


"""simple docstring"""







from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


a__				:							Dict						=     logging.get_logger(__name__)




class  UpperCamelCase_	( _SCREAMING_SNAKE_CASE):

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				str			,			UpperCAmelCase__			:				Union[str, Any]   )       ->	int:
						if isinstance(A_			,			A_   ):
											__SCREAMING_SNAKE_CASE						   =				[label.strip() for label in labels.split(","   ) if label.strip()]
						return labels




	def __call__( self			:				Union[str, Any]			,			UpperCAmelCase__			:				Tuple			,			UpperCAmelCase__			:				Optional[Any]			,			UpperCAmelCase__			:				List[str]   )       ->	List[Any]:
						if len(A_   ) == 0 or len(A_   ) == 0:
											raise ValueError("You must include at least one label and at least one sequence."   )
						if hypothesis_template.format(labels[0]   ) == hypothesis_template:
											raise ValueError(
											    (
											        "The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. "
											        "Make sure the passed template includes formatting syntax such as {{}} where the label should go."
											    ).format(A_   )   )

						if isinstance(A_			,			A_   ):
											__SCREAMING_SNAKE_CASE						   =				[sequences]

						__SCREAMING_SNAKE_CASE						   =				[]
						for sequence in sequences:
											sequence_pairs.extend([[sequence, hypothesis_template.format(A_   )] for label in labels]   )

						return sequence_pairs, sequences




@add_end_docstrings(_SCREAMING_SNAKE_CASE)
class  UpperCamelCase_	( _SCREAMING_SNAKE_CASE):

	"""simple docstring"""





	def __init__( self			:				List[str]			,			UpperCAmelCase__			:				List[Any]=ZeroShotClassificationArgumentHandler()			,			*UpperCAmelCase__			:				int			,			**UpperCAmelCase__			:				Any   )       ->	Optional[Any]:
						__SCREAMING_SNAKE_CASE						   =				args_parser
						super().__init__(*A_			,			**A_   )
						if self.entailment_id == -1:
											logger.warning(
											    "Failed to determine \'entailment\' label id from the label2id mapping in the model config. Setting to "
											    "-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."   )




	@property
	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	Union[str, Any]:
						for label, ind in self.model.config.labelaid.items():
											if label.lower().startswith("entail"   ):
																return ind
						return -1




	def   UpperCAmelCase_      ( self			:				str			,			UpperCAmelCase__			:				Tuple			,			UpperCAmelCase__			:				Union[str, Any]=True			,			UpperCAmelCase__			:				Dict=True			,			UpperCAmelCase__			:				Tuple=TruncationStrategy.ONLY_FIRST			,			**UpperCAmelCase__			:				int   )       ->	Any:
						__SCREAMING_SNAKE_CASE						   =				self.framework
						if self.tokenizer.pad_token is None:
											# Override for tokenizers not supporting padding
											logger.error(
											    "Tokenizer was not supporting padding necessary for zero-shot, attempting to use "
											    " `pad_token=eos_token`"   )
											__SCREAMING_SNAKE_CASE						   =				self.tokenizer.eos_token
						try:
											__SCREAMING_SNAKE_CASE						   =				self.tokenizer(
											    A_			,			add_special_tokens=A_			,			return_tensors=A_			,			padding=A_			,			truncation=A_			,			)
						except Exception as e:
											if "too short" in str(A_   ):
																# tokenizers might yell that we want to truncate
																# to a value that is not even reached by the input.
																# In that case we don't want to truncate.
																# It seems there's not a really better way to catch that
																# exception.

																__SCREAMING_SNAKE_CASE						   =				self.tokenizer(
																    A_			,			add_special_tokens=A_			,			return_tensors=A_			,			padding=A_			,			truncation=TruncationStrategy.DO_NOT_TRUNCATE			,			)
											else:
																raise e

						return inputs




	def   UpperCAmelCase_      ( self			:				List[Any]			,			**UpperCAmelCase__			:				Any   )       ->	Tuple:
						if kwargs.get("multi_class"			,			A_   ) is not None:
											__SCREAMING_SNAKE_CASE						   =				kwargs["multi_class"]
											logger.warning(
											    "The `multi_class` argument has been deprecated and renamed to `multi_label`. "
											    "`multi_class` will be removed in a future version of Transformers."   )
						__SCREAMING_SNAKE_CASE						   =				{}
						if "candidate_labels" in kwargs:
											__SCREAMING_SNAKE_CASE						   =				self._args_parser._parse_labels(kwargs["candidate_labels"]   )
						if "hypothesis_template" in kwargs:
											__SCREAMING_SNAKE_CASE						   =				kwargs["hypothesis_template"]

						__SCREAMING_SNAKE_CASE						   =				{}
						if "multi_label" in kwargs:
											__SCREAMING_SNAKE_CASE						   =				kwargs["multi_label"]
						return preprocess_params, {}, postprocess_params




	def __call__( self			:				int			,			UpperCAmelCase__			:				Tuple			,			*UpperCAmelCase__			:				Any			,			**UpperCAmelCase__			:				Any			,			)       ->	Tuple:
						if len(A_   ) == 0:
											pass
						elif len(A_   ) == 1 and "candidate_labels" not in kwargs:
											__SCREAMING_SNAKE_CASE						   =				args[0]
						else:
											raise ValueError(F"""Unable to understand extra arguments {args}"""   )

						return super().__call__(A_			,			**A_   )




	def   UpperCAmelCase_      ( self			:				Optional[Any]			,			UpperCAmelCase__			:				Dict			,			UpperCAmelCase__			:				int=None			,			UpperCAmelCase__			:				Optional[int]="This example is {}."   )       ->	Dict:
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				self._args_parser(A_			,			A_			,			A_   )

						for i, (candidate_label, sequence_pair) in enumerate(zip(A_			,			A_   )   ):
											__SCREAMING_SNAKE_CASE						   =				self._parse_and_tokenize([sequence_pair]   )

											yield {
											    "candidate_label": candidate_label,
											    "sequence": sequences[0],
											    "is_last": i == len(A_   ) - 1,
											    **model_input,
											}




	def   UpperCAmelCase_      ( self			:				Tuple			,			UpperCAmelCase__			:				int   )       ->	Optional[Any]:
						__SCREAMING_SNAKE_CASE						   =				inputs["candidate_label"]
						__SCREAMING_SNAKE_CASE						   =				inputs["sequence"]
						__SCREAMING_SNAKE_CASE						   =				{k: inputs[k] for k in self.tokenizer.model_input_names}
						__SCREAMING_SNAKE_CASE						   =				self.model(**A_   )

						__SCREAMING_SNAKE_CASE						   =				{
						    "candidate_label": candidate_label,
						    "sequence": sequence,
						    "is_last": inputs["is_last"],
						    **outputs,
						}
						return model_outputs




	def   UpperCAmelCase_      ( self			:				Union[str, Any]			,			UpperCAmelCase__			:				int			,			UpperCAmelCase__			:				Union[str, Any]=False   )       ->	Union[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				[outputs["candidate_label"] for outputs in model_outputs]
						__SCREAMING_SNAKE_CASE						   =				[outputs["sequence"] for outputs in model_outputs]
						__SCREAMING_SNAKE_CASE						   =				np.concatenate([output["logits"].numpy() for output in model_outputs]   )
						__SCREAMING_SNAKE_CASE						   =				logits.shape[0]
						__SCREAMING_SNAKE_CASE						   =				len(A_   )
						__SCREAMING_SNAKE_CASE						   =				N // n
						__SCREAMING_SNAKE_CASE						   =				logits.reshape((num_sequences, n, -1)   )

						if multi_label or len(A_   ) == 1:
											# softmax over the entailment vs. contradiction dim for each label independently
											__SCREAMING_SNAKE_CASE						   =				self.entailment_id
											__SCREAMING_SNAKE_CASE						   =				-1 if entailment_id == 0 else 0
											__SCREAMING_SNAKE_CASE						   =				reshaped_outputs[..., [contradiction_id, entailment_id]]
											__SCREAMING_SNAKE_CASE						   =				np.exp(A_   ) / np.exp(A_   ).sum(-1			,			keepdims=A_   )
											__SCREAMING_SNAKE_CASE						   =				scores[..., 1]
						else:
											# softmax the "entailment" logits over all candidate labels
											__SCREAMING_SNAKE_CASE						   =				reshaped_outputs[..., self.entailment_id]
											__SCREAMING_SNAKE_CASE						   =				np.exp(A_   ) / np.exp(A_   ).sum(-1			,			keepdims=A_   )

						__SCREAMING_SNAKE_CASE						   =				list(reversed(scores[0].argsort()   )   )
						return {
						    "sequence": sequences[0],
						    "labels": [candidate_labels[i] for i in top_inds],
						    "scores": scores[0, top_inds].tolist(),
						}


def       A       (					lowercase )   ->  str:
		'''simple docstring'''

		return " ".join(
		    ''.join(word[::-1] ) if len(lowercase ) > 4 else word for word in sentence.split() )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(reverse_long_words("Hey wollef sroirraw"))
