





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)