
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 		lowercase_     (  A__					,						A__=10					,						A__=10					,						A__=False					,						A__=False						)				->				Any:
    """simple docstring"""

    snake_case						=   []
    for i in range(image_processor_tester.num_frames						):
        video.append(np.random.randint(255					,						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
        snake_case						=   [Image.fromarray(np.moveaxis(A__					,						0					,						-1						)						) for frame in video]

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

    return video



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

    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

    return video_inputs







class 	lowerCamelCase						(    unittest.TestCase    ):





     def __init__(self :       Tuple							,       _A :       Dict							,       _A :       List[str]=7							,       _A :       str=3							,       _A :       Tuple=4							,       _A :       str=1_8							,       _A :       Dict=3_0							,       _A :       str=4_0_0							,       _A :       str=True							,       _A :       str=None							,       _A :       Tuple=True							,       _A :       List[Any]=[0.5, 0.5, 0.5]							,       _A :       Union[str, Any]=[0.5, 0.5, 0.5]							,       _A :       Dict=True							,       _A :       Optional[int]=None							,       )		->		List[str]:
         snake_case						=   size if size is not None else {"shortest_edge": 1_8}
         snake_case						=   crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}

         snake_case						=   parent
         snake_case						=   batch_size
         snake_case						=   num_channels
         snake_case						=   num_frames
         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
         snake_case						=   do_center_crop
         snake_case						=   crop_size





     def 						UpperCAmelCase(self :       Optional[Any]  )		->		Optional[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,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
         }







@require_torch
@require_vision
class 	lowerCamelCase						(    A_				,					unittest.TestCase    ):
     UpperCAmelCase__							:				int 	=						TvltImageProcessor if is_vision_available() else None





     def 						UpperCAmelCase(self :       Any  )		->		List[Any]:
         snake_case						=   TvltImageProcessorTester(self  )





     @property
     def 						UpperCAmelCase(self :       List[Any]  )		->		str:
         return self.image_processor_tester.prepare_image_processor_dict()





     def 						UpperCAmelCase(self :       str  )		->		str:
         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							,       "do_center_crop"  )  )
         self.assertTrue(hasattr(_A							,       "size"  )  )





     def 						UpperCAmelCase(self :       List[str]  )		->		str:
         # Initialize image_processor
         snake_case						=   self.image_processing_class(**self.image_processor_dict  )
         # create random PIL videos
         snake_case						=   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
         snake_case						=   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
         snake_case						=   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 						UpperCAmelCase(self :       Optional[Any]  )		->		Union[str, Any]:
         # Initialize image_processor
         snake_case						=   self.image_processing_class(**self.image_processor_dict  )
         # create random numpy tensors
         snake_case						=   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
         snake_case						=   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
         snake_case						=   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 						UpperCAmelCase(self :       List[Any]  )		->		Union[str, Any]:
         # Initialize image_processor
         snake_case						=   self.image_processing_class(**self.image_processor_dict  )
         # create random PyTorch tensors
         snake_case						=   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
         snake_case						=   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
         snake_case						=   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))

