"""simple docstring"""






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


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import TvltImageProcessor



def     a_		(   lowerCamelCase   ,     lowerCamelCase=1_0   ,     lowerCamelCase=1_0   ,     lowerCamelCase=False   ,     lowerCamelCase=False			):
				UpperCAmelCase__			 =				[]
				for i in range(image_processor_tester.num_frames			):
								video.append(np.random.randint(2_5_5   ,     size=(image_processor_tester.num_channels, width, height)   ,     dtype=np.uinta			)			)

				if not numpify and not torchify:
								# PIL expects the channel dimension as last dimension
								UpperCAmelCase__			 =				[Image.fromarray(np.moveaxis(A__   ,     0   ,     -1			)			) for frame in video]

				if torchify:
								UpperCAmelCase__			 =				[torch.from_numpy(A__			) for frame in video]

				return video



def     a_		(   lowerCamelCase   ,     lowerCamelCase=False   ,     lowerCamelCase=False   ,     lowerCamelCase=False			):
				assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

				UpperCAmelCase__			 =				[]
				for i in range(image_processor_tester.batch_size			):
								if equal_resolution:
												UpperCAmelCase__			 =				UpperCAmelCase__			 =				image_processor_tester.max_resolution
								else:
												UpperCAmelCase__ ,      UpperCAmelCase__			 =				np.random.choice(
												    np.arange(image_processor_tester.min_resolution   ,     image_processor_tester.max_resolution			)   ,     2			)
												UpperCAmelCase__			 =				prepare_video(
												    image_processor_tester=A__   ,     width=A__   ,     height=A__   ,     numpify=A__   ,     torchify=A__   ,     )
								video_inputs.append(A__			)

				return video_inputs
class 							snake_case			(					unittest.TestCase							):




					"""simple docstring"""

					def __init__(							self  :      Tuple       ,lowerCamelCase__  :      Dict       ,lowerCamelCase__  :      List[str]=7       ,lowerCamelCase__  :      str=3       ,lowerCamelCase__  :      Tuple=4       ,lowerCamelCase__  :      str=18       ,lowerCamelCase__  :      Dict=30       ,lowerCamelCase__  :      str=400       ,lowerCamelCase__  :      str=True       ,lowerCamelCase__  :      str=None       ,lowerCamelCase__  :      Tuple=True       ,lowerCamelCase__  :      List[Any]=[0.5, 0.5, 0.5]       ,lowerCamelCase__  :      Union[str, Any]=[0.5, 0.5, 0.5]       ,lowerCamelCase__  :      Dict=True       ,lowerCamelCase__  :      Optional[int]=None       ,):
									UpperCAmelCase__			 =				size if size is not None else {'shortest_edge': 18}
									UpperCAmelCase__			 =				crop_size if crop_size is not None else {'height': 18, 'width': 18}

									UpperCAmelCase__			 =				parent
									UpperCAmelCase__			 =				batch_size
									UpperCAmelCase__			 =				num_channels
									UpperCAmelCase__			 =				num_frames
									UpperCAmelCase__			 =				image_size
									UpperCAmelCase__			 =				min_resolution
									UpperCAmelCase__			 =				max_resolution
									UpperCAmelCase__			 =				do_resize
									UpperCAmelCase__			 =				size
									UpperCAmelCase__			 =				do_normalize
									UpperCAmelCase__			 =				image_mean
									UpperCAmelCase__			 =				image_std
									UpperCAmelCase__			 =				do_center_crop
									UpperCAmelCase__			 =				crop_size







					def 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									}






@require_torch
@require_vision
class 							snake_case			(					A_ ,       unittest.TestCase							):




					"""simple docstring"""

					snake_case__							       =					TvltImageProcessor if is_vision_available() else None







					def 						__lowerCAmelCase		(							self  :      Any     ):
									UpperCAmelCase__			 =				TvltImageProcessorTester(self     )







					@property
					def 						__lowerCAmelCase		(							self  :      List[Any]     ):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 						__lowerCAmelCase		(							self  :      str     ):
									UpperCAmelCase__			 =				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       ,'do_center_crop'     )     )
									self.assertTrue(hasattr(_A       ,'size'     )     )







					def 						__lowerCAmelCase		(							self  :      List[str]     ):
									# Initialize image_processor
									UpperCAmelCase__			 =				self.image_processing_class(**self.image_processor_dict     )
									# create random PIL videos
									UpperCAmelCase__			 =				prepare_video_inputs(self.image_processor_tester       ,equal_resolution=_A     )
									for video in video_inputs:
													self.assertIsInstance(_A       ,_A     )
													self.assertIsInstance(video[0]       ,Image.Image     )

									# Test not batched input
									UpperCAmelCase__			 =				image_processor(video_inputs[0]       ,return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_videos.shape       ,(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )       ,)

									# Test batched
									UpperCAmelCase__			 =				image_processor(_A       ,return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_videos.shape       ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )       ,)







					def 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									# Initialize image_processor
									UpperCAmelCase__			 =				self.image_processing_class(**self.image_processor_dict     )
									# create random numpy tensors
									UpperCAmelCase__			 =				prepare_video_inputs(self.image_processor_tester       ,equal_resolution=_A       ,numpify=_A     )
									for video in video_inputs:
													self.assertIsInstance(_A       ,_A     )
													self.assertIsInstance(video[0]       ,np.ndarray     )

									# Test not batched input
									UpperCAmelCase__			 =				image_processor(video_inputs[0]       ,return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_videos.shape       ,(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )       ,)

									# Test batched
									UpperCAmelCase__			 =				image_processor(_A       ,return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_videos.shape       ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )       ,)







					def 						__lowerCAmelCase		(							self  :      List[Any]     ):
									# Initialize image_processor
									UpperCAmelCase__			 =				self.image_processing_class(**self.image_processor_dict     )
									# create random PyTorch tensors
									UpperCAmelCase__			 =				prepare_video_inputs(self.image_processor_tester       ,equal_resolution=_A       ,torchify=_A     )
									for video in video_inputs:
													self.assertIsInstance(_A       ,_A     )
													self.assertIsInstance(video[0]       ,torch.Tensor     )

									# Test not batched input
									UpperCAmelCase__			 =				image_processor(video_inputs[0]       ,return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_videos.shape       ,(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )       ,)

									# Test batched
									UpperCAmelCase__			 =				image_processor(_A       ,return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_videos.shape       ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )       ,)





_A       =					{
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}



def 		lowercase_     (  A__						)				->				Union[str, Any]:
    """simple docstring"""

    return c.isdigit()



def 		lowercase_     (  A__						)				->				int:
    """simple docstring"""

    snake_case						=   []

    # 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
            snake_case						=   stack.pop()
            snake_case						=   stack.pop()
            stack.append(calc[c](A__					,						A__						)						)

    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))

