


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 MobileViTImageProcessor

class 							a						(unittest.TestCase ):




							"""simple docstring"""


							def __init__(				self			:    Dict       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    str=7       ,      lowerCamelCase			:    List[str]=3       ,      lowerCamelCase			:    int=18       ,      lowerCamelCase			:    List[str]=30       ,      lowerCamelCase			:    Any=400       ,      lowerCamelCase			:    Optional[int]=True       ,      lowerCamelCase			:    List[Any]=None       ,      lowerCamelCase			:    Tuple=True       ,      lowerCamelCase			:    Optional[int]=None       ,      lowerCamelCase			:    Any=True       ,      )      ->       str:
														__snake_case						:     Dict									=  size if size is not None else {"shortest_edge": 20}
														__snake_case						:     Dict									=  crop_size if crop_size is not None else {"height": 18, "width": 18}
														__snake_case						:     Optional[Any]									=  parent
														__snake_case						:     Optional[int]									=  batch_size
														__snake_case						:     Optional[Any]									=  num_channels
														__snake_case						:     List[str]									=  image_size
														__snake_case						:     Optional[int]									=  min_resolution
														__snake_case						:     Optional[Any]									=  max_resolution
														__snake_case						:     List[Any]									=  do_resize
														__snake_case						:     List[Any]									=  size
														__snake_case						:     Optional[int]									=  do_center_crop
														__snake_case						:     int									=  crop_size
														__snake_case						:     List[str]									=  do_flip_channel_order






							def        __snake_case   (				self			:    Tuple							)      ->       List[str]:
														return {
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_center_crop": self.do_center_crop,
														    "crop_size": self.crop_size,
														    "do_flip_channel_order": self.do_flip_channel_order,
														}

@require_torch
@require_vision
class 							a						(_lowerCAmelCase	,							unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Optional[int]	    =    MobileViTImageProcessor if is_vision_available() else None

							def        __snake_case   (				self			:    Union[str, Any]							)      ->       str:
														__snake_case						:     int									=  MobileViTImageProcessingTester(self							)

							@property
							def        __snake_case   (				self			:    Optional[Any]							)      ->       Tuple:
														return self.image_processor_tester.prepare_image_processor_dict()

							def        __snake_case   (				self			:    Tuple							)      ->       Any:
														__snake_case						:     str									=  self.image_processing_class(**self.image_processor_dict							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "do_resize"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "size"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "do_center_crop"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "center_crop"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "do_flip_channel_order"							)							)

							def        __snake_case   (				self			:    List[Any]							)      ->       Union[str, Any]:
														__snake_case						:     Optional[Any]									=  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}							)

														__snake_case						:     Union[str, Any]									=  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        __snake_case   (				self			:    Optional[Any]							)      ->       Optional[Any]:
														pass

							def        __snake_case   (				self			:    Union[str, Any]							)      ->       int:
														# Initialize image_processing
														__snake_case						:     List[str]									=  self.image_processing_class(**self.image_processor_dict							)
														# create random PIL images
														__snake_case						:     str									=  prepare_image_inputs(self.image_processor_tester       ,      equal_resolution=lowerCamelCase							)
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase       ,      Image.Image							)

														# Test not batched input
														__snake_case						:     Tuple									=  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
														__snake_case						:     Dict									=  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			:    Tuple							)      ->       List[Any]:
														# Initialize image_processing
														__snake_case						:     List[Any]									=  self.image_processing_class(**self.image_processor_dict							)
														# create random numpy tensors
														__snake_case						:     str									=  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
														__snake_case						:     Any									=  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
														__snake_case						:     Tuple									=  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			:    Dict							)      ->       int:
														# Initialize image_processing
														__snake_case						:     Dict									=  self.image_processing_class(**self.image_processor_dict							)
														# create random PyTorch tensors
														__snake_case						:     Optional[Any]									=  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
														__snake_case						:     Optional[Any]									=  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
														__snake_case						:     Any									=  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"""

def lowerCAmelCase_   (					UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 return x if y == 0 else greatest_common_divisor(UpperCamelCase__					, x % y			)


def lowerCAmelCase_   (					UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 return (x * y) // greatest_common_divisor(UpperCamelCase__					, UpperCamelCase__			)


def lowerCAmelCase_   (					UpperCamelCase__						:			int = 20			):






 """simple docstring"""







 __lowercase						=						1
 for i in range(1					, n + 1			):
  __lowercase						=						lcm(UpperCamelCase__					, UpperCamelCase__			)
 return g


if __name__ == "__main__":
       print(f"""{solution() = }""")
