code: 
  
  
  
  
  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			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE=7			,    SCREAMING_SNAKE_CASE=3			,    SCREAMING_SNAKE_CASE=18			,    SCREAMING_SNAKE_CASE=30			,    SCREAMING_SNAKE_CASE=400			,    SCREAMING_SNAKE_CASE=True			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=True			,    SCREAMING_SNAKE_CASE=None			,    )     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	size if size is not None else {'shortest_edge': 20}
  				SCREAMING_SNAKE_CASE_			=	crop_size if crop_size is not None else {'height': 18, 'width': 18}
  				SCREAMING_SNAKE_CASE_			=	parent
  				SCREAMING_SNAKE_CASE_			=	batch_size
  				SCREAMING_SNAKE_CASE_			=	num_channels
  				SCREAMING_SNAKE_CASE_			=	image_size
  				SCREAMING_SNAKE_CASE_			=	min_resolution
  				SCREAMING_SNAKE_CASE_			=	max_resolution
  				SCREAMING_SNAKE_CASE_			=	do_resize
  				SCREAMING_SNAKE_CASE_			=	size
  				SCREAMING_SNAKE_CASE_			=	do_center_crop
  				SCREAMING_SNAKE_CASE_			=	crop_size
  
  
  
  
  
  	def 	A_(       self			)     ->				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 			a_					(    SCREAMING_SNAKE_CASE__     ,						unittest.TestCase    ):
  	A						       =   MobileNetVaImageProcessor if is_vision_available() else None
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	MobileNetVaImageProcessingTester(self			)
  
  
  
  
  
  	@property
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  	def 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.image_processing_class(**self.image_processor_dict			)
  				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,    'do_resize'			)			)
  				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,    'size'			)			)
  				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,    'do_center_crop'			)			)
  				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,    'center_crop'			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	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}			)
  
  				SCREAMING_SNAKE_CASE_			=	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			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				pass
  
  
  
  
  
  	def 	A_(       self			)     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.image_processing_class(**self.image_processor_dict			)
  				# create random PIL images
  				SCREAMING_SNAKE_CASE_			=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=SCREAMING_SNAKE_CASE			)
  				for image in image_inputs:
  							self.assertIsInstance(SCREAMING_SNAKE_CASE			,    Image.Image			)
  
  				# Test not batched input
  				SCREAMING_SNAKE_CASE_			=	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
  				SCREAMING_SNAKE_CASE_			=	image_processing(SCREAMING_SNAKE_CASE			,    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			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.image_processing_class(**self.image_processor_dict			)
  				# create random numpy tensors
  				SCREAMING_SNAKE_CASE_			=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=SCREAMING_SNAKE_CASE			,    numpify=SCREAMING_SNAKE_CASE			)
  				for image in image_inputs:
  							self.assertIsInstance(SCREAMING_SNAKE_CASE			,    np.ndarray			)
  
  				# Test not batched input
  				SCREAMING_SNAKE_CASE_			=	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
  				SCREAMING_SNAKE_CASE_			=	image_processing(SCREAMING_SNAKE_CASE			,    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"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.image_processing_class(**self.image_processor_dict			)
  				# create random PyTorch tensors
  				SCREAMING_SNAKE_CASE_			=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=SCREAMING_SNAKE_CASE			,    torchify=SCREAMING_SNAKE_CASE			)
  				for image in image_inputs:
  							self.assertIsInstance(SCREAMING_SNAKE_CASE			,    torch.Tensor			)
  
  				# Test not batched input
  				SCREAMING_SNAKE_CASE_			=	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
  				SCREAMING_SNAKE_CASE_			=	image_processing(SCREAMING_SNAKE_CASE			,    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'],
  				    )			,    )
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					{"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    int								=					["DetrFeatureExtractor"]
  				SCREAMING_SNAKE_CASE__    :    Any								=					["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    List[Any]								=					[
  				    "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
  
  				SCREAMING_SNAKE_CASE__    :    Tuple								=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  