






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 ViTImageProcessor


class      lowercase__     (			unittest.TestCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase=13		,    __UpperCAmelCase=3		,    __UpperCAmelCase=224		,    __UpperCAmelCase=30		,    __UpperCAmelCase=400		,    __UpperCAmelCase=True		,    __UpperCAmelCase=None		,    __UpperCAmelCase=True		,    __UpperCAmelCase=[0.5, 0.5, 0.5]		,    __UpperCAmelCase=[0.5, 0.5, 0.5]		,    )->					Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						size if size is not None else {"height": 18, "width": 18}
							lowerCAmelCase__       =						parent
							lowerCAmelCase__       =						batch_size
							lowerCAmelCase__       =						num_channels
							lowerCAmelCase__       =						image_size
							lowerCAmelCase__       =						min_resolution
							lowerCAmelCase__       =						max_resolution
							lowerCAmelCase__       =						do_resize
							lowerCAmelCase__       =						size
							lowerCAmelCase__       =						do_normalize
							lowerCAmelCase__       =						image_mean
							lowerCAmelCase__       =						image_std


	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							}





@require_torch
@require_vision
class      lowercase__     (			_UpperCAmelCase,					unittest.TestCase	):
	a_         =ViTImageProcessor if is_vision_available() else None

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						EfficientFormerImageProcessorTester(self       )

	@property
	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							return self.image_proc_tester.prepare_image_processor_dict()

	def 		UpperCAmelCase  (							self       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(__UpperCAmelCase		,    "image_mean"       )       )
							self.assertTrue(hasattr(__UpperCAmelCase		,    "image_std"       )       )
							self.assertTrue(hasattr(__UpperCAmelCase		,    "do_normalize"       )       )
							self.assertTrue(hasattr(__UpperCAmelCase		,    "do_resize"       )       )
							self.assertTrue(hasattr(__UpperCAmelCase		,    "size"       )       )

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							pass

	def 		UpperCAmelCase  (							self       )->					Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							lowerCAmelCase__       =						prepare_image_inputs(self.image_proc_tester		,    equal_resolution=__UpperCAmelCase       )
							for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase		,    Image.Image       )

							# Test not batched input
							lowerCAmelCase__       =						image_processor(image_inputs[0]		,    return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape		,    (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["height"],
							        self.image_proc_tester.size["width"],
							    )		,    )

							# Test batched
							lowerCAmelCase__       =						image_processor(__UpperCAmelCase		,    return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape		,    (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["height"],
							        self.image_proc_tester.size["width"],
							    )		,    )

	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							lowerCAmelCase__       =						prepare_image_inputs(self.image_proc_tester		,    equal_resolution=__UpperCAmelCase		,    numpify=__UpperCAmelCase       )
							for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase		,    np.ndarray       )

							# Test not batched input
							lowerCAmelCase__       =						image_processor(image_inputs[0]		,    return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape		,    (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["height"],
							        self.image_proc_tester.size["width"],
							    )		,    )

							# Test batched
							lowerCAmelCase__       =						image_processor(__UpperCAmelCase		,    return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape		,    (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["height"],
							        self.image_proc_tester.size["width"],
							    )		,    )


	def 		UpperCAmelCase  (							self       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							lowerCAmelCase__       =						prepare_image_inputs(self.image_proc_tester		,    equal_resolution=__UpperCAmelCase		,    torchify=__UpperCAmelCase       )
							for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase		,    torch.Tensor       )

							# Test not batched input
							lowerCAmelCase__       =						image_processor(image_inputs[0]		,    return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape		,    (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["height"],
							        self.image_proc_tester.size["width"],
							    )		,    )

							# Test batched
							lowerCAmelCase__       =						image_processor(__UpperCAmelCase		,    return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape		,    (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["height"],
							        self.image_proc_tester.size["width"],
							    )		,    )














def        _a  (     UpperCamelCase_					:				list    ,   UpperCamelCase_					:				list   ) ->   float:
						"""simple docstring"""







						_validate_point(UpperCamelCase_   )
						_validate_point(UpperCamelCase_   )
						if len(UpperCamelCase_   ) != len(UpperCamelCase_   ):
												raise ValueError("Both points must be in the same n-dimensional space"   )

						return float(sum(abs(a - b   ) for a, b in zip(UpperCamelCase_    ,   UpperCamelCase_   )   )   )


def        _a  (     UpperCamelCase_					:				list[float]   ) ->   None:
						"""simple docstring"""







						if point:
												if isinstance(UpperCamelCase_    ,   UpperCamelCase_   ):
																		for item in point:
																								if not isinstance(UpperCamelCase_    ,   (int, float)   ):
																														lowerCAmelCase__       =						(
																														    "Expected a list of numbers as input, found "
																														    F"{type(UpperCamelCase_   ).__name__}"
																														)
																														raise TypeError(UpperCamelCase_   )
												else:
																		lowerCAmelCase__       =						F"Expected a list of numbers as input, found {type(UpperCamelCase_   ).__name__}"
																		raise TypeError(UpperCamelCase_   )
						else:
												raise ValueError("Missing an input"   )


def        _a  (     UpperCamelCase_					:				list    ,   UpperCamelCase_					:				list   ) ->   float:
						"""simple docstring"""







						_validate_point(UpperCamelCase_   )
						_validate_point(UpperCamelCase_   )
						if len(UpperCamelCase_   ) != len(UpperCamelCase_   ):
												raise ValueError("Both points must be in the same n-dimensional space"   )

						return float(sum(abs(x - y   ) for x, y in zip(UpperCamelCase_    ,   UpperCamelCase_   )   )   )


if __name__ == "__main__":
	import doctest

	doctest.testmod()






