"""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 ConditionalDetrImageProcessor
class 						__A							( unittest.TestCase		):






     def __init__(		self				,     a__				,     a__=7				,     a__=3				,     a__=30				,     a__=400				,     a__=True				,     a__=None				,     a__=True				,     a__=[0.5, 0.5, 0.5]				,     a__=[0.5, 0.5, 0.5]				,     a__=True				,     a__=1 / 255				,     a__=True				,     ):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       _lowerCAmelCase						:       int  			=     size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
       _lowerCAmelCase						:       List[str]  			=     parent
       _lowerCAmelCase						:       Dict  			=     batch_size
       _lowerCAmelCase						:       int  			=     num_channels
       _lowerCAmelCase						:       Optional[Any]  			=     min_resolution
       _lowerCAmelCase						:       Tuple  			=     max_resolution
       _lowerCAmelCase						:       List[Any]  			=     do_resize
       _lowerCAmelCase						:       Optional[Any]  			=     size
       _lowerCAmelCase						:       Optional[int]  			=     do_normalize
       _lowerCAmelCase						:       Optional[int]  			=     image_mean
       _lowerCAmelCase						:       Dict  			=     image_std
       _lowerCAmelCase						:       Optional[Any]  			=     do_rescale
       _lowerCAmelCase						:       List[Any]  			=     rescale_factor
       _lowerCAmelCase						:       int  			=     do_pad






     def     __A  (		self  ):
       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				,     a__				,     a__=False  ):
       if not batched:
         _lowerCAmelCase						:       Optional[int]  			=     image_inputs[0]
         if isinstance(a__				,     Image.Image  ):
           _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     image.size
         else:
           _lowerCAmelCase			,	_lowerCAmelCase						:       Tuple  			=     image.shape[1], image.shape[2]
         if w < h:
           _lowerCAmelCase						:       Any  			=     int(self.size["""shortest_edge"""] * h / w  )
           _lowerCAmelCase						:       Optional[int]  			=     self.size["""shortest_edge"""]
         elif w > h:
           _lowerCAmelCase						:       Tuple  			=     self.size["""shortest_edge"""]
           _lowerCAmelCase						:       Union[str, Any]  			=     int(self.size["""shortest_edge"""] * w / h  )
         else:
           _lowerCAmelCase						:       List[Any]  			=     self.size["""shortest_edge"""]
           _lowerCAmelCase						:       Any  			=     self.size["""shortest_edge"""]

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

       return expected_height, expected_width







@require_torch
@require_vision
class 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			Tuple							=		ConditionalDetrImageProcessor if is_vision_available() else None






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     ConditionalDetrImageProcessingTester(self  )






     @property
     def     __A  (		self  ):
       return self.image_processor_tester.prepare_image_processor_dict()






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(a__				,     """image_mean"""  )  )
       self.assertTrue(hasattr(a__				,     """image_std"""  )  )
       self.assertTrue(hasattr(a__				,     """do_normalize"""  )  )
       self.assertTrue(hasattr(a__				,     """do_resize"""  )  )
       self.assertTrue(hasattr(a__				,     """size"""  )  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     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				,     a__  )

       _lowerCAmelCase						:       Tuple  			=     self.image_processing_class.from_dict(
           self.image_processor_dict				,     size=42				,     max_size=84				,     pad_and_return_pixel_mask=a__  )
       self.assertEqual(image_processor.size				,     {"""shortest_edge""": 42, """longest_edge""": 84}  )
       self.assertEqual(image_processor.do_pad				,     a__  )






     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:       Tuple  			=     self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       _lowerCAmelCase						:       Optional[int]  			=     prepare_image_inputs(self.image_processor_tester				,     equal_resolution=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__				,     Image.Image  )

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

       _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     self.image_processor_tester.get_expected_values(a__  )

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

       # Test batched
       _lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, Any]  			=     self.image_processor_tester.get_expected_values(a__				,     batched=a__  )

       _lowerCAmelCase						:       Optional[Any]  			=     image_processing(a__				,     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  ):
       # Initialize image_processing
       _lowerCAmelCase						:       Tuple  			=     self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       _lowerCAmelCase						:       Dict  			=     prepare_image_inputs(self.image_processor_tester				,     equal_resolution=a__				,     numpify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__				,     np.ndarray  )

       # Test not batched input
       _lowerCAmelCase						:       Union[str, Any]  			=     image_processing(image_inputs[0]				,     return_tensors="""pt"""  ).pixel_values

       _lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, Any]  			=     self.image_processor_tester.get_expected_values(a__  )

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

       # Test batched
       _lowerCAmelCase						:       str  			=     image_processing(a__				,     return_tensors="""pt"""  ).pixel_values

       _lowerCAmelCase			,	_lowerCAmelCase						:       int  			=     self.image_processor_tester.get_expected_values(a__				,     batched=a__  )

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






     def     __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:       str  			=     self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       _lowerCAmelCase						:       Tuple  			=     prepare_image_inputs(self.image_processor_tester				,     equal_resolution=a__				,     torchify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__				,     torch.Tensor  )

       # Test not batched input
       _lowerCAmelCase						:       Dict  			=     image_processing(image_inputs[0]				,     return_tensors="""pt"""  ).pixel_values

       _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[int]  			=     self.image_processor_tester.get_expected_values(a__  )

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

       # Test batched
       _lowerCAmelCase						:       Tuple  			=     image_processing(a__				,     return_tensors="""pt"""  ).pixel_values

       _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[int]  			=     self.image_processor_tester.get_expected_values(a__				,     batched=a__  )

       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  ):
       # prepare image and target
       _lowerCAmelCase						:       Tuple  			=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
       with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""				,     """r"""  ) as f:
         _lowerCAmelCase						:       Optional[Any]  			=     json.loads(f.read()  )

       _lowerCAmelCase						:       Optional[int]  			=     {"""image_id""": 39769, """annotations""": target}

       # encode them
       _lowerCAmelCase						:       Union[str, Any]  			=     ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""  )
       _lowerCAmelCase						:       Any  			=     image_processing(images=a__				,     annotations=a__				,     return_tensors="""pt"""  )

       # verify pixel values
       _lowerCAmelCase						:       Union[str, Any]  			=     torch.Size([1, 3, 800, 1066]  )
       self.assertEqual(encoding["""pixel_values"""].shape				,     a__  )

       _lowerCAmelCase						:       Optional[Any]  			=     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]  )
       self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]				,     a__				,     atol=1e-4  )  )

       # verify area
       _lowerCAmelCase						:       Optional[int]  			=     torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]  )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]				,     a__  )  )
       # verify boxes
       _lowerCAmelCase						:       str  			=     torch.Size([6, 4]  )
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				,     a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]  )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]				,     a__				,     atol=1e-3  )  )
       # verify image_id
       _lowerCAmelCase						:       Any  			=     torch.tensor([39769]  )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				,     a__  )  )
       # verify is_crowd
       _lowerCAmelCase						:       Union[str, Any]  			=     torch.tensor([0, 0, 0, 0, 0, 0]  )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				,     a__  )  )
       # verify class_labels
       _lowerCAmelCase						:       str  			=     torch.tensor([75, 75, 63, 65, 17, 17]  )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				,     a__  )  )
       # verify orig_size
       _lowerCAmelCase						:       int  			=     torch.tensor([480, 640]  )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				,     a__  )  )
       # verify size
       _lowerCAmelCase						:       str  			=     torch.tensor([800, 1066]  )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				,     a__  )  )






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

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

       _lowerCAmelCase						:       Tuple  			=     pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""  )

       # encode them
       _lowerCAmelCase						:       List[str]  			=     ConditionalDetrImageProcessor(format="""coco_panoptic"""  )
       _lowerCAmelCase						:       str  			=     image_processing(images=a__				,     annotations=a__				,     masks_path=a__				,     return_tensors="""pt"""  )

       # verify pixel values
       _lowerCAmelCase						:       List[Any]  			=     torch.Size([1, 3, 800, 1066]  )
       self.assertEqual(encoding["""pixel_values"""].shape				,     a__  )

       _lowerCAmelCase						:       List[str]  			=     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]  )
       self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]				,     a__				,     atol=1e-4  )  )

       # verify area
       _lowerCAmelCase						:       List[str]  			=     torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]  )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]				,     a__  )  )
       # verify boxes
       _lowerCAmelCase						:       Dict  			=     torch.Size([6, 4]  )
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				,     a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]  )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]				,     a__				,     atol=1e-3  )  )
       # verify image_id
       _lowerCAmelCase						:       List[Any]  			=     torch.tensor([39769]  )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				,     a__  )  )
       # verify is_crowd
       _lowerCAmelCase						:       Union[str, Any]  			=     torch.tensor([0, 0, 0, 0, 0, 0]  )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				,     a__  )  )
       # verify class_labels
       _lowerCAmelCase						:       Optional[int]  			=     torch.tensor([17, 17, 63, 75, 75, 93]  )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				,     a__  )  )
       # verify masks
       _lowerCAmelCase						:       int  			=     822873
       self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()				,     a__  )
       # verify orig_size
       _lowerCAmelCase						:       str  			=     torch.tensor([480, 640]  )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				,     a__  )  )
       # verify size
       _lowerCAmelCase						:       Union[str, Any]  			=     torch.tensor([800, 1066]  )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				,     a__  )  )





"""simple docstring"""




# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)




