




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


_lowerCamelCase			:	int							 =     logging.get_logger(__name__)


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    def  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			Union[str, Any])		->Union[str, Any]:





           '''simple docstring'''

           if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__):
                  A__   				=					[label.strip() for label in labels.split(''',''') if label.strip()]
           return labels





    def __call__(    self   :			List[Any]      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			int)		->Tuple:





           '''simple docstring'''

           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__):
                  A__   				=					[sequences]

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

           return sequence_pairs, sequences


@add_end_docstrings(UpperCAmelCase__  )
class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    def __init__(    self   :			Dict      ,				UpperCAmelCase__   :			List[str]=ZeroShotClassificationArgumentHandler()      ,				*UpperCAmelCase__   :			Dict      ,				**UpperCAmelCase__   :			Optional[Any])		->Tuple:





           '''simple docstring'''

           A__   				=					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  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Dict:





           '''simple docstring'''

           for label, ind in self.model.config.labelaid.items():
                  if label.lower().startswith('''entail'''):
                         return ind
           return -1


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			Dict=True      ,				UpperCAmelCase__   :			Any=True      ,				UpperCAmelCase__   :			Optional[Any]=TruncationStrategy.ONLY_FIRST      ,				**UpperCAmelCase__   :			Dict)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					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`''')
                  A__   				=					self.tokenizer.eos_token
           try:
                  A__   				=					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.

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

           return inputs


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				**UpperCAmelCase__   :			Union[str, Any])		->Any:





           '''simple docstring'''

           if kwargs.get('''multi_class'''      ,				UpperCAmelCase__) is not None:
                  A__   				=					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.''')
           A__   				=					{}
           if "candidate_labels" in kwargs:
                  A__   				=					self._args_parser._parse_labels(kwargs['''candidate_labels'''])
           if "hypothesis_template" in kwargs:
                  A__   				=					kwargs['''hypothesis_template''']

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


    def __call__(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Union[str, List[str]]      ,				*UpperCAmelCase__   :			Dict      ,				**UpperCAmelCase__   :			int      ,				)		->Tuple:





           '''simple docstring'''

           if len(UpperCAmelCase__) == 0:
                  pass
           elif len(UpperCAmelCase__) == 1 and "candidate_labels" not in kwargs:
                  A__   				=					args[0]
           else:
                  raise ValueError(f"""Unable to understand extra arguments {args}""")

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


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Tuple=None      ,				UpperCAmelCase__   :			List[Any]="This example is {}.")		->Tuple:





           '''simple docstring'''

           A__				,							A__   				=					self._args_parser(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__)

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

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


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				UpperCAmelCase__   :			Optional[int])		->Any:





           '''simple docstring'''

           A__   				=					inputs['''candidate_label''']
           A__   				=					inputs['''sequence''']
           A__   				=					{k: inputs[k] for k in self.tokenizer.model_input_names}
           A__   				=					self.model(**UpperCAmelCase__)

           A__   				=					{
               '''candidate_label''': candidate_label,
               '''sequence''': sequence,
               '''is_last''': inputs['''is_last'''],
               **outputs,
           }
           return model_outputs





    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Optional[int]=False)		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					[outputs['''candidate_label'''] for outputs in model_outputs]
           A__   				=					[outputs['''sequence'''] for outputs in model_outputs]
           A__   				=					np.concatenate([output['''logits'''].numpy() for output in model_outputs])
           A__   				=					logits.shape[0]
           A__   				=					len(UpperCAmelCase__)
           A__   				=					N // n
           A__   				=					logits.reshape((num_sequences, n, -1))

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

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








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer


@require_torch
@require_sentencepiece
@require_tokenizers
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Any:





           '''simple docstring'''

           A__   				=					AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''      ,				return_dict=UpperCAmelCase__).to(UpperCAmelCase__)
           A__   				=					AutoTokenizer.from_pretrained('''google/mt5-small''')

           A__   				=					tokenizer('''Hello there'''      ,				return_tensors='''pt''').input_ids
           A__   				=					tokenizer('''Hi I am'''      ,				return_tensors='''pt''').input_ids

           A__   				=					model(input_ids.to(UpperCAmelCase__)      ,				labels=labels.to(UpperCAmelCase__)).loss
           A__   				=					-(labels.shape[-1] * loss.item())

           A__   				=					-84.9127
           self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)


