





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





							'''simple docstring'''







							lowerCAmelCase__						=					KandinskyImgaImgPipeline
							lowerCAmelCase__						=					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
							lowerCAmelCase__						=					[
							    """prompt""",
							    """negative_prompt""",
							    """image_embeds""",
							    """negative_image_embeds""",
							    """image""",
							]
							lowerCAmelCase__						=					[
							    """generator""",
							    """height""",
							    """width""",
							    """strength""",
							    """guidance_scale""",
							    """negative_prompt""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							lowerCAmelCase__						=					False




							@property
							def 							UpperCAmelCase__			(  self			:   Dict		):
									return 32




							@property
							def 							UpperCAmelCase__			(  self			:   Any		):
									return 32




							@property
							def 							UpperCAmelCase__			(  self			:   str		):
									return self.time_input_dim




							@property
							def 							UpperCAmelCase__			(  self			:   Any		):
									return self.time_input_dim * 4




							@property
							def 							UpperCAmelCase__			(  self			:   List[Any]		):
									return 100




							@property
							def 							UpperCAmelCase__			(  self			:   Any		):
									__snake_case:						Optional[Any]			  =						XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""		)
									return tokenizer




							@property
							def 							UpperCAmelCase__			(  self			:   Tuple		):
									torch.manual_seed(0		)
									__snake_case:						str			  =						MCLIPConfig(
									    numDims=self.cross_attention_dim							,    transformerDimensions=self.text_embedder_hidden_size							,    hidden_size=self.text_embedder_hidden_size							,    intermediate_size=37							,    num_attention_heads=4							,    num_hidden_layers=5							,    vocab_size=1_005							,    )

									__snake_case:						Any			  =						MultilingualCLIP(A		)
									__snake_case:						List[Any]			  =						text_encoder.eval()

									return text_encoder




							@property
							def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
									torch.manual_seed(0		)

									__snake_case:						str			  =						{
									    """in_channels""": 4,
									    # Out channels is double in channels because predicts mean and variance
									    """out_channels""": 8,
									    """addition_embed_type""": """text_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""": """text_image_proj""",
									    """cross_attention_dim""": self.cross_attention_dim,
									    """attention_head_dim""": 4,
									    """resnet_time_scale_shift""": """scale_shift""",
									    """class_embed_type""": None,
									}

									__snake_case:						Union[str, Any]			  =						UNetaDConditionModel(**A		)
									return model




							@property
							def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
									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			:   Optional[Any]		):
									torch.manual_seed(0		)
									__snake_case:						Union[str, Any]			  =						VQModel(**self.dummy_movq_kwargs		)
									return model




							def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
									__snake_case:						Any			  =						self.dummy_text_encoder
									__snake_case:						Optional[Any]			  =						self.dummy_tokenizer
									__snake_case:						Dict			  =						self.dummy_unet
									__snake_case:						Optional[int]			  =						self.dummy_movq

									__snake_case:						Dict			  =						{
									    """num_train_timesteps""": 1_000,
									    """beta_schedule""": """linear""",
									    """beta_start""": 0.0_0085,
									    """beta_end""": 0.012,
									    """clip_sample""": False,
									    """set_alpha_to_one""": False,
									    """steps_offset""": 0,
									    """prediction_type""": """epsilon""",
									    """thresholding""": False,
									}

									__snake_case:						List[str]			  =						DDIMScheduler(**A		)

									__snake_case:						Union[str, Any]			  =						{
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """movq""": movq,
									}

									return components




							def 							UpperCAmelCase__			(  self			:   Optional[int]							,    A			:   Optional[Any]							,    A			:   Tuple=0		):
									__snake_case:						List[Any]			  =						floats_tensor((1, self.cross_attention_dim)							,    rng=random.Random(A		)		).to(A		)
									__snake_case:						Tuple			  =						floats_tensor((1, self.cross_attention_dim)							,    rng=random.Random(seed + 1		)		).to(A		)
									# create init_image
									__snake_case:						int			  =						floats_tensor((1, 3, 64, 64)							,    rng=random.Random(A		)		).to(A		)
									__snake_case:						Any			  =						image.cpu().permute(0							,    2							,    3							,    1		)[0]
									__snake_case:						Dict			  =						Image.fromarray(np.uinta(A		)		).convert("""RGB"""		).resize((256, 256)		)

									if str(A		).startswith("""mps"""		):
											__snake_case:						List[Any]			  =						torch.manual_seed(A		)
									else:
											__snake_case:						Union[str, Any]			  =						torch.Generator(device=A		).manual_seed(A		)
									__snake_case:						Any			  =						{
									    """prompt""": """horse""",
									    """image""": init_image,
									    """image_embeds""": image_embeds,
									    """negative_image_embeds""": negative_image_embeds,
									    """generator""": generator,
									    """height""": 64,
									    """width""": 64,
									    """num_inference_steps""": 10,
									    """guidance_scale""": 7.0,
									    """strength""": 0.2,
									    """output_type""": """np""",
									}
									return inputs






							def 							UpperCAmelCase__			(  self			:   Tuple		):
									__snake_case:						Optional[Any]			  =						"""cpu"""

									__snake_case:						int			  =						self.get_dummy_components()

									__snake_case:						List[str]			  =						self.pipeline_class(**A		)
									__snake_case:						List[Any]			  =						pipe.to(A		)

									pipe.set_progress_bar_config(disable=A		)

									__snake_case:						int			  =						pipe(**self.get_dummy_inputs(A		)		)
									__snake_case:						Optional[int]			  =						output.images

									__snake_case:						Union[str, Any]			  =						pipe(
									    **self.get_dummy_inputs(A		)							,    return_dict=A							,    )[0]

									__snake_case:						Tuple			  =						image[0, -3:, -3:, -1]
									__snake_case:						int			  =						image_from_tuple[0, -3:, -3:, -1]

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

									__snake_case:						int			  =						np.array(
									    [0.6147_4943, 0.607_3539, 0.4330_8544, 0.592_8269, 0.4749_3595, 0.4675_5973, 0.461_3838, 0.4536_8797, 0.5011_9233]		)
									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()}'''







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





							'''simple docstring'''







							def 							UpperCAmelCase__			(  self			:   Optional[int]		):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






							def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
									__snake_case:						Optional[Any]			  =						load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/kandinsky/kandinsky_img2img_frog.npy"""		)

									__snake_case:						List[Any]			  =						load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""		)
									__snake_case:						int			  =						"""A red cartoon frog, 4k"""

									__snake_case:						Any			  =						KandinskyPriorPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-1-prior"""							,    torch_dtype=torch.floataa		)
									pipe_prior.to(A		)

									__snake_case:						List[Any]			  =						KandinskyImgaImgPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-1"""							,    torch_dtype=torch.floataa		)
									__snake_case:						List[str]			  =						pipeline.to(A		)

									pipeline.set_progress_bar_config(disable=A		)

									__snake_case:						Tuple			  =						torch.Generator(device="""cpu"""		).manual_seed(0		)
									__snake_case:						Tuple			  =						pipe_prior(
									    A							,    generator=A							,    num_inference_steps=5							,    negative_prompt=""""""							,    ).to_tuple()

									__snake_case:						int			  =						pipeline(
									    A							,    image=A							,    image_embeds=A							,    negative_image_embeds=A							,    generator=A							,    num_inference_steps=100							,    height=768							,    width=768							,    strength=0.2							,    output_type="""np"""							,    )

									__snake_case:						List[Any]			  =						output.images[0]

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

									assert_mean_pixel_difference(A							,    A		)













import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       pass







@nightly
@require_onnxruntime
@require_torch_gpu
class    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )




       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Optional[int]			  =						ort.SessionOptions()
         __snake_case:						List[Any]			  =						False
         return options




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Any			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						List[str]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						int			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Any			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=10							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[Any]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Any			  =						np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3






       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Optional[int]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						Optional[int]			  =						LMSDiscreteScheduler.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    subfolder="""scheduler"""							,    revision="""onnx"""		)
         __snake_case:						List[Any]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    scheduler=A							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						Optional[int]			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Dict			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=20							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[str]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Union[str, Any]			  =						np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3






