






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor
class     lowerCamelCase_					(   unittest.TestCase			):







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=7							,       __lowerCAmelCase=3							,       __lowerCAmelCase=3_0							,       __lowerCAmelCase=4_0_0							,       __lowerCAmelCase=True							,       __lowerCAmelCase=None							,       __lowerCAmelCase=True							,       __lowerCAmelCase=[0.5, 0.5, 0.5]							,       __lowerCAmelCase=[0.5, 0.5, 0.5]							,       __lowerCAmelCase=True							,       __lowerCAmelCase=1 / 2_5_5							,       __lowerCAmelCase=True							,       ):
								"""simple docstring"""
								# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
								__magic_name__		:Any   =   size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
								__magic_name__		:Any   =   parent
								__magic_name__		:Optional[int]   =   batch_size
								__magic_name__		:str   =   num_channels
								__magic_name__		:Any   =   min_resolution
								__magic_name__		:Any   =   max_resolution
								__magic_name__		:List[Any]   =   do_resize
								__magic_name__		:Optional[int]   =   size
								__magic_name__		:Any   =   do_normalize
								__magic_name__		:Optional[int]   =   image_mean
								__magic_name__		:str   =   image_std
								__magic_name__		:Optional[Any]   =   do_rescale
								__magic_name__		:str   =   rescale_factor
								__magic_name__		:int   =   do_pad







				def       A						(    self  ):
								"""simple docstring"""
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_rescale": self.do_rescale,
								    "rescale_factor": self.rescale_factor,
								    "do_pad": self.do_pad,
								}







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=False  ):
								"""simple docstring"""
								if not batched:
												__magic_name__		:Optional[int]   =   image_inputs[0]
												if isinstance(__lowerCAmelCase							,       Image.Image  ):
																__magic_name__  ,					__magic_name__		:List[str]   =   image.size
												else:
																__magic_name__  ,					__magic_name__		:List[Any]   =   image.shape[1], image.shape[2]
												if w < h:
																__magic_name__		:str   =   int(self.size['''shortest_edge'''] * h / w  )
																__magic_name__		:List[str]   =   self.size['''shortest_edge''']
												elif w > h:
																__magic_name__		:List[Any]   =   self.size['''shortest_edge''']
																__magic_name__		:Optional[Any]   =   int(self.size['''shortest_edge'''] * w / h  )
												else:
																__magic_name__		:str   =   self.size['''shortest_edge''']
																__magic_name__		:Optional[Any]   =   self.size['''shortest_edge''']

								else:
												__magic_name__		:str   =   []
												for image in image_inputs:
																__magic_name__  ,					__magic_name__		:Optional[int]   =   self.get_expected_values([image]  )
																expected_values.append((expected_height, expected_width)  )
												__magic_name__		:Tuple   =   max(__lowerCAmelCase							,       key=lambda __lowerCAmelCase  : item[0]  )[0]
												__magic_name__		:List[Any]   =   max(__lowerCAmelCase							,       key=lambda __lowerCAmelCase  : item[1]  )[1]

								return expected_height, expected_width







@require_torch
@require_vision
class     lowerCamelCase_					(   lowerCamelCase      ,					unittest.TestCase			):
				a__       							=	DetaImageProcessor if is_vision_available() else None







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   DetaImageProcessingTester(self  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								return self.image_processor_tester.prepare_image_processor_dict()







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''image_mean'''  )  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''image_std'''  )  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''do_normalize'''  )  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''do_resize'''  )  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''do_rescale'''  )  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''do_pad'''  )  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''size'''  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.image_processing_class.from_dict(self.image_processor_dict  )
								self.assertEqual(image_processor.size							,       {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}  )
								self.assertEqual(image_processor.do_pad							,       __lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								pass







				def       A						(    self  ):
								"""simple docstring"""
								# Initialize image_processing
								__magic_name__		:Tuple   =   self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								__magic_name__		:Optional[int]   =   prepare_image_inputs(self.image_processor_tester							,       equal_resolution=__lowerCAmelCase  )
								for image in image_inputs:
												self.assertIsInstance(__lowerCAmelCase							,       Image.Image  )

								# Test not batched input
								__magic_name__		:List[Any]   =   image_processing(image_inputs[0]							,       return_tensors='''pt'''  ).pixel_values

								__magic_name__  ,					__magic_name__		:str   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase  )

								self.assertEqual(
								    encoded_images.shape							,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,       )

								# Test batched
								__magic_name__  ,					__magic_name__		:int   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase							,       batched=__lowerCAmelCase  )

								__magic_name__		:Optional[Any]   =   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,
								        expected_height,
								        expected_width,
								    )							,       )







				def       A						(    self  ):
								"""simple docstring"""
								# Initialize image_processing
								__magic_name__		:Optional[int]   =   self.image_processing_class(**self.image_processor_dict  )
								# create random numpy tensors
								__magic_name__		:Optional[int]   =   prepare_image_inputs(self.image_processor_tester							,       equal_resolution=__lowerCAmelCase							,       numpify=__lowerCAmelCase  )
								for image in image_inputs:
												self.assertIsInstance(__lowerCAmelCase							,       np.ndarray  )

								# Test not batched input
								__magic_name__		:Optional[int]   =   image_processing(image_inputs[0]							,       return_tensors='''pt'''  ).pixel_values

								__magic_name__  ,					__magic_name__		:str   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase  )

								self.assertEqual(
								    encoded_images.shape							,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,       )

								# Test batched
								__magic_name__		:str   =   image_processing(__lowerCAmelCase							,       return_tensors='''pt'''  ).pixel_values

								__magic_name__  ,					__magic_name__		:Optional[int]   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase							,       batched=__lowerCAmelCase  )

								self.assertEqual(
								    encoded_images.shape							,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )							,       )







				def       A						(    self  ):
								"""simple docstring"""
								# Initialize image_processing
								__magic_name__		:Any   =   self.image_processing_class(**self.image_processor_dict  )
								# create random PyTorch tensors
								__magic_name__		:Tuple   =   prepare_image_inputs(self.image_processor_tester							,       equal_resolution=__lowerCAmelCase							,       torchify=__lowerCAmelCase  )
								for image in image_inputs:
												self.assertIsInstance(__lowerCAmelCase							,       torch.Tensor  )

								# Test not batched input
								__magic_name__		:List[str]   =   image_processing(image_inputs[0]							,       return_tensors='''pt'''  ).pixel_values

								__magic_name__  ,					__magic_name__		:Dict   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase  )

								self.assertEqual(
								    encoded_images.shape							,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,       )

								# Test batched
								__magic_name__		:Dict   =   image_processing(__lowerCAmelCase							,       return_tensors='''pt'''  ).pixel_values

								__magic_name__  ,					__magic_name__		:str   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase							,       batched=__lowerCAmelCase  )

								self.assertEqual(
								    encoded_images.shape							,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )							,       )







				@slow
				def       A						(    self  ):
								"""simple docstring"""
								# prepare image and target
								__magic_name__		:Union[str, Any]   =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''							,       '''r'''  ) as f:
												__magic_name__		:List[Any]   =   json.loads(f.read()  )

								__magic_name__		:Tuple   =   {'''image_id''': 3_9_7_6_9, '''annotations''': target}

								# encode them
								__magic_name__		:Tuple   =   DetaImageProcessor()
								__magic_name__		:Dict   =   image_processing(images=__lowerCAmelCase							,       annotations=__lowerCAmelCase							,       return_tensors='''pt'''  )

								# verify pixel values
								__magic_name__		:Dict   =   torch.Size([1, 3, 8_0_0, 1_0_6_6]  )
								self.assertEqual(encoding['''pixel_values'''].shape							,       __lowerCAmelCase  )

								__magic_name__		:Dict   =   torch.tensor([0.2796, 0.3138, 0.3481]  )
								self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,       __lowerCAmelCase							,       atol=1E-4  )  )

								# verify area
								__magic_name__		:List[str]   =   torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,       __lowerCAmelCase  )  )
								# verify boxes
								__magic_name__		:List[Any]   =   torch.Size([6, 4]  )
								self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,       __lowerCAmelCase  )
								__magic_name__		:Union[str, Any]   =   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,       __lowerCAmelCase							,       atol=1E-3  )  )
								# verify image_id
								__magic_name__		:Tuple   =   torch.tensor([3_9_7_6_9]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,       __lowerCAmelCase  )  )
								# verify is_crowd
								__magic_name__		:Optional[Any]   =   torch.tensor([0, 0, 0, 0, 0, 0]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,       __lowerCAmelCase  )  )
								# verify class_labels
								__magic_name__		:Union[str, Any]   =   torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,       __lowerCAmelCase  )  )
								# verify orig_size
								__magic_name__		:Optional[int]   =   torch.tensor([4_8_0, 6_4_0]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,       __lowerCAmelCase  )  )
								# verify size
								__magic_name__		:List[Any]   =   torch.tensor([8_0_0, 1_0_6_6]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,       __lowerCAmelCase  )  )







				@slow
				def       A						(    self  ):
								"""simple docstring"""
								# prepare image, target and masks_path
								__magic_name__		:Any   =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''							,       '''r'''  ) as f:
												__magic_name__		:str   =   json.loads(f.read()  )

								__magic_name__		:Optional[int]   =   {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

								__magic_name__		:Any   =   pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''  )

								# encode them
								__magic_name__		:List[str]   =   DetaImageProcessor(format='''coco_panoptic'''  )
								__magic_name__		:Optional[int]   =   image_processing(images=__lowerCAmelCase							,       annotations=__lowerCAmelCase							,       masks_path=__lowerCAmelCase							,       return_tensors='''pt'''  )

								# verify pixel values
								__magic_name__		:Dict   =   torch.Size([1, 3, 8_0_0, 1_0_6_6]  )
								self.assertEqual(encoding['''pixel_values'''].shape							,       __lowerCAmelCase  )

								__magic_name__		:Optional[int]   =   torch.tensor([0.2796, 0.3138, 0.3481]  )
								self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,       __lowerCAmelCase							,       atol=1E-4  )  )

								# verify area
								__magic_name__		:str   =   torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,       __lowerCAmelCase  )  )
								# verify boxes
								__magic_name__		:Optional[Any]   =   torch.Size([6, 4]  )
								self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,       __lowerCAmelCase  )
								__magic_name__		:int   =   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,       __lowerCAmelCase							,       atol=1E-3  )  )
								# verify image_id
								__magic_name__		:int   =   torch.tensor([3_9_7_6_9]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,       __lowerCAmelCase  )  )
								# verify is_crowd
								__magic_name__		:Tuple   =   torch.tensor([0, 0, 0, 0, 0, 0]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,       __lowerCAmelCase  )  )
								# verify class_labels
								__magic_name__		:Tuple   =   torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,       __lowerCAmelCase  )  )
								# verify masks
								__magic_name__		:List[str]   =   8_2_2_8_7_3
								self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()							,       __lowerCAmelCase  )
								# verify orig_size
								__magic_name__		:Union[str, Any]   =   torch.tensor([4_8_0, 6_4_0]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,       __lowerCAmelCase  )  )
								# verify size
								__magic_name__		:List[str]   =   torch.tensor([8_0_0, 1_0_6_6]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,       __lowerCAmelCase  )  )










'''simple docstring'''
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


__SCREAMING_SNAKE_CASE					:				List[str]									=		logging.get_logger(__name__)






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Union[str, Any]   						=["pixel_values"]






						def __init__(  self							:      Optional[int] ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BICUBIC ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Union[int, float] = 1 / 2_55 ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      bool = True ,  **lowerCAmelCase							:      List[Any] ,  ):
													super().__init__(**lowerCAmelCase	)
													A_			    =		size if size is not None else {"height": 3_84, "width": 3_84}
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)

													A_			    =		do_resize
													A_			    =		size
													A_			    =		resample
													A_			    =		do_rescale
													A_			    =		rescale_factor
													A_			    =		do_normalize
													A_			    =		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
													A_			    =		image_std if image_std is not None else OPENAI_CLIP_STD
													A_			    =		do_convert_rgb






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Dict[str, int] ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BICUBIC ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Tuple ,  ):
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)
													if "height" not in size or "width" not in size:
																				raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"	)
													A_			    =		(size["height"], size["width"])
													return resize(lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      str ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[int, float] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      str ,  ):
													return rescale(lowerCAmelCase ,  scale=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[int] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[float, List[float]] ,  lowerCAmelCase							:      Union[float, List[float]] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Dict ,  ):
													return normalize(lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      ImageInput ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[Dict[str, int]] = None ,  lowerCAmelCase							:      PILImageResampling = None ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[float] = None ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[str, TensorType]] = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      ChannelDimension = ChannelDimension.FIRST ,  **lowerCAmelCase							:      List[str] ,  ):
													A_			    =		do_resize if do_resize is not None else self.do_resize
													A_			    =		resample if resample is not None else self.resample
													A_			    =		do_rescale if do_rescale is not None else self.do_rescale
													A_			    =		rescale_factor if rescale_factor is not None else self.rescale_factor
													A_			    =		do_normalize if do_normalize is not None else self.do_normalize
													A_			    =		image_mean if image_mean is not None else self.image_mean
													A_			    =		image_std if image_std is not None else self.image_std
													A_			    =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													A_			    =		size if size is not None else self.size
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)

													A_			    =		make_list_of_images(lowerCAmelCase	)

													if not valid_images(lowerCAmelCase	):
																				raise ValueError(
																				    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																				    "torch.Tensor, tf.Tensor or jax.ndarray."	)

													if do_resize and size is None or resample is None:
																				raise ValueError("Size and resample must be specified if do_resize is True."	)

													if do_rescale and rescale_factor is None:
																				raise ValueError("Rescale factor must be specified if do_rescale is True."	)

													if do_normalize and (image_mean is None or image_std is None):
																				raise ValueError("Image mean and std must be specified if do_normalize is True."	)

													# PIL RGBA images are converted to RGB
													if do_convert_rgb:
																				A_			    =		[convert_to_rgb(lowerCAmelCase	) for image in images]

													# All transformations expect numpy arrays.
													A_			    =		[to_numpy_array(lowerCAmelCase	) for image in images]

													if do_resize:
																				A_			    =		[self.resize(image=lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase	) for image in images]

													if do_rescale:
																				A_			    =		[self.rescale(image=lowerCAmelCase ,  scale=lowerCAmelCase	) for image in images]

													if do_normalize:
																				A_			    =		[self.normalize(image=lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase	) for image in images]

													A_			    =		[to_channel_dimension_format(lowerCAmelCase ,  lowerCAmelCase	) for image in images]

													A_			    =		BatchFeature(data={"pixel_values": images} ,  tensor_type=lowerCAmelCase	)

													return encoded_outputs





