

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      lowerCamelCase_      (	_lowercase						,      _lowercase						,      unittest.TestCase	):
	_lowercase		:   Tuple     					=				StableDiffusionDiffEditPipeline
	_lowercase		:   Optional[Any]     					=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
	_lowercase		:   Union[str, Any]     					=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
	_lowercase		:   Optional[int]     					=				frozenset(
	    []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	_lowercase		:   List[Any]     					=				frozenset([]	)




	def        lowerCAmelCase_       (     self						:     List[str]							):
		torch.manual_seed(0							)
		__A :			Optional[Any]		    =			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					,      )
		__A :			Dict		    =			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					,      )
		__A :			Union[str, Any]		    =			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							)
		__A :			Tuple		    =			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							)
		__A :			Tuple		    =			CLIPTextConfig(
		    bos_token_id=0					,      eos_token_id=2					,      hidden_size=32					,      intermediate_size=37					,      layer_norm_eps=1e-0_5					,      num_attention_heads=4					,      num_hidden_layers=5					,      pad_token_id=1					,      vocab_size=1000					,      hidden_act="""gelu"""					,      projection_dim=512					,      )
		__A :			Optional[Any]		    =			CLIPTextModel(__A							)
		__A :			Union[str, Any]		    =			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

		__A :			str		    =			{
		    """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        lowerCAmelCase_       (     self						:     Any					,      __A						:     List[str]					,      __A						:     List[Any]=0							):
		__A :			Optional[Any]		    =			floats_tensor((1, 16, 16)					,      rng=random.Random(__A							)							).to(__A							)
		__A :			Optional[Any]		    =			floats_tensor((1, 2, 4, 16, 16)					,      rng=random.Random(__A							)							).to(__A							)
		if str(__A							).startswith("""mps"""							):
			__A :			Dict		    =			torch.manual_seed(__A							)
		else:
			__A :			Tuple		    =			torch.Generator(device=__A							).manual_seed(__A							)
		__A :			Tuple		    =			{
		    """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        lowerCAmelCase_       (     self						:     Union[str, Any]					,      __A						:     int					,      __A						:     List[str]=0							):
		__A :			Dict		    =			floats_tensor((1, 3, 32, 32)					,      rng=random.Random(__A							)							).to(__A							)
		__A :			int		    =			image.cpu().permute(0					,      2					,      3					,      1							)[0]
		__A :			int		    =			Image.fromarray(np.uinta(__A							)							).convert("""RGB"""							)
		if str(__A							).startswith("""mps"""							):
			__A :			Any		    =			torch.manual_seed(__A							)
		else:
			__A :			Union[str, Any]		    =			torch.Generator(device=__A							).manual_seed(__A							)
		__A :			Optional[int]		    =			{
		    """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        lowerCAmelCase_       (     self						:     int					,      __A						:     str					,      __A						:     List[Any]=0							):
		__A :			str		    =			floats_tensor((1, 3, 32, 32)					,      rng=random.Random(__A							)							).to(__A							)
		__A :			int		    =			image.cpu().permute(0					,      2					,      3					,      1							)[0]
		__A :			Dict		    =			Image.fromarray(np.uinta(__A							)							).convert("""RGB"""							)
		if str(__A							).startswith("""mps"""							):
			__A :			List[str]		    =			torch.manual_seed(__A							)
		else:
			__A :			Any		    =			torch.Generator(device=__A							).manual_seed(__A							)
		__A :			int		    =			{
		    """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        lowerCAmelCase_       (     self						:     Dict							):
		if not hasattr(self.pipeline_class					,      """_optional_components"""							):
			return

		__A :			Tuple		    =			self.get_dummy_components()
		__A :			Tuple		    =			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}							)

		__A :			Optional[int]		    =			self.get_dummy_inputs(__A							)
		__A :			str		    =			pipe(**__A							)[0]

		with tempfile.TemporaryDirectory() as tmpdir:
			pipe.save_pretrained(__A							)
			__A :			str		    =			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."""					,      )

		__A :			int		    =			self.get_dummy_inputs(__A							)
		__A :			List[Any]		    =			pipe_loaded(**__A							)[0]

		__A :			str		    =			np.abs(output - output_loaded							).max()
		self.assertLess(__A					,      1e-4							)




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		__A :			str		    =			"""cpu"""

		__A :			int		    =			self.get_dummy_components()
		__A :			Any		    =			self.pipeline_class(**__A							)
		pipe.to(__A							)
		pipe.set_progress_bar_config(disable=__A							)

		__A :			Dict		    =			self.get_dummy_mask_inputs(__A							)
		__A :			int		    =			pipe.generate_mask(**__A							)
		__A :			Optional[Any]		    =			mask[0, -3:, -3:]

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




	def        lowerCAmelCase_       (     self						:     int							):
		__A :			int		    =			"""cpu"""

		__A :			List[Any]		    =			self.get_dummy_components()
		__A :			Optional[int]		    =			self.pipeline_class(**__A							)
		pipe.to(__A							)
		pipe.set_progress_bar_config(disable=__A							)

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

		self.assertEqual(image.shape					,      (2, 32, 32, 3)							)
		__A :			int		    =			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]					,      )
		__A :			Optional[int]		    =			np.abs(image_slice.flatten() - expected_slice							).max()
		self.assertLessEqual(__A					,      1e-3							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		super().test_inference_batch_single_identical(expected_max_diff=5e-3							)




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		__A :			Union[str, Any]		    =			"""cpu"""

		__A :			Optional[Any]		    =			self.get_dummy_components()

		__A :			Dict		    =			{"""beta_start""": 0.0_0_0_8_5, """beta_end""": 0.0_1_2, """beta_schedule""": """scaled_linear"""}
		__A :			Optional[int]		    =			DPMSolverMultistepScheduler(**__A							)
		__A :			Dict		    =			DPMSolverMultistepInverseScheduler(**__A							)

		__A :			List[Any]		    =			self.pipeline_class(**__A							)
		pipe.to(__A							)
		pipe.set_progress_bar_config(disable=__A							)

		__A :			Union[str, Any]		    =			self.get_dummy_inversion_inputs(__A							)
		__A :			Optional[int]		    =			pipe.invert(**__A							).images
		__A :			Any		    =			image[0, -1, -3:, -3:]

		self.assertEqual(image.shape					,      (2, 32, 32, 3)							)
		__A :			int		    =			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]					,      )
		__A :			int		    =			np.abs(image_slice.flatten() - expected_slice							).max()
		self.assertLessEqual(__A					,      1e-3							)






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




	def        lowerCAmelCase_       (     self						:     str							):
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()




	@classmethod
	def        lowerCAmelCase_       (     cls						:     str							):
		__A :			Dict		    =			load_image(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""							)

		__A :			Any		    =			raw_image.convert("""RGB"""							).resize((768, 768)							)

		__A :			Dict		    =			raw_image




	def        lowerCAmelCase_       (     self						:     List[str]							):
		__A :			List[str]		    =			torch.manual_seed(0							)

		__A :			Any		    =			StableDiffusionDiffEditPipeline.from_pretrained(
		    """stabilityai/stable-diffusion-2-1"""					,      safety_checker=__A					,      torch_dtype=torch.floataa							)
		__A :			int		    =			DDIMScheduler.from_config(pipe.scheduler.config							)
		__A :			List[str]		    =			DDIMInverseScheduler.from_config(pipe.scheduler.config							)
		pipe.enable_model_cpu_offload()
		pipe.set_progress_bar_config(disable=__A							)

		__A :			Optional[int]		    =			"""a bowl of fruit"""
		__A :			int		    =			"""a bowl of pears"""

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

		__A :			List[Any]		    =			pipe.invert(
		    prompt=__A					,      image=self.raw_image					,      inpaint_strength=0.7					,      generator=__A							).latents

		__A :			Optional[int]		    =			pipe(
		    prompt=__A					,      mask_image=__A					,      image_latents=__A					,      generator=__A					,      negative_prompt=__A					,      inpaint_strength=0.7					,      output_type="""numpy"""					,      ).images[0]

		__A :			Dict		    =			(
		    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        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A :			Tuple		    =			torch.manual_seed(0							)

		__A :			Optional[Any]		    =			StableDiffusionDiffEditPipeline.from_pretrained(
		    """stabilityai/stable-diffusion-2-1"""					,      safety_checker=__A					,      torch_dtype=torch.floataa							)
		__A :			Dict		    =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
		__A :			str		    =			DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config							)
		pipe.enable_model_cpu_offload()
		pipe.set_progress_bar_config(disable=__A							)

		__A :			Any		    =			"""a bowl of fruit"""
		__A :			List[str]		    =			"""a bowl of pears"""

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

		__A :			Optional[Any]		    =			pipe.invert(
		    prompt=__A					,      image=self.raw_image					,      inpaint_strength=0.7					,      generator=__A					,      num_inference_steps=25					,      ).latents

		__A :			Optional[Any]		    =			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]

		__A :			Dict		    =			(
		    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							)							)





