoder





      def        __UpperCamelCase( self       ,							A_   ):
       '''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_   )
       UpperCamelCase	:					List[Any]       	=   os.path.join(A_       ,							"question_encoder_tokenizer"   )
       UpperCamelCase	:					Union[str, Any]       	=   os.path.join(A_       ,							"generator_tokenizer"   )
       self.question_encoder.save_pretrained(A_   )
       self.generator.save_pretrained(A_   )





      @classmethod
      def        __UpperCamelCase( cls       ,							A_       ,							**A_   ):
       '''simple docstring'''




       from ..auto.tokenization_auto import AutoTokenizer

       UpperCamelCase	:					str       	=   kwargs.pop("config"       ,							A_   )

       if config is None:
        UpperCamelCase	:					List[Any]       	=   RagConfig.from_pretrained(A_   )

       UpperCamelCase	:					Tuple       	=   AutoTokenizer.from_pretrained(
           A_       ,							config=config.question_encoder       ,							subfolder="question_encoder_tokenizer"   )
       UpperCamelCase	:					str       	=   AutoTokenizer.from_pretrained(
           A_       ,							config=config.generator       ,							subfolder="generator_tokenizer"   )
       return cls(question_encoder=A_       ,							generator=A_   )





      def __call__( self       ,							*A_       ,							**A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							*A_       ,							**A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							*A_       ,							**A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   self.question_encoder





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.generator





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = "longest"       ,							A_ = None       ,							A_ = True       ,							**A_       ,							):
       '''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:
        UpperCamelCase	:					str       	=   self.current_tokenizer.model_max_length
       UpperCamelCase	:					Dict       	=   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:
        UpperCamelCase	:					Optional[int]       	=   self.current_tokenizer.model_max_length
       UpperCamelCase	:					str       	=   self(
           text_target=A_       ,							add_special_tokens=A_       ,							return_tensors=A_       ,							padding=A_       ,							max_length=A_       ,							truncation=A_       ,							**A_       ,							)
       UpperCamelCase	:					List[str]       	=   labels["input_ids"]
       return model_inputs




def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[Any]				,							UpperCamelCase__     :  Union[str, Any]						):
       # "extended trapezoidal rule"
       # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
       _A      :       int				    = (boundary[1] - boundary[0]) / steps
       _A      :       Any				    = boundary[0]
       _A      :       List[Any]				    = boundary[1]
       _A      :       str				    = make_points(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__						)
       _A      :       str				    = 0.0
       y += (h / 2.0) * f(UpperCamelCase__						)
       for i in x_i:
              # print(i)
              y += h * f(UpperCamelCase__						)
       y += (h / 2.0) * f(UpperCamelCase__						)
       return y
def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  Optional[int]				,							UpperCamelCase__     :  Any						):
       _A      :       Optional[int]				    = a + h
       while x < (b - h):
              yield x
              _A      :       Dict				    = x + h
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[int]						):  # enter your function here
       _A      :       Any				    = (x - 0) * (x - 0)
       return y
def 							_UpperCAmelCase				():
       _A      :       Optional[Any]				    = 0.0  # Lower bound of integration
       _A      :       Optional[int]				    = 1.0  # Upper bound of integration
       _A      :       List[Any]				    = 10.0  # define number of steps or resolution
       _A      :       Any				    = [a, b]  # define boundary of integration
       _A      :       Tuple				    = method_a(UpperCamelCase__				,							UpperCamelCase__						)
       print(f"y = {y}"						)


if __name__ == "__main__":
    main()
