






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


snake_case    				=		logging.get_logger(__name__)


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def 				_A       (				self       :	Tuple					, UpperCAmelCase_       :	int      ):
       if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      Dict							=    [label.strip() for label in labels.split(","      ) if label.strip()]
       return labels






     def __call__(				self       :	Any					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	List[Any]      ):
       if len(UpperCAmelCase_      ) == 0 or len(UpperCAmelCase_      ) == 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(UpperCAmelCase_      )      )

       if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      List[str]							=    [sequences]

       SCREAMING_SNAKE_CASE       :      List[Any]							=    []
       for sequence in sequences:
         sequence_pairs.extend([[sequence, hypothesis_template.format(UpperCAmelCase_      )] for label in labels]      )

       return sequence_pairs, sequences





@add_end_docstrings(lowerCAmelCase					)
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def __init__(				self       :	Optional[int]					, UpperCAmelCase_       :	Dict=ZeroShotClassificationArgumentHandler()					, *UpperCAmelCase_       :	Optional[int]					, **UpperCAmelCase_       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Any							=    args_parser
       super().__init__(*UpperCAmelCase_					, **UpperCAmelCase_      )
       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 				_A       (				self       :	Any      ):
       for label, ind in self.model.config.labelaid.items():
         if label.lower().startswith("entail"      ):
           return ind
       return -1


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	int=True					, UpperCAmelCase_       :	List[str]=True					, UpperCAmelCase_       :	Tuple=TruncationStrategy.ONLY_FIRST					, **UpperCAmelCase_       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    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       :      Optional[int]							=    self.tokenizer.eos_token
       try:
         SCREAMING_SNAKE_CASE       :      Dict							=    self.tokenizer(
             UpperCAmelCase_					, add_special_tokens=UpperCAmelCase_					, return_tensors=UpperCAmelCase_					, padding=UpperCAmelCase_					, truncation=UpperCAmelCase_					, )
       except Exception as e:
         if "too short" in str(UpperCAmelCase_      ):
           # 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       :      List[Any]							=    self.tokenizer(
               UpperCAmelCase_					, add_special_tokens=UpperCAmelCase_					, return_tensors=UpperCAmelCase_					, padding=UpperCAmelCase_					, truncation=TruncationStrategy.DO_NOT_TRUNCATE					, )
         else:
           raise e

       return inputs


     def 				_A       (				self       :	List[Any]					, **UpperCAmelCase_       :	Union[str, Any]      ):
       if kwargs.get("multi_class"					, UpperCAmelCase_      ) is not None:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    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       :      Any							=    {}
       if "candidate_labels" in kwargs:
         SCREAMING_SNAKE_CASE       :      Tuple							=    self._args_parser._parse_labels(kwargs["candidate_labels"]      )
       if "hypothesis_template" in kwargs:
         SCREAMING_SNAKE_CASE       :      List[str]							=    kwargs["hypothesis_template"]

       SCREAMING_SNAKE_CASE       :      int							=    {}
       if "multi_label" in kwargs:
         SCREAMING_SNAKE_CASE       :      str							=    kwargs["multi_label"]
       return preprocess_params, {}, postprocess_params


     def __call__(				self       :	Dict					, UpperCAmelCase_       :	Union[str, List[str]]					, *UpperCAmelCase_       :	Optional[Any]					, **UpperCAmelCase_       :	Tuple					, ):
       if len(UpperCAmelCase_      ) == 0:
         pass
       elif len(UpperCAmelCase_      ) == 1 and "candidate_labels" not in kwargs:
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    args[0]
       else:
         raise ValueError(f'''Unable to understand extra arguments {args}'''      )

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


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	str=None					, UpperCAmelCase_       :	str="This example is {}."      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      str							=    self._args_parser(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_      )

       for i, (candidate_label, sequence_pair) in enumerate(zip(UpperCAmelCase_					, UpperCAmelCase_      )      ):
         SCREAMING_SNAKE_CASE       :      int							=    self._parse_and_tokenize([sequence_pair]      )

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


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    inputs["candidate_label"]
       SCREAMING_SNAKE_CASE       :      Any							=    inputs["sequence"]
       SCREAMING_SNAKE_CASE       :      str							=    {k: inputs[k] for k in self.tokenizer.model_input_names}
       SCREAMING_SNAKE_CASE       :      Dict							=    self.model(**UpperCAmelCase_      )

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






     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	List[str]=False      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    [outputs["candidate_label"] for outputs in model_outputs]
       SCREAMING_SNAKE_CASE       :      Tuple							=    [outputs["sequence"] for outputs in model_outputs]
       SCREAMING_SNAKE_CASE       :      List[Any]							=    np.concatenate([output["logits"].numpy() for output in model_outputs]      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    logits.shape[0]
       SCREAMING_SNAKE_CASE       :      Dict							=    len(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    N // n
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    logits.reshape((num_sequences, n, -1)      )

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

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














from ..utils import DummyObject, requires_backends


class  SCREAMING_SNAKE_CASE			(			metaclass=lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Union[str, Any]			=	['''keras_nlp''']


     def __init__(				self       :	Dict					, *UpperCAmelCase_       :	Optional[Any]					, **UpperCAmelCase_       :	Dict      ):
       requires_backends(self					, ["keras_nlp"]      )






