
"""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 LevitImageProcessor






class        _UpperCAmelCase(       unittest.TestCase      ):



		def __init__(   self							,						__a							,						__a=7							,						__a=3							,						__a=18							,						__a=30							,						__a=4_00							,						__a=True							,						__a=None							,						__a=True							,						__a=None							,						__a=True							,						__a=[0.5, 0.5, 0.5]							,						__a=[0.5, 0.5, 0.5]							,						)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    size if size is not None else {'''shortest_edge''': 18}
							_UpperCamelCase									=    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
							_UpperCamelCase									=    parent
							_UpperCamelCase									=    batch_size
							_UpperCamelCase									=    num_channels
							_UpperCamelCase									=    image_size
							_UpperCamelCase									=    min_resolution
							_UpperCamelCase									=    max_resolution
							_UpperCamelCase									=    do_resize
							_UpperCamelCase									=    size
							_UpperCamelCase									=    do_center_crop
							_UpperCamelCase									=    crop_size
							_UpperCamelCase									=    do_normalize
							_UpperCamelCase									=    image_mean
							_UpperCamelCase									=    image_std




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






							'''simple docstring'''
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "do_center_crop": self.do_center_crop,
							    "size": self.size,
							    "crop_size": self.crop_size,
							}






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



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    LevitImageProcessingTester(self)



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






							'''simple docstring'''
							return self.image_processor_tester.prepare_image_processor_dict()



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






							'''simple docstring'''
							_UpperCamelCase									=    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							,						'''do_center_crop'''))
							self.assertTrue(hasattr(__a							,						'''size'''))



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






							'''simple docstring'''
							_UpperCamelCase									=    self.image_processing_class.from_dict(self.image_processor_dict)
							self.assertEqual(image_processor.size							,						{'''shortest_edge''': 18})
							self.assertEqual(image_processor.crop_size							,						{'''height''': 18, '''width''': 18})

							_UpperCamelCase									=    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 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							pass



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase									=    self.image_processing_class(**self.image_processor_dict)
							# create random PIL images
							_UpperCamelCase									=    prepare_image_inputs(self.image_processor_tester							,						equal_resolution=__a)
							for image in image_inputs:
												self.assertIsInstance(__a							,						Image.Image)

							# Test not batched input
							_UpperCamelCase									=    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
							_UpperCamelCase									=    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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )							,						)



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






							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase									=    self.image_processing_class(**self.image_processor_dict)
							# create random numpy tensors
							_UpperCamelCase									=    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
							_UpperCamelCase									=    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
							_UpperCamelCase									=    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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )							,						)




		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase									=    self.image_processing_class(**self.image_processor_dict)
							# create random PyTorch tensors
							_UpperCamelCase									=    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
							_UpperCamelCase									=    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
							_UpperCamelCase									=    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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )							,						)












import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version





def 						__A						(      *__lowerCamelCase				,     __lowerCamelCase = None				,     __lowerCamelCase=True				,     __lowerCamelCase=2       ) ->			Dict:
	from .. import __version__

	a									=				take_from
	a									=				()
	if not isinstance(args[0]				,     __lowerCamelCase       ):
		a									=				(args,)

	for attribute, version_name, message in args:
		if version.parse(version.parse(__lowerCamelCase       ).base_version       ) >= version.parse(__lowerCamelCase       ):
			raise ValueError(
			    f'The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''
			    f' version {__version__} is >= {version_name}'       )

		a									=				None
		if isinstance(__lowerCamelCase				,     __lowerCamelCase       ) and attribute in deprecated_kwargs:
			values += (deprecated_kwargs.pop(__lowerCamelCase       ),)
			a									=				f'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
		elif hasattr(__lowerCamelCase				,     __lowerCamelCase       ):
			values += (getattr(__lowerCamelCase				,     __lowerCamelCase       ),)
			a									=				f'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
		elif deprecated_kwargs is None:
			a									=				f'`{attribute}` is deprecated and will be removed in version {version_name}.'

		if warning is not None:
			a									=				warning + """ """ if standard_warn else """"""
			warnings.warn(warning + message				,     __lowerCamelCase				,     stacklevel=__lowerCamelCase       )

	if isinstance(__lowerCamelCase				,     __lowerCamelCase       ) and len(__lowerCamelCase       ) > 0:
		a									=				inspect.getouterframes(inspect.currentframe()       )[1]
		a									=				call_frame.filename
		a									=				call_frame.lineno
		a									=				call_frame.function
		a							,					a									=				next(iter(deprecated_kwargs.items()       )       )
		raise TypeError(f'{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'       )

	if len(__lowerCamelCase       ) == 0:
		return
	elif len(__lowerCamelCase       ) == 1:
		return values[0]
	return values

