


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 			SCREAMING_SNAKE_CASE__  (    _lowerCAmelCase						,   unittest.TestCase      ):
      snake_case__    :					Tuple			       =						KandinskyVaaInpaintPipeline
      snake_case__    :					Tuple			       =						["image_embeds", "negative_image_embeds", "image", "mask_image"]
      snake_case__    :					Optional[Any]			       =						[
          "image_embeds",
          "negative_image_embeds",
          "image",
          "mask_image",
      ]
      snake_case__    :					List[str]			       =						[
          "generator",
          "height",
          "width",
          "latents",
          "guidance_scale",
          "num_inference_steps",
          "return_dict",
          "guidance_scale",
          "num_images_per_prompt",
          "output_type",
          "return_dict",
      ]
      snake_case__    :					Optional[int]			       =						False
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      Tuple:
             return 3_2
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Any:
             return 3_2
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      int:
             return self.time_input_dim
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      str:
             return self.time_input_dim * 4
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      str:
             return 1_0_0
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      List[str]:
             torch.manual_seed(0					)

             a_ :		Dict							=  {
                 '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,
             }

             a_ :		Union[str, Any]							=  UNetaDConditionModel(**_lowercase					)
             return model
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Any:
             return {
                 "block_out_channels": [3_2, 6_4],
                 "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": 1_2,
                 "out_channels": 3,
                 "up_block_types": [
                     "AttnUpDecoderBlock2D",
                     "UpDecoderBlock2D",
                 ],
                 "vq_embed_dim": 4,
             }
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      int:
             torch.manual_seed(0					)
             a_ :		Tuple							=  VQModel(**self.dummy_movq_kwargs					)
             return model
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      int:
             a_ :		Optional[Any]							=  self.dummy_unet
             a_ :		Optional[Any]							=  self.dummy_movq

             a_ :		List[str]							=  DDIMScheduler(
                 num_train_timesteps=1_0_0_0					,							beta_schedule='linear'					,							beta_start=0.00085					,							beta_end=0.012					,							clip_sample=_lowercase					,							set_alpha_to_one=_lowercase					,							steps_offset=1					,							prediction_type='epsilon'					,							thresholding=_lowercase					,							)

             a_ :		int							=  {
                 'unet': unet,
                 'scheduler': scheduler,
                 'movq': movq,
             }

             return components
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=0					)		->      Any:
             a_ :		Union[str, Any]							=  floats_tensor((1, self.text_embedder_hidden_size)					,							rng=random.Random(_lowercase					)					).to(_lowercase					)
             a_ :		Union[str, Any]							=  floats_tensor((1, self.text_embedder_hidden_size)					,							rng=random.Random(seed + 1					)					).to(
                 _lowercase					)
             # create init_image
             a_ :		List[Any]							=  floats_tensor((1, 3, 6_4, 6_4)					,							rng=random.Random(_lowercase					)					).to(_lowercase					)
             a_ :		Optional[Any]							=  image.cpu().permute(0					,							2					,							3					,							1					)[0]
             a_ :		Optional[Any]							=  Image.fromarray(np.uinta(_lowercase					)					).convert('RGB'					).resize((2_5_6, 2_5_6)					)
             # create mask
             a_ :		List[Any]							=  np.ones((6_4, 6_4)					,							dtype=np.floataa					)
             a_ :		Optional[Any]							=  0

             if str(_lowercase					).startswith('mps'					):
                    a_ :		List[Any]							=  torch.manual_seed(_lowercase					)
             else:
                    a_ :		Dict							=  torch.Generator(device=_lowercase					).manual_seed(_lowercase					)
             a_ :		List[str]							=  {
                 'image': init_image,
                 'mask_image': mask,
                 'image_embeds': image_embeds,
                 'negative_image_embeds': negative_image_embeds,
                 'generator': generator,
                 'height': 6_4,
                 'width': 6_4,
                 'num_inference_steps': 2,
                 'guidance_scale': 4.0,
                 'output_type': 'np',
             }
             return inputs
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Union[str, Any]:
             a_ :		Optional[int]							=  'cpu'

             a_ :		Dict							=  self.get_dummy_components()

             a_ :		List[Any]							=  self.pipeline_class(**_lowercase					)
             a_ :		int							=  pipe.to(_lowercase					)

             pipe.set_progress_bar_config(disable=_lowercase					)

             a_ :		List[str]							=  pipe(**self.get_dummy_inputs(_lowercase					)					)
             a_ :		Optional[Any]							=  output.images

             a_ :		Optional[int]							=  pipe(
                 **self.get_dummy_inputs(_lowercase					)					,							return_dict=_lowercase					,							)[0]

             a_ :		Dict							=  image[0, -3:, -3:, -1]
             a_ :		Optional[Any]							=  image_from_tuple[0, -3:, -3:, -1]

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

             assert image.shape == (1, 6_4, 6_4, 3)

             a_ :		Optional[int]							=  np.array(
                 [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]					)

             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    :	Union[str, Any]					)		->      Tuple:
             super().test_inference_batch_single_identical(expected_max_diff=3E-3					)
@slow
@require_torch_gpu
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Optional[Any]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()

      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Union[str, Any]:
             a_ :		List[str]							=  load_numpy(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'					)

             a_ :		str							=  load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'					)
             a_ :		Tuple							=  np.ones((7_6_8, 7_6_8)					,							dtype=np.floataa					)
             a_ :		Union[str, Any]							=  0

             a_ :		Any							=  'a hat'

             a_ :		Optional[Any]							=  KandinskyVaaPriorPipeline.from_pretrained(
                 'kandinsky-community/kandinsky-2-2-prior'					,							torch_dtype=torch.floataa					)
             pipe_prior.to(_lowercase					)

             a_ :		Optional[int]							=  KandinskyVaaInpaintPipeline.from_pretrained(
                 'kandinsky-community/kandinsky-2-2-decoder-inpaint'					,							torch_dtype=torch.floataa					)
             a_ :		List[Any]							=  pipeline.to(_lowercase					)
             pipeline.set_progress_bar_config(disable=_lowercase					)

             a_ :		Dict							=  torch.Generator(device='cpu'					).manual_seed(0					)
             a_							,		a_ :		List[Any]							=  pipe_prior(
                 _lowercase					,							generator=_lowercase					,							num_inference_steps=5					,							negative_prompt=''					,							).to_tuple()

             a_ :		Union[str, Any]							=  pipeline(
                 image=_lowercase					,							mask_image=_lowercase					,							image_embeds=_lowercase					,							negative_image_embeds=_lowercase					,							generator=_lowercase					,							num_inference_steps=1_0_0					,							height=7_6_8					,							width=7_6_8					,							output_type='np'					,							)

             a_ :		Tuple							=  output.images[0]

             assert image.shape == (7_6_8, 7_6_8, 3)

             assert_mean_pixel_difference(_lowercase					,							_lowercase					)






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   lowercase__      ):
      snake_case__    :					Dict					       =						1
      @register_to_config
      def __init__(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Tuple=2_0_0_0					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=0.1					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=2_0					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=1E-3					)		->      Optional[int]:
             a_ :		Tuple							=  None
             a_ :		int							=  None
             a_ :		Tuple							=  None
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Union[str, torch.device] = None					)		->      List[str]:
             a_ :		Tuple							=  torch.linspace(1					,							self.config.sampling_eps					,							SCREAMING_SNAKE_CASE__					,							device=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Tuple=None					)		->      Tuple:
             if self.timesteps is None:
                    raise ValueError(
                        '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'					)

             # TODO(Patrick) better comments + non-PyTorch
             # postprocess model score
             a_ :		Tuple							=  (
                 -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
             )
             a_ :		int							=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff					)					)
             a_ :		Dict							=  std.flatten()
             while len(std.shape					) < len(score.shape					):
                    a_ :		str							=  std.unsqueeze(-1					)
             a_ :		List[str]							=  -score / std

             # compute
             a_ :		List[str]							=  -1.0 / len(self.timesteps					)

             a_ :		Optional[int]							=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
             a_ :		Optional[Any]							=  beta_t.flatten()
             while len(beta_t.shape					) < len(x.shape					):
                    a_ :		List[str]							=  beta_t.unsqueeze(-1					)
             a_ :		Optional[Any]							=  -0.5 * beta_t * x

             a_ :		Tuple							=  torch.sqrt(SCREAMING_SNAKE_CASE__					)
             a_ :		List[Any]							=  drift - diffusion**2 * score
             a_ :		List[str]							=  x + drift * dt

             # add noise
             a_ :		Optional[Any]							=  randn_tensor(x.shape					,							layout=x.layout					,							generator=SCREAMING_SNAKE_CASE__					,							device=x.device					,							dtype=x.dtype					)
             a_ :		Optional[Any]							=  x_mean + diffusion * math.sqrt(-dt					) * noise

             return x, x_mean

      def __len__(	self    :	int					)		->      Tuple:
             return self.config.num_train_timesteps


