




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.
_lowerCamelCase      			=						{'LayoutLMv2Config', 'LayoutLMv3Config'}



@is_pipeline_test
class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	int       		=  MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
     lowerCAmelCase    :	Dict       		=  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

     if model_mapping is not None:
          lowerCAmelCase    :	Optional[int]       		=  {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
     if tf_model_mapping is not None:
          lowerCAmelCase    :	List[str]       		=  {
              config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
          }
     def   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      Optional[int] ,   __snake_case       :      Tuple ,   __snake_case       :      Tuple    ):
           UpperCAmelCase_										=					ZeroShotClassificationPipeline(
               model=__snake_case ,   tokenizer=__snake_case ,   candidate_labels=['''polics''', '''health''']    )
           return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
     def   lowerCamelCase_       (     self       :      List[str] ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Optional[Any]    ):
           UpperCAmelCase_										=					classifier('''Who are you voting for in 2020?''' ,   candidate_labels='''politics'''    )
           self.assertEqual(__snake_case ,   {'''sequence''': ANY(__snake_case    ), '''labels''': [ANY(__snake_case    )], '''scores''': [ANY(__snake_case    )]}    )

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

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

           UpperCAmelCase_										=					classifier('''Who are you voting for in 2020?''' ,   candidate_labels='''politics, public health'''    )
           self.assertEqual(
               __snake_case ,   {'''sequence''': ANY(__snake_case    ), '''labels''': [ANY(__snake_case    ), ANY(__snake_case    )], '''scores''': [ANY(__snake_case    ), ANY(__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(
               __snake_case ,   {'''sequence''': ANY(__snake_case    ), '''labels''': [ANY(__snake_case    ), ANY(__snake_case    )], '''scores''': [ANY(__snake_case    ), ANY(__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(__snake_case ,   {'''sequence''': ANY(__snake_case    ), '''labels''': [ANY(__snake_case    )], '''scores''': [ANY(__snake_case    )]}    )

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

           with self.assertRaises(__snake_case    ):
                 classifier('''''' ,   candidate_labels='''politics'''    )

           with self.assertRaises(__snake_case    ):
                 classifier(__snake_case ,   candidate_labels='''politics'''    )

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

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

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

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

           self.run_entailment_id(__snake_case    )
     def   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      Pipeline    ):
           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(__snake_case ,   zero_shot_classifier.entailment_id    )
     @require_torch
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
           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?''' * 1_00 ,   candidate_labels=['''politics''', '''public health''', '''science''']    )
     @require_torch
     def   lowerCamelCase_       (     self       :      List[str]    ):
           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(__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   lowerCamelCase_       (     self       :      Tuple    ):
           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(__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   lowerCamelCase_       (     self       :      str    ):
           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(__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=__snake_case ,   )
           self.assertEqual(
               nested_simplify(__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   lowerCamelCase_       (     self       :      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(__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=__snake_case ,   )
           self.assertEqual(
               nested_simplify(__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 Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCamelCase      			=						logging.get_logger(__name__)



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	List[Any]       		=  ['pixel_values']
     def __init__(     self       :      Any ,   __snake_case       :      bool = True ,   __snake_case       :      Dict[str, int] = None ,   __snake_case       :      PILImageResampling = PILImageResampling.BICUBIC ,   __snake_case       :      bool = True ,   __snake_case       :      Dict[str, int] = None ,   __snake_case       :      bool = True ,   __snake_case       :      Union[int, float] = 1 / 2_55 ,   __snake_case       :      bool = True ,   __snake_case       :      Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN ,   __snake_case       :      Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD ,   **__snake_case       :      Optional[Any] ,   ):
         super().__init__(**__snake_case    )
         UpperCAmelCase_										=					size if size is not None else {'''shortest_edge''': 2_24}
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   default_to_square=__snake_case    )
         UpperCAmelCase_										=					crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   param_name='''crop_size'''    )

         UpperCAmelCase_										=					do_resize
         UpperCAmelCase_										=					size
         UpperCAmelCase_										=					resample
         UpperCAmelCase_										=					do_center_crop
         UpperCAmelCase_										=					crop_size
         UpperCAmelCase_										=					do_rescale
         UpperCAmelCase_										=					rescale_factor
         UpperCAmelCase_										=					do_normalize
         UpperCAmelCase_										=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCAmelCase_										=					image_std if image_std is not None else IMAGENET_DEFAULT_STD
     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      np.ndarray ,   __snake_case       :      Dict[str, int] ,   __snake_case       :      PILImageResampling = PILImageResampling.BICUBIC ,   __snake_case       :      Optional[Union[str, ChannelDimension]] = None ,   **__snake_case       :      Tuple ,   ):
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   default_to_square=__snake_case    )
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
             UpperCAmelCase_										=					int((2_56 / 2_24) * size['''shortest_edge''']    )
             UpperCAmelCase_										=					get_resize_output_image_size(__snake_case ,   size=__snake_case ,   default_to_square=__snake_case    )
             UpperCAmelCase_										=					{'''height''': output_size[0], '''width''': output_size[1]}
         if "height" not in size_dict or "width" not in size_dict:
             raise ValueError(
                 F'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'    )
         return resize(
             __snake_case ,   size=(size_dict['''height'''], size_dict['''width''']) ,   resample=__snake_case ,   data_format=__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      Tuple ,   __snake_case       :      np.ndarray ,   __snake_case       :      Dict[str, int] ,   __snake_case       :      Optional[Union[str, ChannelDimension]] = None ,   **__snake_case       :      Optional[int] ,   ):
         UpperCAmelCase_										=					get_size_dict(__snake_case    )
         if "height" not in size or "width" not in size:
             raise ValueError(F'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'    )
         return center_crop(__snake_case ,   size=(size['''height'''], size['''width''']) ,   data_format=__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      np.ndarray ,   __snake_case       :      Union[int, float] ,   __snake_case       :      Optional[Union[str, ChannelDimension]] = None ,   **__snake_case       :      List[Any] ,   ):
         return rescale(__snake_case ,   scale=__snake_case ,   data_format=__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      np.ndarray ,   __snake_case       :      Union[float, List[float]] ,   __snake_case       :      Union[float, List[float]] ,   __snake_case       :      Optional[Union[str, ChannelDimension]] = None ,   **__snake_case       :      Union[str, Any] ,   ):
         return normalize(__snake_case ,   mean=__snake_case ,   std=__snake_case ,   data_format=__snake_case ,   **__snake_case    )




     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      ImageInput ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[Dict[str, int]] = None ,   __snake_case       :      PILImageResampling = None ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[Dict[str, int]] = None ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[float] = None ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[Union[float, Iterable[float]]] = None ,   __snake_case       :      Optional[Union[float, Iterable[float]]] = None ,   __snake_case       :      Optional[TensorType] = None ,   __snake_case       :      ChannelDimension = ChannelDimension.FIRST ,   **__snake_case       :      List[str] ,   ):
         UpperCAmelCase_										=					do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_										=					resample if resample is not None else self.resample
         UpperCAmelCase_										=					do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_										=					do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_										=					rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_										=					do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_										=					image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_										=					image_std if image_std is not None else self.image_std

         UpperCAmelCase_										=					size if size is not None else self.size
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   default_to_square=__snake_case    )
         UpperCAmelCase_										=					crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   param_name='''crop_size'''    )

         UpperCAmelCase_										=					make_list_of_images(__snake_case    )

         if not valid_images(__snake_case    ):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.'''    )

         if do_resize and size is None:
             raise ValueError('''Size must be specified if do_resize is True.'''    )

         if do_center_crop and crop_size is None:
             raise ValueError('''Crop size must be specified if do_center_crop is True.'''    )

         if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.'''    )

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.'''    )

         # All transformations expect numpy arrays.
         UpperCAmelCase_										=					[to_numpy_array(__snake_case    ) for image in images]

         if do_resize:
             UpperCAmelCase_										=					[self.resize(__snake_case ,   __snake_case ,   __snake_case    ) for image in images]

         if do_center_crop:
             UpperCAmelCase_										=					[self.center_crop(__snake_case ,   __snake_case    ) for image in images]

         if do_rescale:
             UpperCAmelCase_										=					[self.rescale(__snake_case ,   __snake_case    ) for image in images]

         if do_normalize:
             UpperCAmelCase_										=					[self.normalize(__snake_case ,   __snake_case ,   __snake_case    ) for image in images]

         UpperCAmelCase_										=					[to_channel_dimension_format(__snake_case ,   __snake_case    ) for image in images]

         UpperCAmelCase_										=					{'''pixel_values''': images}
         return BatchFeature(data=__snake_case ,   tensor_type=__snake_case    )





