




'''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_						:      str       =						logging.get_logger(__name__)

class        _a    :






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      List[str]:
          _snake_case							=				question_encoder
          _snake_case							=				generator
          _snake_case							=				self.question_encoder






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Dict:
          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     _lowercase     (			cls ,_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      Optional[Any]:
          # 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 ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      Tuple:
          return self.current_tokenizer(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      Dict:
          return self.generator.batch_decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return self.generator.decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      List[Any]:
          _snake_case							=				self.question_encoder






     def     _lowercase     (			self   )							->      List[str]:
          _snake_case							=				self.generator






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = "longest" ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = True ,**_SCREAMING_SNAKE_CASE ,)							->      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








'''simple docstring'''





from __future__ import annotations

from collections.abc import Callable





def      __a     (       _UpperCamelCase:   Callable[[int | float], int | float] ,						_UpperCamelCase:   int | float ,						_UpperCamelCase:   int | float ,						_UpperCamelCase:   int = 100 ,						)						->			float:

     """simple docstring"""




     _snake_case							=				x_start
     _snake_case							=				fnc(_UpperCamelCase   )
     _snake_case							=				0.0
     for _ in range(_UpperCamelCase   ):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          _snake_case							=				(x_end - x_start) / steps + xa
          _snake_case							=				fnc(_UpperCamelCase   )
          area += abs(fxa + fxa   ) * (xa - xa) / 2
          # Increment step
          _snake_case							=				xa
          _snake_case							=				fxa
     return area


if __name__ == "__main__":





   def      __a     (       _UpperCamelCase:   Any   )						->			Optional[int]:

        """simple docstring"""




        return x**3 + x**2

   print('''f(x) = x^3 + x^2''')
   print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
   UpperCamelCase_						:      Optional[int]       =						10
   while i <= 100000:
      print(F'with {i} steps: {trapezoidal_area(f, -5, 5, i)}')
      i *= 10


