



'''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 ChineseCLIPImageProcessor
class 							UpperCAmelCase__	(     unittest.TestCase):



 def __init__(	self		,						lowercase		,						lowercase=7		,						lowercase=3		,						lowercase=1_8		,						lowercase=3_0		,						lowercase=4_0_0		,						lowercase=True		,						lowercase=None		,						lowercase=True		,						lowercase=None		,						lowercase=True		,						lowercase=[0.48_145_466, 0.4_578_275, 0.40_821_073]		,						lowercase=[0.26_862_954, 0.26_130_258, 0.27_577_711]		,						lowercase=True		,						)	->				Dict:
  __UpperCamelCase					   =	size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
  __UpperCamelCase					   =	crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}
  __UpperCamelCase					   =	parent
  __UpperCamelCase					   =	batch_size
  __UpperCamelCase					   =	num_channels
  __UpperCamelCase					   =	image_size
  __UpperCamelCase					   =	min_resolution
  __UpperCamelCase					   =	max_resolution
  __UpperCamelCase					   =	do_resize
  __UpperCamelCase					   =	size
  __UpperCamelCase					   =	do_center_crop
  __UpperCamelCase					   =	crop_size
  __UpperCamelCase					   =	do_normalize
  __UpperCamelCase					   =	image_mean
  __UpperCamelCase					   =	image_std
  __UpperCamelCase					   =	do_convert_rgb



 def  __lowerCamelCase					(	self      )	->				List[str]:
  return {
      "do_resize": self.do_resize,
      "size": self.size,
      "do_center_crop": self.do_center_crop,
      "crop_size": self.crop_size,
      "do_normalize": self.do_normalize,
      "image_mean": self.image_mean,
      "image_std": self.image_std,
      "do_convert_rgb": self.do_convert_rgb,
  }




 def  __lowerCamelCase					(	self		,						lowercase=False		,						lowercase=False		,						lowercase=False      )	->				Union[str, Any]:
  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

  if equal_resolution:
   __UpperCamelCase					   =	[]
   for i in range(self.batch_size      ):
    image_inputs.append(
        np.random.randint(
            2_5_5		,						size=(self.num_channels, self.max_resolution, self.max_resolution)		,						dtype=np.uinta      )      )
  else:
   __UpperCamelCase					   =	[]
   for i in range(self.batch_size      ):
    __UpperCamelCase   ,		__UpperCamelCase					   =	np.random.choice(np.arange(self.min_resolution		,						self.max_resolution      )		,						2      )
    image_inputs.append(np.random.randint(2_5_5		,						size=(self.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(_snake_case		,						0		,						-1      )      ) for x in image_inputs]

  if torchify:
   __UpperCamelCase					   =	[torch.from_numpy(_snake_case      ) for x in image_inputs]

  return image_inputs


@require_torch
@require_vision
class 							UpperCAmelCase__	(     UpperCAmelCase_				,      unittest.TestCase):
 __SCREAMING_SNAKE_CASE          =					ChineseCLIPImageProcessor if is_vision_available() else None



 def  __lowerCamelCase					(	self      )	->				Any:
  __UpperCamelCase					   =	ChineseCLIPImageProcessingTester(self		,						do_center_crop=_snake_case      )



 @property
 def  __lowerCamelCase					(	self      )	->				Optional[int]:
  return self.image_processor_tester.prepare_image_processor_dict()



 def  __lowerCamelCase					(	self      )	->				int:
  __UpperCamelCase					   =	self.image_processing_class(**self.image_processor_dict      )
  self.assertTrue(hasattr(_snake_case		,						"""do_resize"""      )      )
  self.assertTrue(hasattr(_snake_case		,						"""size"""      )      )
  self.assertTrue(hasattr(_snake_case		,						"""do_center_crop"""      )      )
  self.assertTrue(hasattr(_snake_case		,						"""center_crop"""      )      )
  self.assertTrue(hasattr(_snake_case		,						"""do_normalize"""      )      )
  self.assertTrue(hasattr(_snake_case		,						"""image_mean"""      )      )
  self.assertTrue(hasattr(_snake_case		,						"""image_std"""      )      )
  self.assertTrue(hasattr(_snake_case		,						"""do_convert_rgb"""      )      )



 def  __lowerCamelCase					(	self      )	->				Optional[int]:
  __UpperCamelCase					   =	self.image_processing_class.from_dict(self.image_processor_dict      )
  self.assertEqual(image_processor.size		,						{"""height""": 2_2_4, """width""": 2_2_4}      )
  self.assertEqual(image_processor.crop_size		,						{"""height""": 1_8, """width""": 1_8}      )

  __UpperCamelCase					   =	self.image_processing_class.from_dict(self.image_processor_dict		,						size=4_2		,						crop_size=8_4      )
  self.assertEqual(image_processor.size		,						{"""shortest_edge""": 4_2}      )
  self.assertEqual(image_processor.crop_size		,						{"""height""": 8_4, """width""": 8_4}      )



 def  __lowerCamelCase					(	self      )	->				Dict:
  pass



 def  __lowerCamelCase					(	self      )	->				Union[str, Any]:
  __UpperCamelCase					   =	self.image_processing_class(**self.image_processor_dict      )
  # create random PIL images
  __UpperCamelCase					   =	self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case      )
  for image in image_inputs:
   self.assertIsInstance(_snake_case		,						Image.Image      )

  # Test not batched input
  __UpperCamelCase					   =	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.crop_size["""height"""],
          self.image_processor_tester.crop_size["""width"""],
      )		,						)

  # Test batched
  __UpperCamelCase					   =	image_processing(_snake_case		,						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.crop_size["""height"""],
          self.image_processor_tester.crop_size["""width"""],
      )		,						)



 def  __lowerCamelCase					(	self      )	->				Any:
  __UpperCamelCase					   =	self.image_processing_class(**self.image_processor_dict      )
  # create random numpy tensors
  __UpperCamelCase					   =	self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case		,						numpify=_snake_case      )
  for image in image_inputs:
   self.assertIsInstance(_snake_case		,						np.ndarray      )

  # Test not batched input
  __UpperCamelCase					   =	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.crop_size["""height"""],
          self.image_processor_tester.crop_size["""width"""],
      )		,						)

  # Test batched
  __UpperCamelCase					   =	image_processing(_snake_case		,						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.crop_size["""height"""],
          self.image_processor_tester.crop_size["""width"""],
      )		,						)




 def  __lowerCamelCase					(	self      )	->				str:
  __UpperCamelCase					   =	self.image_processing_class(**self.image_processor_dict      )
  # create random PyTorch tensors
  __UpperCamelCase					   =	self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case		,						torchify=_snake_case      )
  for image in image_inputs:
   self.assertIsInstance(_snake_case		,						torch.Tensor      )

  # Test not batched input
  __UpperCamelCase					   =	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.crop_size["""height"""],
          self.image_processor_tester.crop_size["""width"""],
      )		,						)

  # Test batched
  __UpperCamelCase					   =	image_processing(_snake_case		,						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.crop_size["""height"""],
          self.image_processor_tester.crop_size["""width"""],
      )		,						)


@require_torch
@require_vision
class 							UpperCAmelCase__	(     UpperCAmelCase_				,      unittest.TestCase):
 __SCREAMING_SNAKE_CASE          =					ChineseCLIPImageProcessor if is_vision_available() else None



 def  __lowerCamelCase					(	self      )	->				Optional[int]:
  __UpperCamelCase					   =	ChineseCLIPImageProcessingTester(self		,						num_channels=4		,						do_center_crop=_snake_case      )
  __UpperCamelCase					   =	3



 @property
 def  __lowerCamelCase					(	self      )	->				Tuple:
  return self.image_processor_tester.prepare_image_processor_dict()



 def  __lowerCamelCase					(	self      )	->				List[str]:
  __UpperCamelCase					   =	self.image_processing_class(**self.image_processor_dict      )
  self.assertTrue(hasattr(_snake_case		,						"""do_resize"""      )      )
  self.assertTrue(hasattr(_snake_case		,						"""size"""      )      )
  self.assertTrue(hasattr(_snake_case		,						"""do_center_crop"""      )      )
  self.assertTrue(hasattr(_snake_case		,						"""center_crop"""      )      )
  self.assertTrue(hasattr(_snake_case		,						"""do_normalize"""      )      )
  self.assertTrue(hasattr(_snake_case		,						"""image_mean"""      )      )
  self.assertTrue(hasattr(_snake_case		,						"""image_std"""      )      )
  self.assertTrue(hasattr(_snake_case		,						"""do_convert_rgb"""      )      )



 def  __lowerCamelCase					(	self      )	->				List[Any]:
  pass




 def  __lowerCamelCase					(	self      )	->				Optional[Any]:
  __UpperCamelCase					   =	self.image_processing_class(**self.image_processor_dict      )
  # create random PIL images
  __UpperCamelCase					   =	self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case      )
  for image in image_inputs:
   self.assertIsInstance(_snake_case		,						Image.Image      )

  # Test not batched input
  __UpperCamelCase					   =	image_processing(image_inputs[0]		,						return_tensors="""pt"""      ).pixel_values
  self.assertEqual(
      encoded_images.shape		,						(
          1,
          self.expected_encoded_image_num_channels,
          self.image_processor_tester.crop_size["""height"""],
          self.image_processor_tester.crop_size["""width"""],
      )		,						)

  # Test batched
  __UpperCamelCase					   =	image_processing(_snake_case		,						return_tensors="""pt"""      ).pixel_values
  self.assertEqual(
      encoded_images.shape		,						(
          self.image_processor_tester.batch_size,
          self.expected_encoded_image_num_channels,
          self.image_processor_tester.crop_size["""height"""],
          self.image_processor_tester.crop_size["""width"""],
      )		,						)










from __future__ import annotations

from typing import Any


def A    (							__UpperCamelCase       )    ->   int:
			if not postfix_notation:
						return 0

			A__	  =     {'+', '-', '*', '/'}
			A__	  =     []

			for token in postfix_notation:
						if token in operations:
									A__  ,			A__	  =     stack.pop(), stack.pop()
									if token == "+":
												stack.append(a + b       )
									elif token == "-":
												stack.append(a - b       )
									elif token == "*":
												stack.append(a * b       )
									else:
												if a * b < 0 and a % b != 0:
															stack.append(a // b + 1       )
												else:
															stack.append(a // b       )
						else:
									stack.append(int(__UpperCamelCase       )       )

			return stack.pop()


if __name__ == "__main__":
							import doctest

							doctest.testmod()




