

import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
		from PIL import Image
else:



		class 				__UpperCAmelCase		:







					@staticmethod
					def 							UpperCamelCase				(     *UpperCAmelCase_:  Optional[Any]				,     **UpperCAmelCase_:  int					):







											'''simple docstring'''




											pass

def 	__lowerCamelCase      (		snake_case__		)							->  str:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  hashlib.mda(image.tobytes()		)
						return m.hexdigest()[:10]

def 	__lowerCamelCase      (		snake_case__		)							->  Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  np.array(snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  npimg.shape
						return {"hash": hashimage(snake_case__		), "shape": shape}




@is_pipeline_test
@require_vision
@require_torch
class 				__UpperCAmelCase		(unittest.TestCase   ):
			__snake_case    :				str									=							dict(
			    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])   )
			__snake_case    :				Any									=							dict(
			    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])   )







			def 							UpperCamelCase				(     self:  Any				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  List[Any]				,     UpperCAmelCase_:  Union[str, Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  MaskGenerationPipeline(model=UpperCAmelCase_				,     image_processor=UpperCAmelCase_					)
									return image_segmenter, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]







			def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  Tuple					):







									'''simple docstring'''




									pass







			@require_tf
			@unittest.skip("""Image segmentation not implemented in TF"""					)
			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									pass







			@slow
			@require_torch
			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  pipeline("""mask-generation"""				,     model="""facebook/sam-vit-huge"""					)

									_SCREAMING_SNAKE_CASE     				=  image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""				,     points_per_batch=256					)

									# Shortening by hashing
									_SCREAMING_SNAKE_CASE     				=  []
									for i, o in enumerate(outputs["""masks"""]					):
															new_outupt += [{"mask": mask_to_test_readable(UpperCAmelCase_					), "scores": outputs["scores"][i]}]

									# fmt: off
									self.assertEqual(
									    nested_simplify(UpperCAmelCase_				,     decimals=4					)				,     [
									        {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.04_44},
									        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0_21},
									        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.01_67},
									        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.01_32},
									        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.00_53},
									        {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (480, 640)}, """scores""": 0.99_67},
									        {"""mask""": {"""hash""": """453c7844bd""", """shape""": (480, 640)}, """scores""": 0.9_93},
									        {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (480, 640)}, """scores""": 0.99_09},
									        {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (480, 640)}, """scores""": 0.98_79},
									        {"""mask""": {"""hash""": """801064ff79""", """shape""": (480, 640)}, """scores""": 0.98_34},
									        {"""mask""": {"""hash""": """6172f276ef""", """shape""": (480, 640)}, """scores""": 0.97_16},
									        {"""mask""": {"""hash""": """b49e60e084""", """shape""": (480, 640)}, """scores""": 0.96_12},
									        {"""mask""": {"""hash""": """a811e775fd""", """shape""": (480, 640)}, """scores""": 0.95_99},
									        {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (480, 640)}, """scores""": 0.95_52},
									        {"""mask""": {"""hash""": """9d8257e080""", """shape""": (480, 640)}, """scores""": 0.95_32},
									        {"""mask""": {"""hash""": """32de6454a8""", """shape""": (480, 640)}, """scores""": 0.95_16},
									        {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (480, 640)}, """scores""": 0.94_99},
									        {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (480, 640)}, """scores""": 0.94_83},
									        {"""mask""": {"""hash""": """c290813fb9""", """shape""": (480, 640)}, """scores""": 0.94_64},
									        {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (480, 640)}, """scores""": 0.9_43},
									        {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (480, 640)}, """scores""": 0.9_43},
									        {"""mask""": {"""hash""": """c749b25868""", """shape""": (480, 640)}, """scores""": 0.94_08},
									        {"""mask""": {"""hash""": """efb6cab859""", """shape""": (480, 640)}, """scores""": 0.93_35},
									        {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (480, 640)}, """scores""": 0.93_26},
									        {"""mask""": {"""hash""": """788b798e24""", """shape""": (480, 640)}, """scores""": 0.92_62},
									        {"""mask""": {"""hash""": """abea804f0e""", """shape""": (480, 640)}, """scores""": 0.89_99},
									        {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (480, 640)}, """scores""": 0.89_86},
									        {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (480, 640)}, """scores""": 0.89_84},
									        {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (480, 640)}, """scores""": 0.88_73},
									        {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (480, 640)}, """scores""": 0.88_71}
									    ]				,     )
									# fmt: on







			@require_torch
			@slow
			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """facebook/sam-vit-huge"""
									_SCREAMING_SNAKE_CASE     				=  pipeline("""mask-generation"""				,     model=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  image_segmenter(
									    """http://images.cocodataset.org/val2017/000000039769.jpg"""				,     pred_iou_thresh=1				,     points_per_batch=256					)

									# Shortening by hashing
									_SCREAMING_SNAKE_CASE     				=  []
									for i, o in enumerate(outputs["""masks"""]					):
															new_outupt += [{"mask": mask_to_test_readable(UpperCAmelCase_					), "scores": outputs["scores"][i]}]

									self.assertEqual(
									    nested_simplify(UpperCAmelCase_				,     decimals=4					)				,     [
									        {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.04_44},
									        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.02_10},
									        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.01_67},
									        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.01_32},
									        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.00_53},
									    ]				,     )









import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 				__UpperCAmelCase		(_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				List[str]									=							KandinskyVaaInpaintPipeline
			__snake_case    :				Union[str, Any]									=							["image_embeds", "negative_image_embeds", "image", "mask_image"]
			__snake_case    :				Tuple									=							[
			    "image_embeds",
			    "negative_image_embeds",
			    "image",
			    "mask_image",
			]
			__snake_case    :				str									=							[
			    "generator",
			    "height",
			    "width",
			    "latents",
			    "guidance_scale",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			__snake_case    :				List[str]									=							False







			@property
			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									return 32







			@property
			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									return 32







			@property
			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									return self.time_input_dim







			@property
			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									return self.time_input_dim * 4







			@property
			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									return 100







			@property
			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									torch.manual_seed(0					)

									_SCREAMING_SNAKE_CASE     				=  {
									    """in_channels""": 9,
									    # Out channels is double in channels because predicts mean and variance
									    """out_channels""": 8,
									    """addition_embed_type""": """image""",
									    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
									    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
									    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
									    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    """layers_per_block""": 1,
									    """encoder_hid_dim""": self.text_embedder_hidden_size,
									    """encoder_hid_dim_type""": """image_proj""",
									    """cross_attention_dim""": self.cross_attention_dim,
									    """attention_head_dim""": 4,
									    """resnet_time_scale_shift""": """scale_shift""",
									    """class_embed_type""": None,
									}

									_SCREAMING_SNAKE_CASE     				=  UNetaDConditionModel(**UpperCAmelCase_					)
									return model







			@property
			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									return {
									    "block_out_channels": [32, 64],
									    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
									    "in_channels": 3,
									    "latent_channels": 4,
									    "layers_per_block": 1,
									    "norm_num_groups": 8,
									    "norm_type": "spatial",
									    "num_vq_embeddings": 12,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}







			@property
			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  VQModel(**self.dummy_movq_kwargs					)
									return model







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.dummy_unet
									_SCREAMING_SNAKE_CASE     				=  self.dummy_movq

									_SCREAMING_SNAKE_CASE     				=  DDIMScheduler(
									    num_train_timesteps=1_000				,     beta_schedule="""linear"""				,     beta_start=0.0_00_85				,     beta_end=0.0_12				,     clip_sample=UpperCAmelCase_				,     set_alpha_to_one=UpperCAmelCase_				,     steps_offset=1				,     prediction_type="""epsilon"""				,     thresholding=UpperCAmelCase_				,     )

									_SCREAMING_SNAKE_CASE     				=  {
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """movq""": movq,
									}

									return components







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  List[str]=0					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  floats_tensor((1, self.text_embedder_hidden_size)				,     rng=random.Random(UpperCAmelCase_					)					).to(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  floats_tensor((1, self.text_embedder_hidden_size)				,     rng=random.Random(seed + 1					)					).to(
									    UpperCAmelCase_					)
									# create init_image
									_SCREAMING_SNAKE_CASE     				=  floats_tensor((1, 3, 64, 64)				,     rng=random.Random(UpperCAmelCase_					)					).to(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  image.cpu().permute(0				,     2				,     3				,     1					)[0]
									_SCREAMING_SNAKE_CASE     				=  Image.fromarray(np.uinta(UpperCAmelCase_					)					).convert("""RGB"""					).resize((256, 256)					)
									# create mask
									_SCREAMING_SNAKE_CASE     				=  np.ones((64, 64)				,     dtype=np.floataa					)
									_SCREAMING_SNAKE_CASE     				=  0

									if str(UpperCAmelCase_					).startswith("""mps"""					):
															_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(UpperCAmelCase_					)
									else:
															_SCREAMING_SNAKE_CASE     				=  torch.Generator(device=UpperCAmelCase_					).manual_seed(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  {
									    """image""": init_image,
									    """mask_image""": mask,
									    """image_embeds""": image_embeds,
									    """negative_image_embeds""": negative_image_embeds,
									    """generator""": generator,
									    """height""": 64,
									    """width""": 64,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 4.0,
									    """output_type""": """np""",
									}
									return inputs







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """cpu"""

									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_components()

									_SCREAMING_SNAKE_CASE     				=  self.pipeline_class(**UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  pipe.to(UpperCAmelCase_					)

									pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  pipe(**self.get_dummy_inputs(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  output.images

									_SCREAMING_SNAKE_CASE     				=  pipe(
									    **self.get_dummy_inputs(UpperCAmelCase_					)				,     return_dict=UpperCAmelCase_				,     )[0]

									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  image_from_tuple[0, -3:, -3:, -1]

									print(F'image.shape {image.shape}'					)

									assert image.shape == (1, 64, 64, 3)

									_SCREAMING_SNAKE_CASE     				=  np.array(
									    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]					)

									assert (
									    np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
									), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
									assert (
									    np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2
									), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									super().test_inference_batch_single_identical(expected_max_diff=3E-3					)



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







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""					)

									_SCREAMING_SNAKE_CASE     				=  load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""					)
									_SCREAMING_SNAKE_CASE     				=  np.ones((768, 768)				,     dtype=np.floataa					)
									_SCREAMING_SNAKE_CASE     				=  0

									_SCREAMING_SNAKE_CASE     				=  """a hat"""

									_SCREAMING_SNAKE_CASE     				=  KandinskyVaaPriorPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-2-prior"""				,     torch_dtype=torch.floataa					)
									pipe_prior.to(UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  KandinskyVaaInpaintPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-2-decoder-inpaint"""				,     torch_dtype=torch.floataa					)
									_SCREAMING_SNAKE_CASE     				=  pipeline.to(UpperCAmelCase_					)
									pipeline.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  torch.Generator(device="""cpu"""					).manual_seed(0					)
									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  pipe_prior(
									    UpperCAmelCase_				,     generator=UpperCAmelCase_				,     num_inference_steps=5				,     negative_prompt=""""""				,     ).to_tuple()

									_SCREAMING_SNAKE_CASE     				=  pipeline(
									    image=UpperCAmelCase_				,     mask_image=UpperCAmelCase_				,     image_embeds=UpperCAmelCase_				,     negative_image_embeds=UpperCAmelCase_				,     generator=UpperCAmelCase_				,     num_inference_steps=100				,     height=768				,     width=768				,     output_type="""np"""				,     )

									_SCREAMING_SNAKE_CASE     				=  output.images[0]

									assert image.shape == (768, 768, 3)

									assert_mean_pixel_difference(UpperCAmelCase_				,     UpperCAmelCase_					)

