



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 MobileNetVaImageProcessor






class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def __init__(     self     :						List[str]						,  __UpperCAmelCase     :						List[str]						,  __UpperCAmelCase     :						int=7						,  __UpperCAmelCase     :						List[Any]=3						,  __UpperCAmelCase     :						List[Any]=18						,  __UpperCAmelCase     :						List[Any]=30						,  __UpperCAmelCase     :						List[Any]=400						,  __UpperCAmelCase     :						str=True						,  __UpperCAmelCase     :						str=None						,  __UpperCAmelCase     :						Optional[Any]=True						,  __UpperCAmelCase     :						Optional[int]=None						,  )      ->			Any:
							UpperCAmelCase_=       size if size is not None else {"""shortest_edge""": 20}
							UpperCAmelCase_=       crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
							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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Any:
							return {
							    "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 		lowercase						(			snake_case__			,   unittest.TestCase):






	"""simple docstring"""


	a__				: int		       =							MobileNetVaImageProcessor if is_vision_available() else None




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Optional[int]:
							UpperCAmelCase_=       MobileNetVaImageProcessingTester(self					)




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




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Optional[int]:
							UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(__UpperCAmelCase						,  """do_resize"""					)					)
							self.assertTrue(hasattr(__UpperCAmelCase						,  """size"""					)					)
							self.assertTrue(hasattr(__UpperCAmelCase						,  """do_center_crop"""					)					)
							self.assertTrue(hasattr(__UpperCAmelCase						,  """crop_size"""					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			List[str]:
							UpperCAmelCase_=       self.image_processing_class.from_dict(self.image_processor_dict					)
							self.assertEqual(image_processor.size						,  {"""shortest_edge""": 20}					)
							self.assertEqual(image_processor.crop_size						,  {"""height""": 18, """width""": 18}					)

							UpperCAmelCase_=       self.image_processing_class.from_dict(self.image_processor_dict						,  size=42						,  crop_size=84					)
							self.assertEqual(image_processor.size						,  {"""shortest_edge""": 42}					)
							self.assertEqual(image_processor.crop_size						,  {"""height""": 84, """width""": 84}					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Tuple:
							pass




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			str:
							# Initialize image_processing
							UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							UpperCAmelCase_=       prepare_image_inputs(self.image_processor_tester						,  equal_resolution=__UpperCAmelCase					)
							for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase						,  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(__UpperCAmelCase						,  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   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Tuple:
							# Initialize image_processing
							UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							UpperCAmelCase_=       prepare_image_inputs(self.image_processor_tester						,  equal_resolution=__UpperCAmelCase						,  numpify=__UpperCAmelCase					)
							for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase						,  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(__UpperCAmelCase						,  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   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			List[str]:
							# Initialize image_processing
							UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							UpperCAmelCase_=       prepare_image_inputs(self.image_processor_tester						,  equal_resolution=__UpperCAmelCase						,  torchify=__UpperCAmelCase					)
							for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase						,  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(__UpperCAmelCase						,  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"""],
							    )						,  )









__A       =						6_5521



def 	__a					(   lowerCAmelCase_       : str    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       1
						UpperCAmelCase_=       0
						for plain_chr in plain_text:
												UpperCAmelCase_=       (a + ord(lowerCAmelCase_    )) % MOD_ADLER
												UpperCAmelCase_=       (b + a) % MOD_ADLER
						return (b << 16) | a




