


import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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        a					(			_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Optional[Any]						      =	KandinskyInpaintPipeline
     UpperCAmelCase__ :  Optional[Any]						      =	["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
     UpperCAmelCase__ :  Union[str, Any]						      =	[
         "prompt",
         "negative_prompt",
         "image_embeds",
         "negative_image_embeds",
         "image",
         "mask_image",
     ]
     UpperCAmelCase__ :  Optional[int]						      =	[
         "generator",
         "height",
         "width",
         "latents",
         "guidance_scale",
         "negative_prompt",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     UpperCAmelCase__ :  str						      =	False





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        return 32





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        return 32





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        return self.time_input_dim





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        return self.time_input_dim * 4





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        return 100





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





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        torch.manual_seed(0							)
        __lowerCamelCase:				Any     =	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=1005    ,	)

        __lowerCamelCase:				Optional[Any]     =	MultilingualCLIP(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Union[str, Any]     =	text_encoder.eval()

        return text_encoder





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        torch.manual_seed(0							)

        __lowerCamelCase:				List[str]     =	{
            """in_channels""": 9,
            # 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,
        }

        __lowerCamelCase:				Optional[int]     =	UNetaDConditionModel(**SCREAMING_SNAKE_CASE_							)
        return model





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[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  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        torch.manual_seed(0							)
        __lowerCamelCase:				Dict     =	VQModel(**self.dummy_movq_kwargs							)
        return model





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				List[str]     =	self.dummy_text_encoder
        __lowerCamelCase:				List[str]     =	self.dummy_tokenizer
        __lowerCamelCase:				Union[str, Any]     =	self.dummy_unet
        __lowerCamelCase:				Optional[Any]     =	self.dummy_movq

        __lowerCamelCase:				Tuple     =	DDIMScheduler(
            num_train_timesteps=1000    ,	beta_schedule="""linear"""    ,	beta_start=0.0_0085    ,	beta_end=0.012    ,	clip_sample=SCREAMING_SNAKE_CASE_    ,	set_alpha_to_one=SCREAMING_SNAKE_CASE_    ,	steps_offset=1    ,	prediction_type="""epsilon"""    ,	thresholding=SCREAMING_SNAKE_CASE_    ,	)

        __lowerCamelCase:				Optional[int]     =	{
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }

        return components





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=0							):
        __lowerCamelCase:				Any     =	floats_tensor((1, self.cross_attention_dim)    ,	rng=random.Random(SCREAMING_SNAKE_CASE_							)							).to(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	floats_tensor((1, self.cross_attention_dim)    ,	rng=random.Random(seed + 1							)							).to(SCREAMING_SNAKE_CASE_							)
        # create init_image
        __lowerCamelCase:				Any     =	floats_tensor((1, 3, 64, 64)    ,	rng=random.Random(SCREAMING_SNAKE_CASE_							)							).to(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	image.cpu().permute(0    ,	2    ,	3    ,	1							)[0]
        __lowerCamelCase:				Optional[int]     =	Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_							)							).convert("""RGB"""							).resize((256, 256)							)
        # create mask
        __lowerCamelCase:				Union[str, Any]     =	np.ones((64, 64)    ,	dtype=np.floataa							)
        __lowerCamelCase:				Tuple     =	0

        if str(SCREAMING_SNAKE_CASE_							).startswith("""mps"""							):
           __lowerCamelCase:				Tuple     =	torch.manual_seed(SCREAMING_SNAKE_CASE_							)
        else:
           __lowerCamelCase:				Optional[int]     =	torch.Generator(device=SCREAMING_SNAKE_CASE_							).manual_seed(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	{
            """prompt""": """horse""",
            """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  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				int     =	"""cpu"""

        __lowerCamelCase:				Dict     =	self.get_dummy_components()

        __lowerCamelCase:				List[Any]     =	self.pipeline_class(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	pipe.to(SCREAMING_SNAKE_CASE_							)

        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[int]     =	pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)							)
        __lowerCamelCase:				int     =	output.images

        __lowerCamelCase:				List[Any]     =	pipe(
            **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)    ,	return_dict=SCREAMING_SNAKE_CASE_    ,	)[0]

        __lowerCamelCase:				str     =	image[0, -3:, -3:, -1]
        __lowerCamelCase:				List[str]     =	image_from_tuple[0, -3:, -3:, -1]

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

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

        __lowerCamelCase:				Any     =	np.array(
            [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]							)

        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  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        super().test_inference_batch_single_identical(expected_max_diff=3E-3							)



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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				List[Any]     =	load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""							)

        __lowerCamelCase:				Optional[int]     =	load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""							)
        __lowerCamelCase:				Any     =	np.ones((768, 768)    ,	dtype=np.floataa							)
        __lowerCamelCase:				Union[str, Any]     =	0

        __lowerCamelCase:				List[Any]     =	"""a hat"""

        __lowerCamelCase:				Optional[Any]     =	KandinskyPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-prior"""    ,	torch_dtype=torch.floataa							)
        pipe_prior.to(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Any     =	KandinskyInpaintPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-inpaint"""    ,	torch_dtype=torch.floataa							)
        __lowerCamelCase:				int     =	pipeline.to(SCREAMING_SNAKE_CASE_							)
        pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[str]     =	torch.Generator(device="""cpu"""							).manual_seed(0							)
        __lowerCamelCase   ,     __lowerCamelCase:				List[Any]     =	pipe_prior(
            SCREAMING_SNAKE_CASE_    ,	generator=SCREAMING_SNAKE_CASE_    ,	num_inference_steps=5    ,	negative_prompt=""""""    ,	).to_tuple()

        __lowerCamelCase:				Union[str, Any]     =	pipeline(
            SCREAMING_SNAKE_CASE_    ,	image=SCREAMING_SNAKE_CASE_    ,	mask_image=SCREAMING_SNAKE_CASE_    ,	image_embeds=SCREAMING_SNAKE_CASE_    ,	negative_image_embeds=SCREAMING_SNAKE_CASE_    ,	generator=SCREAMING_SNAKE_CASE_    ,	num_inference_steps=100    ,	height=768    ,	width=768    ,	output_type="""np"""    ,	)

        __lowerCamelCase:				Optional[Any]     =	output.images[0]

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

        assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)






from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_pt_objects import *  # noqa F403
else:
   from .scheduling_consistency_models import CMStochasticIterativeScheduler
   from .scheduling_ddim import DDIMScheduler
   from .scheduling_ddim_inverse import DDIMInverseScheduler
   from .scheduling_ddim_parallel import DDIMParallelScheduler
   from .scheduling_ddpm import DDPMScheduler
   from .scheduling_ddpm_parallel import DDPMParallelScheduler
   from .scheduling_deis_multistep import DEISMultistepScheduler
   from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
   from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
   from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
   from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
   from .scheduling_euler_discrete import EulerDiscreteScheduler
   from .scheduling_heun_discrete import HeunDiscreteScheduler
   from .scheduling_ipndm import IPNDMScheduler
   from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
   from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
   from .scheduling_karras_ve import KarrasVeScheduler
   from .scheduling_pndm import PNDMScheduler
   from .scheduling_repaint import RePaintScheduler
   from .scheduling_sde_ve import ScoreSdeVeScheduler
   from .scheduling_sde_vp import ScoreSdeVpScheduler
   from .scheduling_unclip import UnCLIPScheduler
   from .scheduling_unipc_multistep import UniPCMultistepScheduler
   from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
   from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_flax_objects import *  # noqa F403
else:
   from .scheduling_ddim_flax import FlaxDDIMScheduler
   from .scheduling_ddpm_flax import FlaxDDPMScheduler
   from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
   from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
   from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
   from .scheduling_pndm_flax import FlaxPNDMScheduler
   from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
   from .scheduling_utils_flax import (
       FlaxKarrasDiffusionSchedulers,
       FlaxSchedulerMixin,
       FlaxSchedulerOutput,
       broadcast_to_shape_from_left,
   )


try:
   if not (is_torch_available() and is_scipy_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
   from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
   if not (is_torch_available() and is_torchsde_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
   from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler


