


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 							lowercase__:






           '''simple docstring'''







           @staticmethod
           def    UpperCAmelCase							(		*__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE)					->						Any:







              """simple docstring"""







              pass



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






      '''simple docstring'''







      snake_case__       =					MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
      snake_case__       =					TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						int:







         """simple docstring"""







         UpperCamelCase__ :			Any 						=ImageClassificationPipeline(model=_UpperCAmelCase   ,			image_processor=_UpperCAmelCase   ,			top_k=2)
         UpperCamelCase__ :			Optional[int] 						=[
             Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
             "http://images.cocodataset.org/val2017/000000039769.jpg",
         ]
         return image_classifier, examples




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						int:







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						=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

         UpperCamelCase__ :			Optional[int] 						=datasets.load_dataset("hf-internal-testing/fixtures_image_utils"   ,			"image"   ,			split="test")

         # Accepts URL + PIL.Image + lists
         UpperCamelCase__ :			Optional[Any] 						=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    UpperCAmelCase							(		self)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			int 						="hf-internal-testing/tiny-random-vit"
         UpperCamelCase__ :			List[Any] 						=pipeline("image-classification"   ,			model=_UpperCAmelCase)

         UpperCamelCase__ :			str 						=image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
         self.assertEqual(
             nested_simplify(_UpperCAmelCase   ,			decimals=4)   ,			[{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}]   ,			)

         UpperCamelCase__ :			str 						=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": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}],
                 [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}],
             ]   ,			)




      @require_tf
      def    UpperCAmelCase							(		self)					->						Union[str, Any]:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						="hf-internal-testing/tiny-random-vit"
         UpperCamelCase__ :			int 						=pipeline("image-classification"   ,			model=_UpperCAmelCase   ,			framework="tf")

         UpperCamelCase__ :			Tuple 						=image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
         self.assertEqual(
             nested_simplify(_UpperCAmelCase   ,			decimals=4)   ,			[{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}]   ,			)

         UpperCamelCase__ :			Optional[int] 						=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": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}],
                 [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}],
             ]   ,			)




      def    UpperCAmelCase							(		self)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=PreTrainedTokenizer()

         # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
         UpperCamelCase__ :			List[Any] 						=pipeline(
             "image-classification"   ,			model="hf-internal-testing/tiny-random-vit"   ,			tokenizer=_UpperCAmelCase)

         self.assertIs(image_classifier.tokenizer   ,			_UpperCAmelCase)




      @slow
      @require_torch
      def    UpperCAmelCase							(		self)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						=pipeline("image-classification"   ,			model="deepmind/vision-perceiver-conv")
         UpperCamelCase__ :			int 						=image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
         self.assertEqual(
             nested_simplify(_UpperCAmelCase   ,			decimals=4)   ,			[
                 {"score": 0.43_85, "label": "tabby, tabby cat"},
                 {"score": 0.3_21, "label": "tiger cat"},
                 {"score": 0.05_02, "label": "Egyptian cat"},
                 {"score": 0.01_37, "label": "crib, cot"},
                 {"score": 0.0_07, "label": "radiator"},
             ]   ,			)

         UpperCamelCase__ :			Optional[int] 						=pipeline("image-classification"   ,			model="deepmind/vision-perceiver-fourier")
         UpperCamelCase__ :			Tuple 						=image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
         self.assertEqual(
             nested_simplify(_UpperCAmelCase   ,			decimals=4)   ,			[
                 {"score": 0.56_58, "label": "tabby, tabby cat"},
                 {"score": 0.13_09, "label": "tiger cat"},
                 {"score": 0.07_22, "label": "Egyptian cat"},
                 {"score": 0.07_07, "label": "remote control, remote"},
                 {"score": 0.00_82, "label": "computer keyboard, keypad"},
             ]   ,			)

         UpperCamelCase__ :			str 						=pipeline("image-classification"   ,			model="deepmind/vision-perceiver-learned")
         UpperCamelCase__ :			Optional[int] 						=image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
         self.assertEqual(
             nested_simplify(_UpperCAmelCase   ,			decimals=4)   ,			[
                 {"score": 0.30_22, "label": "tabby, tabby cat"},
                 {"score": 0.23_62, "label": "Egyptian cat"},
                 {"score": 0.18_56, "label": "tiger cat"},
                 {"score": 0.03_24, "label": "remote control, remote"},
                 {"score": 0.00_96, "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))



