






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 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



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




											'''simple docstring'''



											__a					:						int      =					size if size is not None else {"shortest_edge": 18}
											__a					:						Dict      =					crop_size if crop_size is not None else {"height": 18, "width": 18}
											__a					:						int      =					parent
											__a					:						str      =					batch_size
											__a					:						Union[str, Any]      =					num_channels
											__a					:						Tuple      =					image_size
											__a					:						str      =					min_resolution
											__a					:						List[Any]      =					max_resolution
											__a					:						Tuple      =					do_resize
											__a					:						Any      =					size
											__a					:						Dict      =					do_center_crop
											__a					:						Optional[int]      =					crop_size
											__a					:						Optional[Any]      =					do_normalize
											__a					:						Optional[Any]      =					image_mean
											__a					:						List[str]      =					image_std



						def        UpperCAmelCase__					(self:					Any   )    ->     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 							snake_case_       (    __UpperCamelCase		,							unittest.TestCase      ):

						"""simple docstring"""



						snake_case__        =      LevitImageProcessor if is_vision_available() else None



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




											'''simple docstring'''



											__a					:						Union[str, Any]      =					LevitImageProcessingTester(self   )



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




											'''simple docstring'''



											return self.image_processor_tester.prepare_image_processor_dict()



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




											'''simple docstring'''



											__a					:						List[str]      =					self.image_processing_class(**self.image_processor_dict   )
											self.assertTrue(hasattr(__UpperCAmelCase ,					"image_mean"   )   )
											self.assertTrue(hasattr(__UpperCAmelCase ,					"image_std"   )   )
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_normalize"   )   )
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_resize"   )   )
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_center_crop"   )   )
											self.assertTrue(hasattr(__UpperCAmelCase ,					"size"   )   )



						def        UpperCAmelCase__					(self:					Dict   )    ->     Optional[int]:




											'''simple docstring'''



											__a					:						Optional[int]      =					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}   )

											__a					:						str      =					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:					List[Any]   )    ->     List[Any]:




											'''simple docstring'''



											pass



						def        UpperCAmelCase__					(self:					Any   )    ->     List[str]:




											'''simple docstring'''



											__a					:						Dict      =					self.image_processing_class(**self.image_processor_dict   )
											# create random PIL images
											__a					:						Optional[int]      =					prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCAmelCase   )
											for image in image_inputs:
																self.assertIsInstance(__UpperCAmelCase ,					Image.Image   )

											# Test not batched input
											__a					:						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
											__a					:						Union[str, 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        UpperCAmelCase__					(self:					Tuple   )    ->     Dict:




											'''simple docstring'''



											__a					:						List[Any]      =					self.image_processing_class(**self.image_processor_dict   )
											# create random numpy tensors
											__a					:						List[str]      =					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
											__a					:						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
											__a					:						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"],
											    ) ,					)



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




											'''simple docstring'''



											__a					:						Any      =					self.image_processing_class(**self.image_processor_dict   )
											# create random PyTorch tensors
											__a					:						Optional[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
											__a					:						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
											__a					:						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"],
											    ) ,					)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__      			=						{
    '''configuration_poolformer''': [
        '''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''PoolFormerConfig''',
        '''PoolFormerOnnxConfig''',
    ]
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__      			=						['''PoolFormerFeatureExtractor''']
			UpperCAmelCase__      			=						['''PoolFormerImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__      			=						[
			    '''POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''PoolFormerForImageClassification''',
			    '''PoolFormerModel''',
			    '''PoolFormerPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_poolformer import (
			    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    PoolFormerConfig,
			    PoolFormerOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_poolformer import PoolFormerFeatureExtractor
						from .image_processing_poolformer import PoolFormerImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_poolformer import (
						    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PoolFormerForImageClassification,
						    PoolFormerModel,
						    PoolFormerPreTrainedModel,
						)


else:
			import sys

			UpperCAmelCase__      			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)





