code: 
  '''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
  
  
  if is_torch_available():
  				import torch
  
  if is_vision_available():
  				from PIL import Image
  
  				from transformers import CLIPImageProcessor
  
  
  
  
  
  class 			A							(       unittest.TestCase				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__=7       ,     lowerCamelCase__=3       ,     lowerCamelCase__=18       ,     lowerCamelCase__=30       ,     lowerCamelCase__=400       ,     lowerCamelCase__=True       ,     lowerCamelCase__=None       ,     lowerCamelCase__=True       ,     lowerCamelCase__=None       ,     lowerCamelCase__=True       ,     lowerCamelCase__=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]       ,     lowerCamelCase__=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]       ,     lowerCamelCase__=True       ,     )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       size if size is not None else {"""shortest_edge""": 20}
  									lowercase__							 =       crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
  									lowercase__							 =       parent
  									lowercase__							 =       batch_size
  									lowercase__							 =       num_channels
  									lowercase__							 =       image_size
  									lowercase__							 =       min_resolution
  									lowercase__							 =       max_resolution
  									lowercase__							 =       do_resize
  									lowercase__							 =       size
  									lowercase__							 =       do_center_crop
  									lowercase__							 =       crop_size
  									lowercase__							 =       do_normalize
  									lowercase__							 =       image_mean
  									lowercase__							 =       image_std
  									lowercase__							 =       do_convert_rgb
  
  
  
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return {
  									    "do_resize": self.do_resize,
  									    "size": self.size,
  									    "do_center_crop": self.do_center_crop,
  									    "crop_size": self.crop_size,
  									    "do_normalize": self.do_normalize,
  									    "image_mean": self.image_mean,
  									    "image_std": self.image_std,
  									    "do_convert_rgb": self.do_convert_rgb,
  									}
  
  
  
  			def A__ (					self       ,     lowerCamelCase__=False       ,     lowerCamelCase__=False       ,     lowerCamelCase__=False       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
  									if equal_resolution:
  															lowercase__							 =       []
  															for i in range(self.batch_size       ):
  																					image_inputs.append(
  																					    np.random.randint(
  																					        255       ,     size=(self.num_channels, self.max_resolution, self.max_resolution)       ,     dtype=np.uinta       )       )
  									else:
  															lowercase__							 =       []
  															for i in range(self.batch_size       ):
  																					lowercase__ ,						lowercase__							 =       np.random.choice(np.arange(self.min_resolution       ,     self.max_resolution       )       ,     2       )
  																					image_inputs.append(np.random.randint(255       ,     size=(self.num_channels, width, height)       ,     dtype=np.uinta       )       )
  
  									if not numpify and not torchify:
  															# PIL expects the channel dimension as last dimension
  															lowercase__							 =       [Image.fromarray(np.moveaxis(lowerCamelCase__       ,     0       ,     -1       )       ) for x in image_inputs]
  
  									if torchify:
  															lowercase__							 =       [torch.from_numpy(lowerCamelCase__       ) for x in image_inputs]
  
  									return image_inputs
  
  
  
  
  
  @require_torch
  @require_vision
  class 			A							(       __UpperCAmelCase		,						unittest.TestCase				):
  			lowerCamelCase							:							Any       		=  CLIPImageProcessor if is_vision_available() else None
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       CLIPImageProcessingTester(self       )
  
  
  
  			@property
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  			def A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.image_processing_class(**self.image_processor_dict       )
  									self.assertTrue(hasattr(lowerCamelCase__       ,     """do_resize"""       )       )
  									self.assertTrue(hasattr(lowerCamelCase__       ,     """size"""       )       )
  									self.assertTrue(hasattr(lowerCamelCase__       ,     """do_center_crop"""       )       )
  									self.assertTrue(hasattr(lowerCamelCase__       ,     """center_crop"""       )       )
  									self.assertTrue(hasattr(lowerCamelCase__       ,     """do_normalize"""       )       )
  									self.assertTrue(hasattr(lowerCamelCase__       ,     """image_mean"""       )       )
  									self.assertTrue(hasattr(lowerCamelCase__       ,     """image_std"""       )       )
  									self.assertTrue(hasattr(lowerCamelCase__       ,     """do_convert_rgb"""       )       )
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       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}       )
  
  									lowercase__							 =       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       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.image_processing_class(**self.image_processor_dict       )
  									# create random PIL images
  									lowercase__							 =       self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__       )
  									for image in image_inputs:
  															self.assertIsInstance(lowerCamelCase__       ,     Image.Image       )
  
  									# Test not batched input
  									lowercase__							 =       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
  									lowercase__							 =       image_processing(lowerCamelCase__       ,     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       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.image_processing_class(**self.image_processor_dict       )
  									# create random numpy tensors
  									lowercase__							 =       self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__       ,     numpify=lowerCamelCase__       )
  									for image in image_inputs:
  															self.assertIsInstance(lowerCamelCase__       ,     np.ndarray       )
  
  									# Test not batched input
  									lowercase__							 =       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
  									lowercase__							 =       image_processing(lowerCamelCase__       ,     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       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.image_processing_class(**self.image_processor_dict       )
  									# create random PyTorch tensors
  									lowercase__							 =       self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__       ,     torchify=lowerCamelCase__       )
  									for image in image_inputs:
  															self.assertIsInstance(lowerCamelCase__       ,     torch.Tensor       )
  
  									# Test not batched input
  									lowercase__							 =       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
  									lowercase__							 =       image_processing(lowerCamelCase__       ,     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"""],
  									    )       ,     )
  
  
  
  
  
  @require_torch
  @require_vision
  class 			A							(       __UpperCAmelCase		,						unittest.TestCase				):
  			lowerCamelCase							:							Optional[Any]       		=  CLIPImageProcessor if is_vision_available() else None
  
  
  
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       CLIPImageProcessingTester(self       ,     num_channels=4       )
  									lowercase__							 =       3
  
  
  
  			@property
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  			def A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.image_processing_class(**self.image_processor_dict       )
  									self.assertTrue(hasattr(lowerCamelCase__       ,     """do_resize"""       )       )
  									self.assertTrue(hasattr(lowerCamelCase__       ,     """size"""       )       )
  									self.assertTrue(hasattr(lowerCamelCase__       ,     """do_center_crop"""       )       )
  									self.assertTrue(hasattr(lowerCamelCase__       ,     """center_crop"""       )       )
  									self.assertTrue(hasattr(lowerCamelCase__       ,     """do_normalize"""       )       )
  									self.assertTrue(hasattr(lowerCamelCase__       ,     """image_mean"""       )       )
  									self.assertTrue(hasattr(lowerCamelCase__       ,     """image_std"""       )       )
  									self.assertTrue(hasattr(lowerCamelCase__       ,     """do_convert_rgb"""       )       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			def A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.image_processing_class(**self.image_processor_dict       )
  									# create random PIL images
  									lowercase__							 =       self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__       )
  									for image in image_inputs:
  															self.assertIsInstance(lowerCamelCase__       ,     Image.Image       )
  
  									# Test not batched input
  									lowercase__							 =       image_processing(image_inputs[0]       ,     return_tensors="""pt"""       ).pixel_values
  									self.assertEqual(
  									    encoded_images.shape       ,     (
  									        1,
  									        self.expected_encoded_image_num_channels,
  									        self.image_processor_tester.crop_size["""height"""],
  									        self.image_processor_tester.crop_size["""width"""],
  									    )       ,     )
  
  									# Test batched
  									lowercase__							 =       image_processing(lowerCamelCase__       ,     return_tensors="""pt"""       ).pixel_values
  									self.assertEqual(
  									    encoded_images.shape       ,     (
  									        self.image_processor_tester.batch_size,
  									        self.expected_encoded_image_num_channels,
  									        self.image_processor_tester.crop_size["""height"""],
  									        self.image_processor_tester.crop_size["""width"""],
  									    )       ,     )
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  __A          =    {"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    ["DetrFeatureExtractor"]
  				__A          =    ["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    [
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				__A          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  