"""simple docstring"""

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 	UpperCAmelCase_			(		_lowercase     ,				_lowercase     ,				unittest.TestCase):
							snake_case__								= StableDiffusionDiffEditPipeline
							snake_case__								= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
							snake_case__								= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
							snake_case__								= frozenset(
							    [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							snake_case__								= frozenset([])






							def  _UpperCamelCase     (						self					: List[str]    )			->			List[Any]:
												torch.manual_seed(0    )
												_UpperCamelCase 		=      UNetaDConditionModel(
												    block_out_channels=(32, 64)	,		layers_per_block=2	,		sample_size=32	,		in_channels=4	,		out_channels=4	,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')	,		cross_attention_dim=32	,		attention_head_dim=(2, 4)	,		use_linear_projection=__A	,		)
												_UpperCamelCase 		=      DDIMScheduler(
												    beta_start=0.0_0_0_8_5	,		beta_end=0.0_1_2	,		beta_schedule='''scaled_linear'''	,		clip_sample=__A	,		set_alpha_to_one=__A	,		)
												_UpperCamelCase 		=      DDIMInverseScheduler(
												    beta_start=0.0_0_0_8_5	,		beta_end=0.0_1_2	,		beta_schedule='''scaled_linear'''	,		clip_sample=__A	,		set_alpha_to_zero=__A	,		)
												torch.manual_seed(0    )
												_UpperCamelCase 		=      AutoencoderKL(
												    block_out_channels=[32, 64]	,		in_channels=3	,		out_channels=3	,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']	,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']	,		latent_channels=4	,		sample_size=128	,		)
												torch.manual_seed(0    )
												_UpperCamelCase 		=      CLIPTextConfig(
												    bos_token_id=0	,		eos_token_id=2	,		hidden_size=32	,		intermediate_size=37	,		layer_norm_eps=1E-05	,		num_attention_heads=4	,		num_hidden_layers=5	,		pad_token_id=1	,		vocab_size=1000	,		hidden_act='''gelu'''	,		projection_dim=512	,		)
												_UpperCamelCase 		=      CLIPTextModel(__A    )
												_UpperCamelCase 		=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

												_UpperCamelCase 		=      {
												    """unet""": unet,
												    """scheduler""": scheduler,
												    """inverse_scheduler""": inverse_scheduler,
												    """vae""": vae,
												    """text_encoder""": text_encoder,
												    """tokenizer""": tokenizer,
												    """safety_checker""": None,
												    """feature_extractor""": None,
												}

												return components






							def  _UpperCamelCase     (						self					: Any	,		__UpperCamelCase					: List[str]	,		__UpperCamelCase					: List[Any]=0    )			->			Optional[int]:
												_UpperCamelCase 		=      floats_tensor((1, 16, 16)	,		rng=random.Random(__A    )    ).to(__A    )
												_UpperCamelCase 		=      floats_tensor((1, 2, 4, 16, 16)	,		rng=random.Random(__A    )    ).to(__A    )
												if str(__A    ).startswith('''mps'''    ):
																	_UpperCamelCase 		=      torch.manual_seed(__A    )
												else:
																	_UpperCamelCase 		=      torch.Generator(device=__A    ).manual_seed(__A    )
												_UpperCamelCase 		=      {
												    """prompt""": """a dog and a newt""",
												    """mask_image""": mask,
												    """image_latents""": latents,
												    """generator""": generator,
												    """num_inference_steps""": 2,
												    """inpaint_strength""": 1.0,
												    """guidance_scale""": 6.0,
												    """output_type""": """numpy""",
												}

												return inputs






							def  _UpperCamelCase     (						self					: Union[str, Any]	,		__UpperCamelCase					: int	,		__UpperCamelCase					: List[str]=0    )			->			List[str]:
												_UpperCamelCase 		=      floats_tensor((1, 3, 32, 32)	,		rng=random.Random(__A    )    ).to(__A    )
												_UpperCamelCase 		=      image.cpu().permute(0	,		2	,		3	,		1    )[0]
												_UpperCamelCase 		=      Image.fromarray(np.uinta(__A    )    ).convert('''RGB'''    )
												if str(__A    ).startswith('''mps'''    ):
																	_UpperCamelCase 		=      torch.manual_seed(__A    )
												else:
																	_UpperCamelCase 		=      torch.Generator(device=__A    ).manual_seed(__A    )
												_UpperCamelCase 		=      {
												    """image""": image,
												    """source_prompt""": """a cat and a frog""",
												    """target_prompt""": """a dog and a newt""",
												    """generator""": generator,
												    """num_inference_steps""": 2,
												    """num_maps_per_mask""": 2,
												    """mask_encode_strength""": 1.0,
												    """guidance_scale""": 6.0,
												    """output_type""": """numpy""",
												}

												return inputs






							def  _UpperCamelCase     (						self					: int	,		__UpperCamelCase					: str	,		__UpperCamelCase					: List[Any]=0    )			->			Optional[Any]:
												_UpperCamelCase 		=      floats_tensor((1, 3, 32, 32)	,		rng=random.Random(__A    )    ).to(__A    )
												_UpperCamelCase 		=      image.cpu().permute(0	,		2	,		3	,		1    )[0]
												_UpperCamelCase 		=      Image.fromarray(np.uinta(__A    )    ).convert('''RGB'''    )
												if str(__A    ).startswith('''mps'''    ):
																	_UpperCamelCase 		=      torch.manual_seed(__A    )
												else:
																	_UpperCamelCase 		=      torch.Generator(device=__A    ).manual_seed(__A    )
												_UpperCamelCase 		=      {
												    """image""": image,
												    """prompt""": """a cat and a frog""",
												    """generator""": generator,
												    """num_inference_steps""": 2,
												    """inpaint_strength""": 1.0,
												    """guidance_scale""": 6.0,
												    """decode_latents""": True,
												    """output_type""": """numpy""",
												}
												return inputs






							def  _UpperCamelCase     (						self					: Dict    )			->			Optional[Any]:
												if not hasattr(self.pipeline_class	,		'''_optional_components'''    ):
																	return

												_UpperCamelCase 		=      self.get_dummy_components()
												_UpperCamelCase 		=      self.pipeline_class(**__A    )
												pipe.to(__A    )
												pipe.set_progress_bar_config(disable=__A    )

												# set all optional components to None and update pipeline config accordingly
												for optional_component in pipe._optional_components:
																	setattr(__A	,		__A	,		__A    )
												pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}    )

												_UpperCamelCase 		=      self.get_dummy_inputs(__A    )
												_UpperCamelCase 		=      pipe(**__A    )[0]

												with tempfile.TemporaryDirectory() as tmpdir:
																	pipe.save_pretrained(__A    )
																	_UpperCamelCase 		=      self.pipeline_class.from_pretrained(__A    )
																	pipe_loaded.to(__A    )
																	pipe_loaded.set_progress_bar_config(disable=__A    )

												for optional_component in pipe._optional_components:
																	self.assertTrue(
																	    getattr(__A	,		__A    ) is None	,		F'''`{optional_component}` did not stay set to None after loading.'''	,		)

												_UpperCamelCase 		=      self.get_dummy_inputs(__A    )
												_UpperCamelCase 		=      pipe_loaded(**__A    )[0]

												_UpperCamelCase 		=      np.abs(output - output_loaded    ).max()
												self.assertLess(__A	,		1E-4    )






							def  _UpperCamelCase     (						self					: List[Any]    )			->			str:
												_UpperCamelCase 		=      """cpu"""

												_UpperCamelCase 		=      self.get_dummy_components()
												_UpperCamelCase 		=      self.pipeline_class(**__A    )
												pipe.to(__A    )
												pipe.set_progress_bar_config(disable=__A    )

												_UpperCamelCase 		=      self.get_dummy_mask_inputs(__A    )
												_UpperCamelCase 		=      pipe.generate_mask(**__A    )
												_UpperCamelCase 		=      mask[0, -3:, -3:]

												self.assertEqual(mask.shape	,		(1, 16, 16)    )
												_UpperCamelCase 		=      np.array([0] * 9    )
												_UpperCamelCase 		=      np.abs(mask_slice.flatten() - expected_slice    ).max()
												self.assertLessEqual(__A	,		1E-3    )
												self.assertEqual(mask[0, -3, -4]	,		0    )






							def  _UpperCamelCase     (						self					: int    )			->			List[Any]:
												_UpperCamelCase 		=      """cpu"""

												_UpperCamelCase 		=      self.get_dummy_components()
												_UpperCamelCase 		=      self.pipeline_class(**__A    )
												pipe.to(__A    )
												pipe.set_progress_bar_config(disable=__A    )

												_UpperCamelCase 		=      self.get_dummy_inversion_inputs(__A    )
												_UpperCamelCase 		=      pipe.invert(**__A    ).images
												_UpperCamelCase 		=      image[0, -1, -3:, -3:]

												self.assertEqual(image.shape	,		(2, 32, 32, 3)    )
												_UpperCamelCase 		=      np.array(
												    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]	,		)
												_UpperCamelCase 		=      np.abs(image_slice.flatten() - expected_slice    ).max()
												self.assertLessEqual(__A	,		1E-3    )






							def  _UpperCamelCase     (						self					: Optional[Any]    )			->			str:
												super().test_inference_batch_single_identical(expected_max_diff=5E-3    )






							def  _UpperCamelCase     (						self					: List[Any]    )			->			int:
												_UpperCamelCase 		=      """cpu"""

												_UpperCamelCase 		=      self.get_dummy_components()

												_UpperCamelCase 		=      {"""beta_start""": 0.0_0_0_8_5, """beta_end""": 0.0_1_2, """beta_schedule""": """scaled_linear"""}
												_UpperCamelCase 		=      DPMSolverMultistepScheduler(**__A    )
												_UpperCamelCase 		=      DPMSolverMultistepInverseScheduler(**__A    )

												_UpperCamelCase 		=      self.pipeline_class(**__A    )
												pipe.to(__A    )
												pipe.set_progress_bar_config(disable=__A    )

												_UpperCamelCase 		=      self.get_dummy_inversion_inputs(__A    )
												_UpperCamelCase 		=      pipe.invert(**__A    ).images
												_UpperCamelCase 		=      image[0, -1, -3:, -3:]

												self.assertEqual(image.shape	,		(2, 32, 32, 3)    )
												_UpperCamelCase 		=      np.array(
												    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]	,		)
												_UpperCamelCase 		=      np.abs(image_slice.flatten() - expected_slice    ).max()
												self.assertLessEqual(__A	,		1E-3    )







@require_torch_gpu
@slow
class 	UpperCAmelCase_			(		unittest.TestCase):






							def  _UpperCamelCase     (						self					: str    )			->			Union[str, Any]:
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






							@classmethod
							def  _UpperCamelCase     (						cls					: str    )			->			str:
												_UpperCamelCase 		=      load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''    )

												_UpperCamelCase 		=      raw_image.convert('''RGB'''    ).resize((768, 768)    )

												_UpperCamelCase 		=      raw_image






							def  _UpperCamelCase     (						self					: List[str]    )			->			str:
												_UpperCamelCase 		=      torch.manual_seed(0    )

												_UpperCamelCase 		=      StableDiffusionDiffEditPipeline.from_pretrained(
												    '''stabilityai/stable-diffusion-2-1'''	,		safety_checker=__A	,		torch_dtype=torch.floataa    )
												_UpperCamelCase 		=      DDIMScheduler.from_config(pipe.scheduler.config    )
												_UpperCamelCase 		=      DDIMInverseScheduler.from_config(pipe.scheduler.config    )
												pipe.enable_model_cpu_offload()
												pipe.set_progress_bar_config(disable=__A    )

												_UpperCamelCase 		=      """a bowl of fruit"""
												_UpperCamelCase 		=      """a bowl of pears"""

												_UpperCamelCase 		=      pipe.generate_mask(
												    image=self.raw_image	,		source_prompt=__A	,		target_prompt=__A	,		generator=__A	,		)

												_UpperCamelCase 		=      pipe.invert(
												    prompt=__A	,		image=self.raw_image	,		inpaint_strength=0.7	,		generator=__A    ).latents

												_UpperCamelCase 		=      pipe(
												    prompt=__A	,		mask_image=__A	,		image_latents=__A	,		generator=__A	,		negative_prompt=__A	,		inpaint_strength=0.7	,		output_type='''numpy'''	,		).images[0]

												_UpperCamelCase 		=      (
												    np.array(
												        load_image(
												            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												            '''/diffedit/pears.png'''    ).resize((768, 768)    )    )
												    / 255
												)
												assert np.abs((expected_image - image).max()    ) < 5E-1






							def  _UpperCamelCase     (						self					: Union[str, Any]    )			->			Dict:
												_UpperCamelCase 		=      torch.manual_seed(0    )

												_UpperCamelCase 		=      StableDiffusionDiffEditPipeline.from_pretrained(
												    '''stabilityai/stable-diffusion-2-1'''	,		safety_checker=__A	,		torch_dtype=torch.floataa    )
												_UpperCamelCase 		=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
												_UpperCamelCase 		=      DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
												pipe.enable_model_cpu_offload()
												pipe.set_progress_bar_config(disable=__A    )

												_UpperCamelCase 		=      """a bowl of fruit"""
												_UpperCamelCase 		=      """a bowl of pears"""

												_UpperCamelCase 		=      pipe.generate_mask(
												    image=self.raw_image	,		source_prompt=__A	,		target_prompt=__A	,		generator=__A	,		)

												_UpperCamelCase 		=      pipe.invert(
												    prompt=__A	,		image=self.raw_image	,		inpaint_strength=0.7	,		generator=__A	,		num_inference_steps=25	,		).latents

												_UpperCamelCase 		=      pipe(
												    prompt=__A	,		mask_image=__A	,		image_latents=__A	,		generator=__A	,		negative_prompt=__A	,		inpaint_strength=0.7	,		num_inference_steps=25	,		output_type='''numpy'''	,		).images[0]

												_UpperCamelCase 		=      (
												    np.array(
												        load_image(
												            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												            '''/diffedit/pears.png'''    ).resize((768, 768)    )    )
												    / 255
												)
												assert np.abs((expected_image - image).max()    ) < 5E-1



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




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

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

		return expected_height, expected_width






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




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			Tuple		    =			DetaImageProcessingTester(self							)




	@property
	def        lowerCAmelCase_       (     self						:     List[str]							):
		return self.image_processor_tester.prepare_image_processor_dict()




	def        lowerCAmelCase_       (     self						:     List[str]							):
		__A :			Tuple		    =			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					,      """do_rescale"""							)							)
		self.assertTrue(hasattr(__A					,      """do_pad"""							)							)
		self.assertTrue(hasattr(__A					,      """size"""							)							)




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			Dict		    =			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							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		pass




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

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

		__A    ,				__A :			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
		__A    ,				__A :			List[Any]		    =			self.image_processor_tester.get_expected_values(__A					,      batched=__A							)

		__A :			List[str]		    =			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        lowerCAmelCase_       (     self						:     Optional[int]							):
		# Initialize image_processing
		__A :			Tuple		    =			self.image_processing_class(**self.image_processor_dict							)
		# create random numpy tensors
		__A :			List[str]		    =			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
		__A :			str		    =			image_processing(image_inputs[0]					,      return_tensors="""pt"""							).pixel_values

		__A    ,				__A :			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
		__A :			int		    =			image_processing(__A					,      return_tensors="""pt"""							).pixel_values

		__A    ,				__A :			Optional[Any]		    =			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        lowerCAmelCase_       (     self						:     Dict							):
		# Initialize image_processing
		__A :			int		    =			self.image_processing_class(**self.image_processor_dict							)
		# create random PyTorch tensors
		__A :			Dict		    =			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
		__A :			Tuple		    =			image_processing(image_inputs[0]					,      return_tensors="""pt"""							).pixel_values

		__A    ,				__A :			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
		__A :			Tuple		    =			image_processing(__A					,      return_tensors="""pt"""							).pixel_values

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

		__A :			int		    =			{"""image_id""": 3_9769, """annotations""": target}

		# encode them
		__A :			List[str]		    =			DetaImageProcessor()
		__A :			List[str]		    =			image_processing(images=__A					,      annotations=__A					,      return_tensors="""pt"""							)

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

		__A :			Union[str, 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
		__A :			Tuple		    =			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
		__A :			Tuple		    =			torch.Size([6, 4]							)
		self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape					,      __A							)
		__A :			List[str]		    =			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
		__A :			List[str]		    =			torch.tensor([3_9769]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]					,      __A							)							)
		# verify is_crowd
		__A :			Dict		    =			torch.tensor([0, 0, 0, 0, 0, 0]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]					,      __A							)							)
		# verify class_labels
		__A :			List[Any]		    =			torch.tensor([75, 75, 63, 65, 17, 17]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]					,      __A							)							)
		# verify orig_size
		__A :			str		    =			torch.tensor([480, 640]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]					,      __A							)							)
		# verify size
		__A :			Any		    =			torch.tensor([800, 1066]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,      __A							)							)




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

		__A :			Optional[int]		    =			{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

		__A :			Any		    =			pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

		# encode them
		__A :			Any		    =			DetaImageProcessor(format="""coco_panoptic"""							)
		__A :			int		    =			image_processing(images=__A					,      annotations=__A					,      masks_path=__A					,      return_tensors="""pt"""							)

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

		__A :			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
		__A :			Union[str, Any]		    =			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
		__A :			Union[str, Any]		    =			torch.Size([6, 4]							)
		self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape					,      __A							)
		__A :			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
		__A :			Any		    =			torch.tensor([3_9769]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]					,      __A							)							)
		# verify is_crowd
		__A :			Any		    =			torch.tensor([0, 0, 0, 0, 0, 0]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]					,      __A							)							)
		# verify class_labels
		__A :			Optional[Any]		    =			torch.tensor([17, 17, 63, 75, 75, 93]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]					,      __A							)							)
		# verify masks
		__A :			List[str]		    =			82_2873
		self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()					,      __A							)
		# verify orig_size
		__A :			Tuple		    =			torch.tensor([480, 640]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]					,      __A							)							)
		# verify size
		__A :			Any		    =			torch.tensor([800, 1066]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,      __A							)							)





