






import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__A					=	{"LayoutLMv2Config", "LayoutLMv3Config"}



@is_pipeline_test
class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    lowerCamelCase	:							List[str]				= MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
    lowerCamelCase	:							Optional[int]				= TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

    if model_mapping is not None:
        lowerCamelCase	:							Any				= {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
    if tf_model_mapping is not None:
        lowerCamelCase	:							int				= {
            config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
        }



    def _a							(				self      :     str ,     __SCREAMING_SNAKE_CASE      :     str ,     __SCREAMING_SNAKE_CASE      :     Dict ,     __SCREAMING_SNAKE_CASE      :     str      )      ->							str:
           __UpperCAmelCase     				=ZeroShotClassificationPipeline(
               model=__SCREAMING_SNAKE_CASE ,     tokenizer=__SCREAMING_SNAKE_CASE ,     candidate_labels=["""polics""", """health"""]      )
           return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]



    def _a							(				self      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]      )      ->							Optional[Any]:
           __UpperCAmelCase     				=classifier("""Who are you voting for in 2020?""" ,     candidate_labels="""politics"""      )
           self.assertEqual(__SCREAMING_SNAKE_CASE ,     {"""sequence""": ANY(__SCREAMING_SNAKE_CASE      ), """labels""": [ANY(__SCREAMING_SNAKE_CASE      )], """scores""": [ANY(__SCREAMING_SNAKE_CASE      )]}      )

           # No kwarg
           __UpperCAmelCase     				=classifier("""Who are you voting for in 2020?""" ,     ["""politics"""]      )
           self.assertEqual(__SCREAMING_SNAKE_CASE ,     {"""sequence""": ANY(__SCREAMING_SNAKE_CASE      ), """labels""": [ANY(__SCREAMING_SNAKE_CASE      )], """scores""": [ANY(__SCREAMING_SNAKE_CASE      )]}      )

           __UpperCAmelCase     				=classifier("""Who are you voting for in 2020?""" ,     candidate_labels=["""politics"""]      )
           self.assertEqual(__SCREAMING_SNAKE_CASE ,     {"""sequence""": ANY(__SCREAMING_SNAKE_CASE      ), """labels""": [ANY(__SCREAMING_SNAKE_CASE      )], """scores""": [ANY(__SCREAMING_SNAKE_CASE      )]}      )

           __UpperCAmelCase     				=classifier("""Who are you voting for in 2020?""" ,     candidate_labels="""politics, public health"""      )
           self.assertEqual(
               __SCREAMING_SNAKE_CASE ,     {"""sequence""": ANY(__SCREAMING_SNAKE_CASE      ), """labels""": [ANY(__SCREAMING_SNAKE_CASE      ), ANY(__SCREAMING_SNAKE_CASE      )], """scores""": [ANY(__SCREAMING_SNAKE_CASE      ), ANY(__SCREAMING_SNAKE_CASE      )]}      )
           self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]      )      ) ,     1.0      )

           __UpperCAmelCase     				=classifier("""Who are you voting for in 2020?""" ,     candidate_labels=["""politics""", """public health"""]      )
           self.assertEqual(
               __SCREAMING_SNAKE_CASE ,     {"""sequence""": ANY(__SCREAMING_SNAKE_CASE      ), """labels""": [ANY(__SCREAMING_SNAKE_CASE      ), ANY(__SCREAMING_SNAKE_CASE      )], """scores""": [ANY(__SCREAMING_SNAKE_CASE      ), ANY(__SCREAMING_SNAKE_CASE      )]}      )
           self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]      )      ) ,     1.0      )

           __UpperCAmelCase     				=classifier(
               """Who are you voting for in 2020?""" ,     candidate_labels="""politics""" ,     hypothesis_template="""This text is about {}"""      )
           self.assertEqual(__SCREAMING_SNAKE_CASE ,     {"""sequence""": ANY(__SCREAMING_SNAKE_CASE      ), """labels""": [ANY(__SCREAMING_SNAKE_CASE      )], """scores""": [ANY(__SCREAMING_SNAKE_CASE      )]}      )

           # https://github.com/huggingface/transformers/issues/13846
           __UpperCAmelCase     				=classifier(["""I am happy"""] ,     ["""positive""", """negative"""]      )
           self.assertEqual(
               __SCREAMING_SNAKE_CASE ,     [
                   {"""sequence""": ANY(__SCREAMING_SNAKE_CASE      ), """labels""": [ANY(__SCREAMING_SNAKE_CASE      ), ANY(__SCREAMING_SNAKE_CASE      )], """scores""": [ANY(__SCREAMING_SNAKE_CASE      ), ANY(__SCREAMING_SNAKE_CASE      )]}
                   for i in range(1      )
               ] ,     )
           __UpperCAmelCase     				=classifier(["""I am happy""", """I am sad"""] ,     ["""positive""", """negative"""]      )
           self.assertEqual(
               __SCREAMING_SNAKE_CASE ,     [
                   {"""sequence""": ANY(__SCREAMING_SNAKE_CASE      ), """labels""": [ANY(__SCREAMING_SNAKE_CASE      ), ANY(__SCREAMING_SNAKE_CASE      )], """scores""": [ANY(__SCREAMING_SNAKE_CASE      ), ANY(__SCREAMING_SNAKE_CASE      )]}
                   for i in range(2      )
               ] ,     )

           with self.assertRaises(__SCREAMING_SNAKE_CASE      ):
                  classifier("""""" ,     candidate_labels="""politics"""      )

           with self.assertRaises(__SCREAMING_SNAKE_CASE      ):
                  classifier(__SCREAMING_SNAKE_CASE ,     candidate_labels="""politics"""      )

           with self.assertRaises(__SCREAMING_SNAKE_CASE      ):
                  classifier("""Who are you voting for in 2020?""" ,     candidate_labels=""""""      )

           with self.assertRaises(__SCREAMING_SNAKE_CASE      ):
                  classifier("""Who are you voting for in 2020?""" ,     candidate_labels=__SCREAMING_SNAKE_CASE      )

           with self.assertRaises(__SCREAMING_SNAKE_CASE      ):
                  classifier(
                      """Who are you voting for in 2020?""" ,     candidate_labels="""politics""" ,     hypothesis_template="""Not formatting template""" ,     )

           with self.assertRaises(__SCREAMING_SNAKE_CASE      ):
                  classifier(
                      """Who are you voting for in 2020?""" ,     candidate_labels="""politics""" ,     hypothesis_template=__SCREAMING_SNAKE_CASE ,     )

           self.run_entailment_id(__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Pipeline      )      ->							Optional[int]:
           __UpperCAmelCase     				=zero_shot_classifier.model.config
           __UpperCAmelCase     				=config.labelaid
           __UpperCAmelCase     				=zero_shot_classifier.entailment_id

           __UpperCAmelCase     				={"""LABEL_0""": 0, """LABEL_1""": 1, """LABEL_2""": 2}
           self.assertEqual(zero_shot_classifier.entailment_id ,     -1      )

           __UpperCAmelCase     				={"""entailment""": 0, """neutral""": 1, """contradiction""": 2}
           self.assertEqual(zero_shot_classifier.entailment_id ,     0      )

           __UpperCAmelCase     				={"""ENTAIL""": 0, """NON-ENTAIL""": 1}
           self.assertEqual(zero_shot_classifier.entailment_id ,     0      )

           __UpperCAmelCase     				={"""ENTAIL""": 2, """NEUTRAL""": 1, """CONTR""": 0}
           self.assertEqual(zero_shot_classifier.entailment_id ,     2      )

           __UpperCAmelCase     				=original_labelaid
           self.assertEqual(__SCREAMING_SNAKE_CASE ,     zero_shot_classifier.entailment_id      )



    @require_torch
    def _a							(				self      :     List[str]      )      ->							List[str]:
           __UpperCAmelCase     				=pipeline(
               """zero-shot-classification""" ,     model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" ,     framework="""pt""" ,     )
           # There was a regression in 4.10 for this
           # Adding a test so we don't make the mistake again.
           # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
           zero_shot_classifier(
               """Who are you voting for in 2020?""" * 100 ,     candidate_labels=["""politics""", """public health""", """science"""]      )



    @require_torch
    def _a							(				self      :     List[str]      )      ->							Union[str, Any]:
           __UpperCAmelCase     				=pipeline(
               """zero-shot-classification""" ,     model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" ,     framework="""pt""" ,     )
           __UpperCAmelCase     				=zero_shot_classifier(
               """Who are you voting for in 2020?""" ,     candidate_labels=["""politics""", """public health""", """science"""]      )

           self.assertEqual(
               nested_simplify(__SCREAMING_SNAKE_CASE      ) ,     {
                   """sequence""": """Who are you voting for in 2020?""",
                   """labels""": ["""science""", """public health""", """politics"""],
                   """scores""": [0.333, 0.333, 0.333],
               } ,     )



    @require_tf
    def _a							(				self      :     Dict      )      ->							Optional[int]:
           __UpperCAmelCase     				=pipeline(
               """zero-shot-classification""" ,     model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" ,     framework="""tf""" ,     )
           __UpperCAmelCase     				=zero_shot_classifier(
               """Who are you voting for in 2020?""" ,     candidate_labels=["""politics""", """public health""", """science"""]      )

           self.assertEqual(
               nested_simplify(__SCREAMING_SNAKE_CASE      ) ,     {
                   """sequence""": """Who are you voting for in 2020?""",
                   """labels""": ["""science""", """public health""", """politics"""],
                   """scores""": [0.333, 0.333, 0.333],
               } ,     )



    @slow
    @require_torch
    def _a							(				self      :     str      )      ->							Any:
           __UpperCAmelCase     				=pipeline("""zero-shot-classification""" ,     model="""roberta-large-mnli""" ,     framework="""pt"""      )
           __UpperCAmelCase     				=zero_shot_classifier(
               """Who are you voting for in 2020?""" ,     candidate_labels=["""politics""", """public health""", """science"""]      )

           self.assertEqual(
               nested_simplify(__SCREAMING_SNAKE_CASE      ) ,     {
                   """sequence""": """Who are you voting for in 2020?""",
                   """labels""": ["""politics""", """public health""", """science"""],
                   """scores""": [0.976, 0.015, 0.009],
               } ,     )
           __UpperCAmelCase     				=zero_shot_classifier(
               """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
               """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
               """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
               """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
               """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
               """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
               """ English-to-German translation task, improving over the existing best results, including ensembles by"""
               """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
               """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
               """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
               """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
               """ large and limited training data.""" ,     candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""] ,     multi_label=__SCREAMING_SNAKE_CASE ,     )
           self.assertEqual(
               nested_simplify(__SCREAMING_SNAKE_CASE      ) ,     {
                   """sequence""": (
                       """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                       """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                       """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                       """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                       """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                       """ superior in quality while being more parallelizable and requiring significantly less time to"""
                       """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                       """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                       """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                       """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                       """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                       """ other tasks by applying it successfully to English constituency parsing both with large and"""
                       """ limited training data."""
                   ),
                   """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
                   """scores""": [0.817, 0.713, 0.018, 0.018],
               } ,     )



    @slow
    @require_tf
    def _a							(				self      :     Any      )      ->							List[Any]:
           __UpperCAmelCase     				=pipeline("""zero-shot-classification""" ,     model="""roberta-large-mnli""" ,     framework="""tf"""      )
           __UpperCAmelCase     				=zero_shot_classifier(
               """Who are you voting for in 2020?""" ,     candidate_labels=["""politics""", """public health""", """science"""]      )

           self.assertEqual(
               nested_simplify(__SCREAMING_SNAKE_CASE      ) ,     {
                   """sequence""": """Who are you voting for in 2020?""",
                   """labels""": ["""politics""", """public health""", """science"""],
                   """scores""": [0.976, 0.015, 0.009],
               } ,     )
           __UpperCAmelCase     				=zero_shot_classifier(
               """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
               """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
               """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
               """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
               """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
               """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
               """ English-to-German translation task, improving over the existing best results, including ensembles by"""
               """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
               """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
               """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
               """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
               """ large and limited training data.""" ,     candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""] ,     multi_label=__SCREAMING_SNAKE_CASE ,     )
           self.assertEqual(
               nested_simplify(__SCREAMING_SNAKE_CASE      ) ,     {
                   """sequence""": (
                       """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                       """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                       """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                       """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                       """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                       """ superior in quality while being more parallelizable and requiring significantly less time to"""
                       """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                       """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                       """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                       """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                       """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                       """ other tasks by applying it successfully to English constituency parsing both with large and"""
                       """ limited training data."""
                   ),
                   """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
                   """scores""": [0.817, 0.713, 0.018, 0.018],
               } ,     )












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase      :       List[Any]    =						{
    '''configuration_owlvit''': [
        '''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''OwlViTConfig''',
        '''OwlViTOnnxConfig''',
        '''OwlViTTextConfig''',
        '''OwlViTVisionConfig''',
    ],
    '''processing_owlvit''': ['''OwlViTProcessor'''],
}


try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase      :       List[Any]    =						['''OwlViTFeatureExtractor''']
 lowerCamelCase      :       str    =						['''OwlViTImageProcessor''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase      :       List[str]    =						[
     '''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''OwlViTModel''',
     '''OwlViTPreTrainedModel''',
     '''OwlViTTextModel''',
     '''OwlViTVisionModel''',
     '''OwlViTForObjectDetection''',
 ]

if TYPE_CHECKING:
 from .configuration_owlvit import (
     OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     OwlViTConfig,
     OwlViTOnnxConfig,
     OwlViTTextConfig,
     OwlViTVisionConfig,
 )
 from .processing_owlvit import OwlViTProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_owlvit import OwlViTFeatureExtractor
  from .image_processing_owlvit import OwlViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_owlvit import (
      OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      OwlViTForObjectDetection,
      OwlViTModel,
      OwlViTPreTrainedModel,
      OwlViTTextModel,
      OwlViTVisionModel,
  )

else:
 import sys

 lowerCamelCase      :       List[str]    =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






