
"""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, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor
class 						SCREAMING_SNAKE_CASE_ (						unittest.TestCase ):




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__=7							,  lowerCAmelCase__=3							,  lowerCAmelCase__=1_8							,  lowerCAmelCase__=3_0							,  lowerCAmelCase__=4_0_0							,  lowerCAmelCase__=True							,  lowerCAmelCase__=None							,  lowerCAmelCase__=True							,  lowerCAmelCase__=None							,  lowerCAmelCase__=True							,  lowerCAmelCase__=[0.5, 0.5, 0.5]							,  lowerCAmelCase__=[0.5, 0.5, 0.5]							,  ):
				__SCREAMING_SNAKE_CASE            = size if size is not None else {"""shortest_edge""": 1_8}
				__SCREAMING_SNAKE_CASE            = crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}
				__SCREAMING_SNAKE_CASE            = parent
				__SCREAMING_SNAKE_CASE            = batch_size
				__SCREAMING_SNAKE_CASE            = num_channels
				__SCREAMING_SNAKE_CASE            = image_size
				__SCREAMING_SNAKE_CASE            = min_resolution
				__SCREAMING_SNAKE_CASE            = max_resolution
				__SCREAMING_SNAKE_CASE            = do_resize
				__SCREAMING_SNAKE_CASE            = size
				__SCREAMING_SNAKE_CASE            = do_center_crop
				__SCREAMING_SNAKE_CASE            = crop_size
				__SCREAMING_SNAKE_CASE            = do_normalize
				__SCREAMING_SNAKE_CASE            = image_mean
				__SCREAMING_SNAKE_CASE            = image_std




		def 	snake_case_     (     self):
				return {
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_normalize": self.do_normalize,
				    "do_resize": self.do_resize,
				    "do_center_crop": self.do_center_crop,
				    "size": self.size,
				    "crop_size": self.crop_size,
				}



@require_torch
@require_vision
class 						SCREAMING_SNAKE_CASE_ (						__a     ,				unittest.TestCase ):




		"""simple docstring"""





		__lowercase							:  Union[str, Any]						   =		LevitImageProcessor if is_vision_available() else None




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = LevitImageProcessingTester(self)




		@property
		def 	snake_case_     (     self):
				return self.image_processor_tester.prepare_image_processor_dict()




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.image_processing_class(**self.image_processor_dict)
				self.assertTrue(hasattr(lowerCAmelCase__							,  """image_mean"""))
				self.assertTrue(hasattr(lowerCAmelCase__							,  """image_std"""))
				self.assertTrue(hasattr(lowerCAmelCase__							,  """do_normalize"""))
				self.assertTrue(hasattr(lowerCAmelCase__							,  """do_resize"""))
				self.assertTrue(hasattr(lowerCAmelCase__							,  """do_center_crop"""))
				self.assertTrue(hasattr(lowerCAmelCase__							,  """size"""))




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.image_processing_class.from_dict(self.image_processor_dict)
				self.assertEqual(image_processor.size							,  {"""shortest_edge""": 1_8})
				self.assertEqual(image_processor.crop_size							,  {"""height""": 1_8, """width""": 1_8})

				__SCREAMING_SNAKE_CASE            = 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 	snake_case_     (     self):
				pass




		def 	snake_case_     (     self):
				# Initialize image_processing
				__SCREAMING_SNAKE_CASE            = self.image_processing_class(**self.image_processor_dict)
				# create random PIL images
				__SCREAMING_SNAKE_CASE            = prepare_image_inputs(self.image_processor_tester							,  equal_resolution=lowerCAmelCase__)
				for image in image_inputs:
						self.assertIsInstance(lowerCAmelCase__							,  Image.Image)

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

				# Test batched
				__SCREAMING_SNAKE_CASE            = image_processing(lowerCAmelCase__							,  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 	snake_case_     (     self):
				# Initialize image_processing
				__SCREAMING_SNAKE_CASE            = self.image_processing_class(**self.image_processor_dict)
				# create random numpy tensors
				__SCREAMING_SNAKE_CASE            = prepare_image_inputs(self.image_processor_tester							,  equal_resolution=lowerCAmelCase__							,  numpify=lowerCAmelCase__)
				for image in image_inputs:
						self.assertIsInstance(lowerCAmelCase__							,  np.ndarray)

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

				# Test batched
				__SCREAMING_SNAKE_CASE            = image_processing(lowerCAmelCase__							,  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 	snake_case_     (     self):
				# Initialize image_processing
				__SCREAMING_SNAKE_CASE            = self.image_processing_class(**self.image_processor_dict)
				# create random PyTorch tensors
				__SCREAMING_SNAKE_CASE            = prepare_image_inputs(self.image_processor_tester							,  equal_resolution=lowerCAmelCase__							,  torchify=lowerCAmelCase__)
				for image in image_inputs:
						self.assertIsInstance(lowerCAmelCase__							,  torch.Tensor)

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

				# Test batched
				__SCREAMING_SNAKE_CASE            = image_processing(lowerCAmelCase__							,  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"""],
				    )							,  )






"""simple docstring"""





__magic_name__       =			"Tobias Carryer"

from time import time
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__=int(time())):  # noqa: B008
				__SCREAMING_SNAKE_CASE            = multiplier
				__SCREAMING_SNAKE_CASE            = increment
				__SCREAMING_SNAKE_CASE            = modulo
				__SCREAMING_SNAKE_CASE            = seed




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = (self.multiplier * self.seed + self.increment) % self.modulo
				return self.seed


if __name__ == "__main__":
							# Show the LCG in action.
							__magic_name__       =			LinearCongruentialGenerator(1664525, 1013904223, 2 << 31)
							while True:
														print(lcg.next_number())




