

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import EfficientNetImageProcessor



class 						lowercase_					(						unittest.TestCase  ):



      def __init__(    self   :       Optional[Any]    ,      A__   :       Optional[int]    ,      A__   :       List[Any]=13    ,      A__   :       List[str]=3    ,      A__   :       int=18    ,      A__   :       str=30    ,      A__   :       Optional[Any]=400    ,      A__   :       List[str]=True    ,      A__   :       Any=None    ,      A__   :       Tuple=True    ,      A__   :       str=[0.5, 0.5, 0.5]    ,      A__   :       Union[str, Any]=[0.5, 0.5, 0.5]    ,      )					->  int:
        _snake_case		=							size if size is not None else {'''height''': 18, '''width''': 18}
        _snake_case		=							parent
        _snake_case		=							batch_size
        _snake_case		=							num_channels
        _snake_case		=							image_size
        _snake_case		=							min_resolution
        _snake_case		=							max_resolution
        _snake_case		=							do_resize
        _snake_case		=							size
        _snake_case		=							do_normalize
        _snake_case		=							image_mean
        _snake_case		=							image_std







      def 		UpperCamelCase_	(    self   :       Any	)					->  int:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
        }



@require_torch
@require_vision
class 						lowercase_					(						__lowercase     ,							unittest.TestCase  ):
      UpperCamelCase_    :	Any		   =       EfficientNetImageProcessor if is_vision_available() else None



      def 		UpperCamelCase_	(    self   :       Optional[int]	)					->  Any:
        _snake_case		=							EfficientNetImageProcessorTester(self	)



      @property
      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  Dict:
        return self.image_processor_tester.prepare_image_processor_dict()



      def 		UpperCamelCase_	(    self   :       List[str]	)					->  Dict:
        _snake_case		=							self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(A__    ,      '''image_mean'''	)	)
        self.assertTrue(hasattr(A__    ,      '''image_std'''	)	)
        self.assertTrue(hasattr(A__    ,      '''do_normalize'''	)	)
        self.assertTrue(hasattr(A__    ,      '''do_resize'''	)	)
        self.assertTrue(hasattr(A__    ,      '''size'''	)	)



      def 		UpperCamelCase_	(    self   :       List[Any]	)					->  List[Any]:
        _snake_case		=							self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size    ,      {'''height''': 18, '''width''': 18}	)

        _snake_case		=							self.image_processing_class.from_dict(self.image_processor_dict    ,      size=42	)
        self.assertEqual(image_processor.size    ,      {'''height''': 42, '''width''': 42}	)



      def 		UpperCamelCase_	(    self   :       str	)					->  Any:
        # Initialize image_processing
        _snake_case		=							self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        _snake_case		=							prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=A__	)
        for image in image_inputs:
          self.assertIsInstance(A__    ,      Image.Image	)

        # Test not batched input
        _snake_case		=							image_processing(image_inputs[0]    ,      return_tensors='''pt'''	).pixel_values
        self.assertEqual(
            encoded_images.shape    ,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )    ,      )

        # Test batched
        _snake_case		=							image_processing(A__    ,      return_tensors='''pt'''	).pixel_values
        self.assertEqual(
            encoded_images.shape    ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )    ,      )



      def 		UpperCamelCase_	(    self   :       int	)					->  Dict:
        # Initialize image_processing
        _snake_case		=							self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        _snake_case		=							prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=A__    ,      numpify=A__	)
        for image in image_inputs:
          self.assertIsInstance(A__    ,      np.ndarray	)

        # Test not batched input
        _snake_case		=							image_processing(image_inputs[0]    ,      return_tensors='''pt'''	).pixel_values
        self.assertEqual(
            encoded_images.shape    ,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )    ,      )

        # Test batched
        _snake_case		=							image_processing(A__    ,      return_tensors='''pt'''	).pixel_values
        self.assertEqual(
            encoded_images.shape    ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )    ,      )







      def 		UpperCamelCase_	(    self   :       int	)					->  str:
        # Initialize image_processing
        _snake_case		=							self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        _snake_case		=							prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=A__    ,      torchify=A__	)
        for image in image_inputs:
          self.assertIsInstance(A__    ,      torch.Tensor	)

        # Test not batched input
        _snake_case		=							image_processing(image_inputs[0]    ,      return_tensors='''pt'''	).pixel_values
        self.assertEqual(
            encoded_images.shape    ,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )    ,      )

        # Test batched
        _snake_case		=							image_processing(A__    ,      return_tensors='''pt'''	).pixel_values
        self.assertEqual(
            encoded_images.shape    ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )    ,      )




__A    	=			{
    '''+''': lambda x, y: x + y,
    '''-''': lambda x, y: x - y,
    '''*''': lambda x, y: x * y,
    '''/''': lambda x, y: x / y,
}



def    snake_case_(_UpperCamelCase  )							->			str:



  """simple docstring"""






  return c.isdigit()



def    snake_case_(_UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case		=							[]

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

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

  return stack.pop()


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

 __A    	=			'''/ * 10 2 + 4 1 '''
 print(evaluate(test_expression))

