

from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



class 						_A     (				snake_case				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = False   ,SCREAMING_SNAKE_CASE_ = False   ,SCREAMING_SNAKE_CASE_ = None   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      super().__init__(
          SCREAMING_SNAKE_CASE_   ,split=SCREAMING_SNAKE_CASE_   ,features=SCREAMING_SNAKE_CASE_   ,cache_dir=SCREAMING_SNAKE_CASE_   ,keep_in_memory=SCREAMING_SNAKE_CASE_   ,streaming=SCREAMING_SNAKE_CASE_   ,num_proc=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_   ,)
      snake_case							:		Any        =		path_or_paths if isinstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ) else {self.split: path_or_paths}
      snake_case							:		str        =		Text(
          cache_dir=SCREAMING_SNAKE_CASE_   ,data_files=SCREAMING_SNAKE_CASE_   ,features=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_   ,)





  def    snake_case_       (  self    ):

      '''simple docstring'''
      # Build iterable dataset
      if self.streaming:
          snake_case							:		List[str]        =		self.builder.as_streaming_dataset(split=self.split    )
      # Build regular (map-style) dataset
      else:
          snake_case							:		int        =		None
          snake_case							:		Tuple        =		None
          snake_case							:		str        =		None
          snake_case							:		List[str]        =		None

          self.builder.download_and_prepare(
              download_config=SCREAMING_SNAKE_CASE_   ,download_mode=SCREAMING_SNAKE_CASE_   ,verification_mode=SCREAMING_SNAKE_CASE_   ,base_path=SCREAMING_SNAKE_CASE_   ,num_proc=self.num_proc   ,)
          snake_case							:		Any        =		self.builder.as_dataset(
              split=self.split   ,verification_mode=SCREAMING_SNAKE_CASE_   ,in_memory=self.keep_in_memory    )
      return dataset







import numpy as np

from transformers import Pipeline





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]    )			-> Optional[int]:





	"""simple docstring"""







	A             =					np.max(UpperCamelCase__				,       axis=-1				,       keepdims=UpperCamelCase__    )
	A             =					np.exp(outputs - maxes    )
	return shifted_exp / shifted_exp.sum(axis=-1				,       keepdims=UpperCamelCase__    )






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def   _UpperCAmelCase       (    self					, **a__ )						->					Union[str, Any]:
					A             =					{}
					if "second_text" in kwargs:
						A             =					kwargs["""second_text"""]
					return preprocess_kwargs, {}, {}

				def   _UpperCAmelCase       (    self					, a__					, a__=None )						->					str:
					return self.tokenizer(a__					, text_pair=a__					, return_tensors=self.framework )

				def   _UpperCAmelCase       (    self					, a__ )						->					Tuple:
					return self.model(**a__ )

				def   _UpperCAmelCase       (    self					, a__ )						->					Optional[int]:
					A             =					model_outputs.logits[0].numpy()
					A             =					softmax(a__ )

					A             =					np.argmax(a__ )
					A             =					self.model.config.idalabel[best_class]
					A             =					probabilities[best_class].item()
					A             =					logits.tolist()
					return {"label": label, "score": score, "logits": logits}



