"""simple docstring"""




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 MobileNetVaImageProcessor
class 						__A							(			unittest.TestCase		):






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






					def    __A  (		self  ):
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							}







@require_torch
@require_vision
class 						__A							(			_snake_case   ,     unittest.TestCase		):
					_UpperCamelCase							:      List[str]		 =		MobileNetVaImageProcessor if is_vision_available() else None






					def    __A  (		self  ):
							_lowerCAmelCase						:	Optional[int]        =					MobileNetVaImageProcessingTester(self  )






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






					def    __A  (		self  ):
							_lowerCAmelCase						:	int        =					self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(UpperCamelCase__    ,					"""do_resize"""  )  )
							self.assertTrue(hasattr(UpperCamelCase__    ,					"""size"""  )  )
							self.assertTrue(hasattr(UpperCamelCase__    ,					"""do_center_crop"""  )  )
							self.assertTrue(hasattr(UpperCamelCase__    ,					"""crop_size"""  )  )






					def    __A  (		self  ):
							_lowerCAmelCase						:	Union[str, 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}  )

							_lowerCAmelCase						:	Optional[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    __A  (		self  ):
							pass






					def    __A  (		self  ):
							_lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							_lowerCAmelCase						:	Tuple        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=UpperCamelCase__  )
							for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__    ,					Image.Image  )

							# Test not batched input
							_lowerCAmelCase						:	List[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
							_lowerCAmelCase						:	Tuple        =					image_processing(UpperCamelCase__    ,					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    __A  (		self  ):
							_lowerCAmelCase						:	str        =					self.image_processing_class(**self.image_processor_dict  )
							# create random numpy tensors
							_lowerCAmelCase						:	Any        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=UpperCamelCase__    ,					numpify=UpperCamelCase__  )
							for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__    ,					np.ndarray  )

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

							# Test not batched input
							_lowerCAmelCase						:	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
							_lowerCAmelCase						:	Optional[int]        =					image_processing(UpperCamelCase__    ,					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"""




from __future__ import annotations





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	list     )	->				float:
  if not nums:
    raise ValueError("""List is empty"""     )
  return sum(_lowerCamelCase     ) / len(_lowerCamelCase     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




