
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 			UpperCamelCase_				:




       '''simple docstring'''







       @staticmethod
       def lowercase__		( *_UpperCAmelCase					,					**_UpperCAmelCase):
             pass


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




      '''simple docstring'''







      a		:Union[str, Any]						 =      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
      a		:Tuple						 =      TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      ImageClassificationPipeline(model=_UpperCAmelCase					,					image_processor=_UpperCAmelCase					,					top_k=2)
            lowerCAmelCase_						=      [
                Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''),
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
            ]
            return image_classifier, examples







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      image_classifier('''./tests/fixtures/tests_samples/COCO/000000039769.png''')

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

            import datasets

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

            # Accepts URL + PIL.Image + lists
            lowerCAmelCase_						=      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(
                _UpperCAmelCase					,					[
                    [
                        {'''score''': ANY(_UpperCAmelCase), '''label''': ANY(_UpperCAmelCase)},
                        {'''score''': ANY(_UpperCAmelCase), '''label''': ANY(_UpperCAmelCase)},
                    ],
                    [
                        {'''score''': ANY(_UpperCAmelCase), '''label''': ANY(_UpperCAmelCase)},
                        {'''score''': ANY(_UpperCAmelCase), '''label''': ANY(_UpperCAmelCase)},
                    ],
                    [
                        {'''score''': ANY(_UpperCAmelCase), '''label''': ANY(_UpperCAmelCase)},
                        {'''score''': ANY(_UpperCAmelCase), '''label''': ANY(_UpperCAmelCase)},
                    ],
                    [
                        {'''score''': ANY(_UpperCAmelCase), '''label''': ANY(_UpperCAmelCase)},
                        {'''score''': ANY(_UpperCAmelCase), '''label''': ANY(_UpperCAmelCase)},
                    ],
                    [
                        {'''score''': ANY(_UpperCAmelCase), '''label''': ANY(_UpperCAmelCase)},
                        {'''score''': ANY(_UpperCAmelCase), '''label''': ANY(_UpperCAmelCase)},
                    ],
                ]					,					)







      @require_torch
      def lowercase__		( self):
            lowerCAmelCase_						=      '''hf-internal-testing/tiny-random-vit'''
            lowerCAmelCase_						=      pipeline('''image-classification'''					,					model=_UpperCAmelCase)

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

            lowerCAmelCase_						=      image_classifier(
                [
                    '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                    '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                ]					,					top_k=2					,					)
            self.assertEqual(
                nested_simplify(_UpperCAmelCase					,					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 lowercase__		( self):
            lowerCAmelCase_						=      '''hf-internal-testing/tiny-random-vit'''
            lowerCAmelCase_						=      pipeline('''image-classification'''					,					model=_UpperCAmelCase					,					framework='''tf''')

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

            lowerCAmelCase_						=      image_classifier(
                [
                    '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                    '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                ]					,					top_k=2					,					)
            self.assertEqual(
                nested_simplify(_UpperCAmelCase					,					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 lowercase__		( self):
            lowerCAmelCase_						=      PreTrainedTokenizer()

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

            self.assertIs(image_classifier.tokenizer					,					_UpperCAmelCase)







      @slow
      @require_torch
      def lowercase__		( self):
            # 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
            lowerCAmelCase_						=      pipeline('''image-classification'''					,					model='''deepmind/vision-perceiver-conv''')
            lowerCAmelCase_						=      image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg''')
            self.assertEqual(
                nested_simplify(_UpperCAmelCase					,					decimals=4)					,					[
                    {'''score''': 0.4385, '''label''': '''tabby, tabby cat'''},
                    {'''score''': 0.321, '''label''': '''tiger cat'''},
                    {'''score''': 0.0502, '''label''': '''Egyptian cat'''},
                    {'''score''': 0.0137, '''label''': '''crib, cot'''},
                    {'''score''': 0.007, '''label''': '''radiator'''},
                ]					,					)

            lowerCAmelCase_						=      pipeline('''image-classification'''					,					model='''deepmind/vision-perceiver-fourier''')
            lowerCAmelCase_						=      image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg''')
            self.assertEqual(
                nested_simplify(_UpperCAmelCase					,					decimals=4)					,					[
                    {'''score''': 0.5658, '''label''': '''tabby, tabby cat'''},
                    {'''score''': 0.1309, '''label''': '''tiger cat'''},
                    {'''score''': 0.0722, '''label''': '''Egyptian cat'''},
                    {'''score''': 0.0707, '''label''': '''remote control, remote'''},
                    {'''score''': 0.0082, '''label''': '''computer keyboard, keypad'''},
                ]					,					)

            lowerCAmelCase_						=      pipeline('''image-classification'''					,					model='''deepmind/vision-perceiver-learned''')
            lowerCAmelCase_						=      image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg''')
            self.assertEqual(
                nested_simplify(_UpperCAmelCase					,					decimals=4)					,					[
                    {'''score''': 0.3022, '''label''': '''tabby, tabby cat'''},
                    {'''score''': 0.2362, '''label''': '''Egyptian cat'''},
                    {'''score''': 0.1856, '''label''': '''tiger cat'''},
                    {'''score''': 0.0324, '''label''': '''remote control, remote'''},
                    {'''score''': 0.0096, '''label''': '''quilt, comforter, comfort, puff'''},
                ]					,					)





_snake_case		  =   {
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}
def lowerCamelCase_       (			A			:      List[Any]						):




      """simple docstring"""
      return c.isdigit()
def lowerCamelCase_       (			A			:      List[Any]						):




      """simple docstring"""
      lowerCAmelCase_						=      []

      # iterate over the string in reverse order
      for c in expression.split()[::-1]:
            # push operand to stack
            if is_operand(A						):
                  stack.append(int(A						)						)

            else:
                  # pop values from stack can calculate the result
                  # push the result onto the stack again
                  lowerCAmelCase_						=      stack.pop()
                  lowerCAmelCase_						=      stack.pop()
                  stack.append(calc[c](A		,					A						)						)

      return stack.pop()


# Driver code
if __name__ == "__main__":
 _snake_case		  =   "+ 9 * 2 6"
 print(evaluate(test_expression))

 _snake_case		  =   "/ * 10 2 + 4 1 "
 print(evaluate(test_expression))



