






'''simple docstring'''




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    _UpperCAmelCase			(     unittest.TestCase						):




   """simple docstring"""

   def __init__(		self :	Any      ,      __UpperCAmelCase :	List[Any]      ,      __UpperCAmelCase :	Dict=7      ,      __UpperCAmelCase :	List[str]=3      ,      __UpperCAmelCase :	Any=30      ,      __UpperCAmelCase :	List[Any]=400      ,      __UpperCAmelCase :	Optional[int]=True      ,      __UpperCAmelCase :	List[Any]=None      ,      __UpperCAmelCase :	Tuple=True      ,      __UpperCAmelCase :	Tuple=[0.5, 0.5, 0.5]      ,      __UpperCAmelCase :	Optional[int]=[0.5, 0.5, 0.5]      ,      __UpperCAmelCase :	int=True      ,      __UpperCAmelCase :	Optional[int]=1 / 255      ,      __UpperCAmelCase :	Tuple=True      ,      ):





         '''simple docstring'''





         _A			     =	size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
         _A			     =	parent
         _A			     =	batch_size
         _A			     =	num_channels
         _A			     =	min_resolution
         _A			     =	max_resolution
         _A			     =	do_resize
         _A			     =	size
         _A			     =	do_normalize
         _A			     =	image_mean
         _A			     =	image_std
         _A			     =	do_rescale
         _A			     =	rescale_factor
         _A			     =	do_pad

   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''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 						lowerCAmelCase (		self :	List[Any]      ,      __UpperCAmelCase :	Any      ,      __UpperCAmelCase :	Optional[int]=False		):





         '''simple docstring'''





         if not batched:
               _A			     =	image_inputs[0]
               if isinstance(__UpperCAmelCase      ,      Image.Image		):
                     _A     ,	_A			     =	image.size
               else:
                     _A     ,	_A			     =	image.shape[1], image.shape[2]
               if w < h:
                     _A			     =	int(self.size["shortest_edge"] * h / w		)
                     _A			     =	self.size["shortest_edge"]
               elif w > h:
                     _A			     =	self.size["shortest_edge"]
                     _A			     =	int(self.size["shortest_edge"] * w / h		)
               else:
                     _A			     =	self.size["shortest_edge"]
                     _A			     =	self.size["shortest_edge"]

         else:
               _A			     =	[]
               for image in image_inputs:
                     _A     ,	_A			     =	self.get_expected_values([image]		)
                     expected_values.append((expected_height, expected_width)		)
               _A			     =	max(__UpperCAmelCase      ,      key=lambda __UpperCAmelCase		: item[0]		)[0]
               _A			     =	max(__UpperCAmelCase      ,      key=lambda __UpperCAmelCase		: item[1]		)[1]

         return expected_height, expected_width





@require_torch
@require_vision
class    _UpperCAmelCase			(     snake_case_							,    unittest.TestCase						):




   """simple docstring"""

   snake_case    				=						DetaImageProcessor if is_vision_available() else None

   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





         _A			     =	DetaImageProcessingTester(self		)

   @property
   def 						lowerCAmelCase (		self :	Any		):





         '''simple docstring'''





         return self.image_processor_tester.prepare_image_processor_dict()

   def 						lowerCAmelCase (		self :	Union[str, Any]		):





         '''simple docstring'''





         _A			     =	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_rescale"		)		)
         self.assertTrue(hasattr(__UpperCAmelCase      ,      "do_pad"		)		)
         self.assertTrue(hasattr(__UpperCAmelCase      ,      "size"		)		)

   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





         _A			     =	self.image_processing_class.from_dict(self.image_processor_dict		)
         self.assertEqual(image_processor.size      ,      {"shortest_edge": 18, "longest_edge": 1333}		)
         self.assertEqual(image_processor.do_pad      ,      __UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





         pass

   def 						lowerCAmelCase (		self :	Dict		):





         '''simple docstring'''





         _A			     =	self.image_processing_class(**self.image_processor_dict		)
         # create random PIL images
         _A			     =	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			     =	image_processing(image_inputs[0]      ,      return_tensors="pt"		).pixel_values

         _A     ,	_A			     =	self.image_processor_tester.get_expected_values(__UpperCAmelCase		)

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

         # Test batched
         _A     ,	_A			     =	self.image_processor_tester.get_expected_values(__UpperCAmelCase      ,      batched=__UpperCAmelCase		)

         _A			     =	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,
                 expected_height,
                 expected_width,
             )      ,      )

   def 						lowerCAmelCase (		self :	Union[str, Any]		):





         '''simple docstring'''





         _A			     =	self.image_processing_class(**self.image_processor_dict		)
         # create random numpy tensors
         _A			     =	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			     =	image_processing(image_inputs[0]      ,      return_tensors="pt"		).pixel_values

         _A     ,	_A			     =	self.image_processor_tester.get_expected_values(__UpperCAmelCase		)

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

         # Test batched
         _A			     =	image_processing(__UpperCAmelCase      ,      return_tensors="pt"		).pixel_values

         _A     ,	_A			     =	self.image_processor_tester.get_expected_values(__UpperCAmelCase      ,      batched=__UpperCAmelCase		)

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

   def 						lowerCAmelCase (		self :	Optional[Any]		):





         '''simple docstring'''





         _A			     =	self.image_processing_class(**self.image_processor_dict		)
         # create random PyTorch tensors
         _A			     =	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			     =	image_processing(image_inputs[0]      ,      return_tensors="pt"		).pixel_values

         _A     ,	_A			     =	self.image_processor_tester.get_expected_values(__UpperCAmelCase		)

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

         # Test batched
         _A			     =	image_processing(__UpperCAmelCase      ,      return_tensors="pt"		).pixel_values

         _A     ,	_A			     =	self.image_processor_tester.get_expected_values(__UpperCAmelCase      ,      batched=__UpperCAmelCase		)

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

   @slow
   def 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





         _A			     =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
         with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"      ,      "r"		) as f:
               _A			     =	json.loads(f.read()		)

         _A			     =	{"image_id": 39769, "annotations": target}

         # encode them
         _A			     =	DetaImageProcessor()
         _A			     =	image_processing(images=__UpperCAmelCase      ,      annotations=__UpperCAmelCase      ,      return_tensors="pt"		)

         # verify pixel values
         _A			     =	torch.Size([1, 3, 800, 1066]		)
         self.assertEqual(encoding["pixel_values"].shape      ,      __UpperCAmelCase		)

         _A			     =	torch.tensor([0.2796, 0.3138, 0.3481]		)
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,      __UpperCAmelCase      ,      atol=1E-4		)		)

         # verify area
         _A			     =	torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,      __UpperCAmelCase		)		)
         # verify boxes
         _A			     =	torch.Size([6, 4]		)
         self.assertEqual(encoding["labels"][0]["boxes"].shape      ,      __UpperCAmelCase		)
         _A			     =	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,      __UpperCAmelCase      ,      atol=1E-3		)		)
         # verify image_id
         _A			     =	torch.tensor([39769]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,      __UpperCAmelCase		)		)
         # verify is_crowd
         _A			     =	torch.tensor([0, 0, 0, 0, 0, 0]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,      __UpperCAmelCase		)		)
         # verify class_labels
         _A			     =	torch.tensor([75, 75, 63, 65, 17, 17]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,      __UpperCAmelCase		)		)
         # verify orig_size
         _A			     =	torch.tensor([480, 640]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,      __UpperCAmelCase		)		)
         # verify size
         _A			     =	torch.tensor([800, 1066]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,      __UpperCAmelCase		)		)

   @slow
   def 						lowerCAmelCase (		self :	int		):





         '''simple docstring'''





         _A			     =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
         with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"      ,      "r"		) as f:
               _A			     =	json.loads(f.read()		)

         _A			     =	{"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

         _A			     =	pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"		)

         # encode them
         _A			     =	DetaImageProcessor(format="coco_panoptic"		)
         _A			     =	image_processing(images=__UpperCAmelCase      ,      annotations=__UpperCAmelCase      ,      masks_path=__UpperCAmelCase      ,      return_tensors="pt"		)

         # verify pixel values
         _A			     =	torch.Size([1, 3, 800, 1066]		)
         self.assertEqual(encoding["pixel_values"].shape      ,      __UpperCAmelCase		)

         _A			     =	torch.tensor([0.2796, 0.3138, 0.3481]		)
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,      __UpperCAmelCase      ,      atol=1E-4		)		)

         # verify area
         _A			     =	torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,      __UpperCAmelCase		)		)
         # verify boxes
         _A			     =	torch.Size([6, 4]		)
         self.assertEqual(encoding["labels"][0]["boxes"].shape      ,      __UpperCAmelCase		)
         _A			     =	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,      __UpperCAmelCase      ,      atol=1E-3		)		)
         # verify image_id
         _A			     =	torch.tensor([39769]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,      __UpperCAmelCase		)		)
         # verify is_crowd
         _A			     =	torch.tensor([0, 0, 0, 0, 0, 0]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,      __UpperCAmelCase		)		)
         # verify class_labels
         _A			     =	torch.tensor([17, 17, 63, 75, 75, 93]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,      __UpperCAmelCase		)		)
         # verify masks
         _A			     =	822873
         self.assertEqual(encoding["labels"][0]["masks"].sum().item()      ,      __UpperCAmelCase		)
         # verify orig_size
         _A			     =	torch.tensor([480, 640]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,      __UpperCAmelCase		)		)
         # verify size
         _A			     =	torch.tensor([800, 1066]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,      __UpperCAmelCase		)		)









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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    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


_A		       =						logging.get_logger(__name__)


if is_vision_available():
   import PIL






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






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






    def __init__(			self					,   A_ = True					,   A_ = None					,   A_ = PILImageResampling.BICUBIC					,   A_ = True					,   A_ = None					,   A_ = True					,   A_ = 1 / 255					,   A_ = True					,   A_ = None					,   A_ = None					,   A_ = True					,   **A_					,   )  ->       None:
         super().__init__(**A_    )
         __UpperCamelCase    							=size if size is not None else {'shortest_edge': 224}
         __UpperCamelCase    							=get_size_dict(A_					,   default_to_square=A_    )
         __UpperCamelCase    							=crop_size if crop_size is not None else {'height': 224, 'width': 224}
         __UpperCamelCase    							=get_size_dict(A_					,   default_to_square=A_					,   param_name='crop_size'    )

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






    def _a					(			self					,   A_					,   A_					,   A_ = PILImageResampling.BICUBIC					,   A_ = None					,   **A_					,   )  ->       np.ndarray:
         __UpperCamelCase    							=get_size_dict(A_					,   default_to_square=A_    )
         if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'    )
         __UpperCamelCase    							=get_resize_output_image_size(A_					,   size=size['shortest_edge']					,   default_to_square=A_    )
         return resize(A_					,   size=A_					,   resample=A_					,   data_format=A_					,   **A_    )






    def _a					(			self					,   A_					,   A_					,   A_ = None					,   **A_					,   )  ->       np.ndarray:
         __UpperCamelCase    							=get_size_dict(A_    )
         if "height" not in size or "width" not in size:
              raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'    )
         return center_crop(A_					,   size=(size['height'], size['width'])					,   data_format=A_					,   **A_    )






    def _a					(			self					,   A_					,   A_					,   A_ = None					,   **A_					,   )  ->       Union[str, Any]:
         return rescale(A_					,   scale=A_					,   data_format=A_					,   **A_    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_ = None					,   **A_					,   )  ->       np.ndarray:
         return normalize(A_					,   mean=A_					,   std=A_					,   data_format=A_					,   **A_    )






    def _a					(			self					,   A_					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = ChannelDimension.FIRST					,   **A_					,   )  ->       PIL.Image.Image:
         __UpperCamelCase    							=do_resize if do_resize is not None else self.do_resize
         __UpperCamelCase    							=size if size is not None else self.size
         __UpperCamelCase    							=get_size_dict(A_					,   param_name='size'					,   default_to_square=A_    )
         __UpperCamelCase    							=resample if resample is not None else self.resample
         __UpperCamelCase    							=do_center_crop if do_center_crop is not None else self.do_center_crop
         __UpperCamelCase    							=crop_size if crop_size is not None else self.crop_size
         __UpperCamelCase    							=get_size_dict(A_					,   param_name='crop_size'					,   default_to_square=A_    )
         __UpperCamelCase    							=do_rescale if do_rescale is not None else self.do_rescale
         __UpperCamelCase    							=rescale_factor if rescale_factor is not None else self.rescale_factor
         __UpperCamelCase    							=do_normalize if do_normalize is not None else self.do_normalize
         __UpperCamelCase    							=image_mean if image_mean is not None else self.image_mean
         __UpperCamelCase    							=image_std if image_std is not None else self.image_std
         __UpperCamelCase    							=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         __UpperCamelCase    							=make_list_of_images(A_    )

         if not valid_images(A_    ):
              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:
              raise ValueError('Size must be specified if do_resize is True.'    )

         if do_center_crop and crop_size is None:
              raise ValueError('Crop size must be specified if do_center_crop 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:
              __UpperCamelCase    							=[convert_to_rgb(A_    ) for image in images]

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

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

         if do_center_crop:
              __UpperCamelCase    							=[self.center_crop(image=A_					,   size=A_    ) for image in images]

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

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

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

         __UpperCamelCase    							={'pixel_values': images}
         return BatchFeature(data=A_					,   tensor_type=A_    )





