'''simple docstring'''

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_     :						Union[str, Any]    							={'''LayoutLMv2Config''', '''LayoutLMv3Config'''}


@is_pipeline_test
class     __UpperCAmelCase       ( unittest.TestCase		):
  __A :				Tuple											= MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
  __A :				Any											= TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

  if model_mapping is not None:
    __A :				Dict											= {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
  if tf_model_mapping is not None:
    __A :				str											= {
        config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
    }





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      ZeroShotClassificationPipeline(
         model=_lowerCamelCase    ,     tokenizer=_lowerCamelCase    ,     candidate_labels=['''polics''', '''health'''] )
     return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]





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

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

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

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

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

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

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

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

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

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

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

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

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

     self.run_entailment_id(_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      zero_shot_classifier.model.config
     lowerCAmelCase_			=      config.labelaid
     lowerCAmelCase_			=      zero_shot_classifier.entailment_id

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

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

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

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

     lowerCAmelCase_			=      original_labelaid
     self.assertEqual(_lowerCamelCase    ,     zero_shot_classifier.entailment_id )





  @require_torch
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      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 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      pipeline(
         '''zero-shot-classification'''    ,     model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''    ,     framework='''pt'''    ,     )
     lowerCAmelCase_			=      zero_shot_classifier(
         '''Who are you voting for in 2020?'''    ,     candidate_labels=['''politics''', '''public health''', '''science'''] )

     self.assertEqual(
         nested_simplify(_lowerCamelCase )    ,     {
             '''sequence''': '''Who are you voting for in 2020?''',
             '''labels''': ['''science''', '''public health''', '''politics'''],
             '''scores''': [0.3_33, 0.3_33, 0.3_33],
         }    ,     )





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

     self.assertEqual(
         nested_simplify(_lowerCamelCase )    ,     {
             '''sequence''': '''Who are you voting for in 2020?''',
             '''labels''': ['''science''', '''public health''', '''politics'''],
             '''scores''': [0.3_33, 0.3_33, 0.3_33],
         }    ,     )





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

     self.assertEqual(
         nested_simplify(_lowerCamelCase )    ,     {
             '''sequence''': '''Who are you voting for in 2020?''',
             '''labels''': ['''politics''', '''public health''', '''science'''],
             '''scores''': [0.9_76, 0.0_15, 0.0_09],
         }    ,     )
     lowerCAmelCase_			=      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=_lowerCamelCase    ,     )
     self.assertEqual(
         nested_simplify(_lowerCamelCase )    ,     {
             '''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''': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
         }    ,     )







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

     self.assertEqual(
         nested_simplify(_lowerCamelCase )    ,     {
             '''sequence''': '''Who are you voting for in 2020?''',
             '''labels''': ['''politics''', '''public health''', '''science'''],
             '''scores''': [0.9_76, 0.0_15, 0.0_09],
         }    ,     )
     lowerCAmelCase_			=      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=_lowerCamelCase    ,     )
     self.assertEqual(
         nested_simplify(_lowerCamelCase )    ,     {
             '''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''': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
         }    ,     )






'''simple docstring'''

import colorsys

from PIL import Image  # type: ignore



def snake_case_      (					__snake_case  :							float	,   __snake_case  :							float	,   __snake_case  :							int)			->					float:
   lowerCAmelCase_			=      x
   lowerCAmelCase_			=      y
   for step in range(__snake_case):  # noqa: B007
      lowerCAmelCase_			=      a * a - b * b + x
      lowerCAmelCase_			=      2 * a * b + y
      lowerCAmelCase_			=      a_new

      # divergence happens for all complex number with an absolute value
      # greater than 4
      if a * a + b * b > 4:
         break
   return step / (max_step - 1)



def snake_case_      (					__snake_case  :							float)			->					tuple:
   if distance == 1:
      return (0, 0, 0)
   else:
      return (255, 255, 255)



def snake_case_      (					__snake_case  :							float)			->					tuple:
   if distance == 1:
      return (0, 0, 0)
   else:
      return tuple(round(i * 255) for i in colorsys.hsv_to_rgb(__snake_case	,   1	,   1))



def snake_case_      (					__snake_case  :							int = 800	,   __snake_case  :							int = 600	,   __snake_case  :							float = -0.6	,   __snake_case  :							float = 0	,   __snake_case  :							float = 3.2	,   __snake_case  :							int = 50	,   __snake_case  :							bool = True	,   )			->					Image.Image:
   lowerCAmelCase_			=      Image.new('''RGB'''	,   (image_width, image_height))
   lowerCAmelCase_			=      img.load()

   # loop through the image-coordinates
   for image_x in range(__snake_case):
      for image_y in range(__snake_case):
         # determine the figure-coordinates based on the image-coordinates
         lowerCAmelCase_			=      figure_width / image_width * image_height
         lowerCAmelCase_			=      figure_center_x + (image_x / image_width - 0.5) * figure_width
         lowerCAmelCase_			=      figure_center_y + (image_y / image_height - 0.5) * figure_height

         lowerCAmelCase_			=      get_distance(__snake_case	,   __snake_case	,   __snake_case)

         # color the corresponding pixel based on the selected coloring-function
         if use_distance_color_coding:
            lowerCAmelCase_			=      get_color_coded_rgb(__snake_case)
         else:
            lowerCAmelCase_			=      get_black_and_white_rgb(__snake_case)

   return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 A_     :						List[str]    							=get_image()

 # uncomment for colored version, different section, zoomed in
 # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
 # figure_width = 0.8)

 # uncomment for black and white version, full figure
 # img = get_image(use_distance_color_coding = False)

 # uncomment to save the image
 # img.save("mandelbrot.png")

 img.show()





