

import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:



 class 						a_			:



       """simple docstring"""







       @staticmethod
       def 			_lowerCAmelCase	( *snake_case	:				Optional[int]     ,**snake_case	:				List[str]							):
            pass



@is_pipeline_test
@require_torch_or_tf
@require_vision
class 						a_			(				unittest.TestCase	):



      """simple docstring"""







      __UpperCAmelCase     						= MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
      __UpperCAmelCase     						= TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
      def 			_lowerCAmelCase	( self	:				Any     ,snake_case	:				Any     ,snake_case	:				Optional[Any]     ,snake_case	:				List[str]							):
           SCREAMING_SNAKE_CASE 		=ImageClassificationPipeline(model=snake_case     ,image_processor=snake_case     ,top_k=2							)
           SCREAMING_SNAKE_CASE 		=[
               Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
               'http://images.cocodataset.org/val2017/000000039769.jpg',
           ]
           return image_classifier, examples
      def 			_lowerCAmelCase	( self	:				List[str]     ,snake_case	:				Optional[Any]     ,snake_case	:				Any							):
           SCREAMING_SNAKE_CASE 		=image_classifier('./tests/fixtures/tests_samples/COCO/000000039769.png'							)

           self.assertEqual(
               snake_case     ,[
                   {'score': ANY(snake_case							), 'label': ANY(snake_case							)},
                   {'score': ANY(snake_case							), 'label': ANY(snake_case							)},
               ]     ,)

           import datasets

           SCREAMING_SNAKE_CASE 		=datasets.load_dataset('hf-internal-testing/fixtures_image_utils'     ,'image'     ,split='test'							)

           # Accepts URL + PIL.Image + lists
           SCREAMING_SNAKE_CASE 		=image_classifier(
               [
                   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
                   'http://images.cocodataset.org/val2017/000000039769.jpg',
                   # RGBA
                   dataset[0]['file'],
                   # LA
                   dataset[1]['file'],
                   # L
                   dataset[2]['file'],
               ]							)
           self.assertEqual(
               snake_case     ,[
                   [
                       {'score': ANY(snake_case							), 'label': ANY(snake_case							)},
                       {'score': ANY(snake_case							), 'label': ANY(snake_case							)},
                   ],
                   [
                       {'score': ANY(snake_case							), 'label': ANY(snake_case							)},
                       {'score': ANY(snake_case							), 'label': ANY(snake_case							)},
                   ],
                   [
                       {'score': ANY(snake_case							), 'label': ANY(snake_case							)},
                       {'score': ANY(snake_case							), 'label': ANY(snake_case							)},
                   ],
                   [
                       {'score': ANY(snake_case							), 'label': ANY(snake_case							)},
                       {'score': ANY(snake_case							), 'label': ANY(snake_case							)},
                   ],
                   [
                       {'score': ANY(snake_case							), 'label': ANY(snake_case							)},
                       {'score': ANY(snake_case							), 'label': ANY(snake_case							)},
                   ],
               ]     ,)
      @require_torch
      def 			_lowerCAmelCase	( self	:				int							):
           SCREAMING_SNAKE_CASE 		='hf-internal-testing/tiny-random-vit'
           SCREAMING_SNAKE_CASE 		=pipeline('image-classification'     ,model=snake_case							)

           SCREAMING_SNAKE_CASE 		=image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
           self.assertEqual(
               nested_simplify(snake_case     ,decimals=4							)     ,[{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}]     ,)

           SCREAMING_SNAKE_CASE 		=image_classifier(
               [
                   'http://images.cocodataset.org/val2017/000000039769.jpg',
                   'http://images.cocodataset.org/val2017/000000039769.jpg',
               ]     ,top_k=2     ,)
           self.assertEqual(
               nested_simplify(snake_case     ,decimals=4							)     ,[
                   [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
                   [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
               ]     ,)
      @require_tf
      def 			_lowerCAmelCase	( self	:				List[Any]							):
           SCREAMING_SNAKE_CASE 		='hf-internal-testing/tiny-random-vit'
           SCREAMING_SNAKE_CASE 		=pipeline('image-classification'     ,model=snake_case     ,framework='tf'							)

           SCREAMING_SNAKE_CASE 		=image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
           self.assertEqual(
               nested_simplify(snake_case     ,decimals=4							)     ,[{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}]     ,)

           SCREAMING_SNAKE_CASE 		=image_classifier(
               [
                   'http://images.cocodataset.org/val2017/000000039769.jpg',
                   'http://images.cocodataset.org/val2017/000000039769.jpg',
               ]     ,top_k=2     ,)
           self.assertEqual(
               nested_simplify(snake_case     ,decimals=4							)     ,[
                   [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
                   [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
               ]     ,)
      def 			_lowerCAmelCase	( self	:				Dict							):
           SCREAMING_SNAKE_CASE 		=PreTrainedTokenizer()

           # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
           SCREAMING_SNAKE_CASE 		=pipeline(
               'image-classification'     ,model='hf-internal-testing/tiny-random-vit'     ,tokenizer=snake_case							)

           self.assertIs(image_classifier.tokenizer     ,snake_case							)


      @slow
      @require_torch
      def 			_lowerCAmelCase	( self	:				Optional[int]							):
           # Perceiver is not tested by `run_pipeline_test` properly.
           # That is because the type of feature_extractor and model preprocessor need to be kept
           # in sync, which is not the case in the current design
           SCREAMING_SNAKE_CASE 		=pipeline('image-classification'     ,model='deepmind/vision-perceiver-conv'							)
           SCREAMING_SNAKE_CASE 		=image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
           self.assertEqual(
               nested_simplify(snake_case     ,decimals=4							)     ,[
                   {'score': 0.4_385, 'label': 'tabby, tabby cat'},
                   {'score': 0.321, 'label': 'tiger cat'},
                   {'score': 0.0_502, 'label': 'Egyptian cat'},
                   {'score': 0.0_137, 'label': 'crib, cot'},
                   {'score': 0.007, 'label': 'radiator'},
               ]     ,)

           SCREAMING_SNAKE_CASE 		=pipeline('image-classification'     ,model='deepmind/vision-perceiver-fourier'							)
           SCREAMING_SNAKE_CASE 		=image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
           self.assertEqual(
               nested_simplify(snake_case     ,decimals=4							)     ,[
                   {'score': 0.5_658, 'label': 'tabby, tabby cat'},
                   {'score': 0.1_309, 'label': 'tiger cat'},
                   {'score': 0.0_722, 'label': 'Egyptian cat'},
                   {'score': 0.0_707, 'label': 'remote control, remote'},
                   {'score': 0.0_082, 'label': 'computer keyboard, keypad'},
               ]     ,)

           SCREAMING_SNAKE_CASE 		=pipeline('image-classification'     ,model='deepmind/vision-perceiver-learned'							)
           SCREAMING_SNAKE_CASE 		=image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
           self.assertEqual(
               nested_simplify(snake_case     ,decimals=4							)     ,[
                   {'score': 0.3_022, 'label': 'tabby, tabby cat'},
                   {'score': 0.2_362, 'label': 'Egyptian cat'},
                   {'score': 0.1_856, 'label': 'tiger cat'},
                   {'score': 0.0_324, 'label': 'remote control, remote'},
                   {'score': 0.0_096, 'label': 'quilt, comforter, comfort, puff'},
               ]     ,)




from math import cos, sin







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=float(lowerCAmelCase_     )
     return [[scaling_factor * int(x == y     ) for x in range(2     )] for y in range(2     )]







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=cos(lowerCAmelCase_     ), sin(lowerCAmelCase_     )
     return [[c, -s], [s, c]]







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=cos(lowerCAmelCase_     ), sin(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=c * s
     return [[c * c, cs], [cs, s * s]]







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=cos(lowerCAmelCase_     ), sin(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=c * s
     return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(f'    {scaling(5) = }')
print(f'  {rotation(45) = }')
print(f'{projection(45) = }')



print(f'{reflection(45) = }')

