



'''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 			_SCREAMING_SNAKE_CASE				(unittest.TestCase      ):



			def __init__(     self		:						int    ,			__UpperCamelCase		:						Union[str, Any]    ,			__UpperCamelCase		:						Dict=7    ,			__UpperCamelCase		:						Dict=3    ,			__UpperCamelCase		:						Tuple=18    ,			__UpperCamelCase		:						Dict=30    ,			__UpperCamelCase		:						Union[str, Any]=400    ,			__UpperCamelCase		:						Dict=True    ,			__UpperCamelCase		:						Tuple=None    ,			__UpperCamelCase		:						Optional[Any]=True    ,			__UpperCamelCase		:						Optional[Any]=None    ,			)      ->      Optional[Any]:
							"""simple docstring"""





							snake_case__      :   List[str]      =    size if size is not None else {'''shortest_edge''': 20}
							snake_case__      :   Union[str, Any]      =    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
							snake_case__      :   Union[str, Any]      =    parent
							snake_case__      :   Optional[int]      =    batch_size
							snake_case__      :   str      =    num_channels
							snake_case__      :   Optional[int]      =    image_size
							snake_case__      :   int      =    min_resolution
							snake_case__      :   Optional[int]      =    max_resolution
							snake_case__      :   Tuple      =    do_resize
							snake_case__      :   str      =    size
							snake_case__      :   Union[str, Any]      =    do_center_crop
							snake_case__      :   List[str]      =    crop_size







			def 				lowerCAmelCase (     self		:						int			)      ->      Any:
							"""simple docstring"""





							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 			_SCREAMING_SNAKE_CASE				(lowercase__,	unittest.TestCase      ):
			A__								=  MobileNetVaImageProcessor if is_vision_available() else None



			def 				lowerCAmelCase (     self		:						Optional[Any]			)      ->      Optional[Any]:
							"""simple docstring"""





							snake_case__      :   Tuple      =    MobileNetVaImageProcessingTester(self			)



			@property
			def 				lowerCAmelCase (     self		:						str			)      ->      List[Any]:
							"""simple docstring"""





							return self.image_processor_tester.prepare_image_processor_dict()



			def 				lowerCAmelCase (     self		:						List[str]			)      ->      Any:
							"""simple docstring"""





							snake_case__      :   Union[str, Any]      =    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    ,			'''center_crop'''			)			)



			def 				lowerCAmelCase (     self		:						List[Any]			)      ->      Tuple:
							"""simple docstring"""





							snake_case__      :   Dict      =    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}			)

							snake_case__      :   Dict      =    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 				lowerCAmelCase (     self		:						List[Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





							pass



			def 				lowerCAmelCase (     self		:						int			)      ->      Optional[int]:
							"""simple docstring"""





							snake_case__      :   Dict      =    self.image_processing_class(**self.image_processor_dict			)
							# create random PIL images
							snake_case__      :   Union[str, Any]      =    prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=__UpperCamelCase			)
							for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase    ,			Image.Image			)

							# Test not batched input
							snake_case__      :   List[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
							snake_case__      :   Dict      =    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 				lowerCAmelCase (     self		:						Optional[Any]			)      ->      List[Any]:
							"""simple docstring"""





							snake_case__      :   Tuple      =    self.image_processing_class(**self.image_processor_dict			)
							# create random numpy tensors
							snake_case__      :   Union[str, 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
							snake_case__      :   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
							snake_case__      :   Any      =    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 				lowerCAmelCase (     self		:						Any			)      ->      int:
							"""simple docstring"""





							snake_case__      :   Optional[Any]      =    self.image_processing_class(**self.image_processor_dict			)
							# create random PyTorch tensors
							snake_case__      :   str      =    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
							snake_case__      :   Tuple      =    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
							snake_case__      :   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'''



import pytest

from datasets.utils.version import Version


@pytest.mark.parametrize(
    '''other, expected_equality'''   ,				[
        (Version('''1.0.0'''			), True),
        ('''1.0.0''', True),
        (Version('''2.0.0'''			), False),
        ('''2.0.0''', False),
        ('''1''', False),
        ('''a''', False),
        (1, False),
        (None, False),
    ]   ,				)
def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[int]   ,				UpperCamelCase__				:int			)		->						List[str]:
				snake_case__      :   Optional[int]      =    Version('''1.0.0'''			)
				assert (version == other) is expected_equality
				assert (version != other) is not expected_equality
				assert (hash(UpperCamelCase__			) == hash(UpperCamelCase__			)) is expected_equality




