
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       _snake_case					(     unittest.TestCase				):







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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				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       _snake_case					(     snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    MobileViTImageProcessor if is_vision_available() else None







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		str	   =     MobileViTImageProcessingTester(self		)







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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     self.image_processing_class(**self.image_processor_dict		)
				self.assertTrue(hasattr(_a						,   "do_resize"		)		)
				self.assertTrue(hasattr(_a						,   "size"		)		)
				self.assertTrue(hasattr(_a						,   "do_center_crop"		)		)
				self.assertTrue(hasattr(_a						,   "center_crop"		)		)
				self.assertTrue(hasattr(_a						,   "do_flip_channel_order"		)		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[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}		)

				__magic_name__		:		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 			SCREAMING_SNAKE_CASE    (				self		):
				pass







			def 			SCREAMING_SNAKE_CASE    (				self		):
				# Initialize image_processing
				__magic_name__		:		Any	   =     self.image_processing_class(**self.image_processor_dict		)
				# create random PIL images
				__magic_name__		:		Optional[int]	   =     prepare_image_inputs(self.image_processor_tester						,   equal_resolution=_a		)
				for image in image_inputs:
					self.assertIsInstance(_a						,   Image.Image		)

				# Test not batched input
				__magic_name__		:		str	   =     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
				__magic_name__		:		List[Any]	   =     image_processing(_a						,   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		):
				# Initialize image_processing
				__magic_name__		:		List[Any]	   =     self.image_processing_class(**self.image_processor_dict		)
				# create random numpy tensors
				__magic_name__		:		Optional[Any]	   =     prepare_image_inputs(self.image_processor_tester						,   equal_resolution=_a						,   numpify=_a		)
				for image in image_inputs:
					self.assertIsInstance(_a						,   np.ndarray		)

				# Test not batched input
				__magic_name__		:		int	   =     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
				__magic_name__		:		Dict	   =     image_processing(_a						,   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		):
				# Initialize image_processing
				__magic_name__		:		Optional[Any]	   =     self.image_processing_class(**self.image_processor_dict		)
				# create random PyTorch tensors
				__magic_name__		:		List[str]	   =     prepare_image_inputs(self.image_processor_tester						,   equal_resolution=_a						,   torchify=_a		)
				for image in image_inputs:
					self.assertIsInstance(_a						,   torch.Tensor		)

				# Test not batched input
				__magic_name__		:		Union[str, 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
				__magic_name__		:		Optional[Any]	   =     image_processing(_a						,   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"],
				    )						,   )


import math






def   lowerCAmelCase_						(					_snake_case      :  float      ,   _snake_case      :  float   )				->		float:







	'''simple docstring'''


	return math.pow(_snake_case      ,   2   ) - a






def   lowerCAmelCase_						(					_snake_case      :  float   )				->		float:







	'''simple docstring'''


	return 2 * x






def   lowerCAmelCase_						(					_snake_case      :  float   )				->		float:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     2.0

	while start <= a:
		__magic_name__		:		str	   =     math.pow(_snake_case      ,   2   )

	return start






def   lowerCAmelCase_						(					_snake_case      :  float      ,   _snake_case      :  int = 9999      ,   _snake_case      :  float = 0.00_000_000_000_001   )				->		float:







	'''simple docstring'''


	if a < 0:
		raise ValueError("math domain error"   )

	__magic_name__		:		Optional[int]	   =     get_initial_point(_snake_case   )

	for _ in range(_snake_case   ):
		__magic_name__		:		int	   =     value
		__magic_name__		:		str	   =     value - fx(_snake_case      ,   _snake_case   ) / fx_derivative(_snake_case   )
		if abs(prev_value - value   ) < tolerance:
			return value

	return value


if __name__ == "__main__":
				from doctest import testmod

				testmod()
