





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 					UpperCamelCase_    (       unittest.TestCase      ):


							def __init__(							self      :int							,			__A      :Optional[int]							,			__A      :Dict=7							,			__A      :int=3							,			__A      :List[str]=18							,			__A      :str=30							,			__A      :List[str]=400							,			__A      :Optional[int]=True							,			__A      :Union[str, Any]=None							,			__A      :Union[str, Any]=True							,			__A      :Union[str, Any]=[0.5, 0.5, 0.5]							,			__A      :int=[0.5, 0.5, 0.5]							,			)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= size if size is not None else {"""height""": 18, """width""": 18}
									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_normalize
									SCREAMING_SNAKE_CASE__								= image_mean
									SCREAMING_SNAKE_CASE__								= image_std


							def        _snake_case					(							self      :List[Any]						)				->				Tuple:



									"""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 					UpperCamelCase_    (       UpperCamelCase__			,					unittest.TestCase      ):
							lowerCamelCase_          =   ViTImageProcessor if is_vision_available() else None


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= ViTImageProcessingTester(self						)


							@property
							def        _snake_case					(							self      :Optional[Any]						)				->				Optional[Any]:



									"""simple docstring"""



									return self.image_processor_tester.prepare_image_processor_dict()


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



									"""simple docstring"""



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


							def        _snake_case					(							self      :int						)				->				Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.image_processing_class.from_dict(self.image_processor_dict						)
									self.assertEqual(image_processor.size							,			{"""height""": 18, """width""": 18}						)

									SCREAMING_SNAKE_CASE__								= self.image_processing_class.from_dict(self.image_processor_dict							,			size=42						)
									self.assertEqual(image_processor.size							,			{"""height""": 42, """width""": 42}						)


							def        _snake_case					(							self      :Union[str, Any]						)				->				Optional[int]:



									"""simple docstring"""



									pass


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



									"""simple docstring"""



									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=__A						)
									for image in image_inputs:
											self.assertIsInstance(__A							,			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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )							,			)

									# Test batched
									SCREAMING_SNAKE_CASE__								= 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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )							,			)


							def        _snake_case					(							self      :Dict						)				->				Any:



									"""simple docstring"""



									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=__A							,			numpify=__A						)
									for image in image_inputs:
											self.assertIsInstance(__A							,			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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )							,			)

									# Test batched
									SCREAMING_SNAKE_CASE__								= 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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )							,			)


							def        _snake_case					(							self      :List[str]						)				->				int:



									"""simple docstring"""



									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=__A							,			torchify=__A						)
									for image in image_inputs:
											self.assertIsInstance(__A							,			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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )							,			)

									# Test batched
									SCREAMING_SNAKE_CASE__								= 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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )							,			)






from __future__ import annotations






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							list[int | float]      ):
		if len(UpperCamelCase__      ) == 0:
				raise ValueError("""find_min() arg is an empty sequence"""      )
		SCREAMING_SNAKE_CASE__								= nums[0]
		for num in nums:
				SCREAMING_SNAKE_CASE__								= min(UpperCamelCase__   ,				UpperCamelCase__      )
		return min_num


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)