



'''simple docstring'''



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 _UpperCamelCase		(     A      ,       unittest.TestCase		):





						'''simple docstring'''





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

						@property
						def 				__lowerCamelCase     (							self  :  List[str]):







													'''simple docstring'''

													return 3_2

						@property
						def 				__lowerCamelCase     (							self  :  List[str]):







													'''simple docstring'''

													return 3_2

						@property
						def 				__lowerCamelCase     (							self  :  Any):







													'''simple docstring'''

													return self.time_input_dim

						@property
						def 				__lowerCamelCase     (							self  :  List[str]):







													'''simple docstring'''

													return self.time_input_dim * 4

						@property
						def 				__lowerCamelCase     (							self  :  Any):







													'''simple docstring'''

													return 1_0_0

						@property
						def 				__lowerCamelCase     (							self  :  Tuple):







													'''simple docstring'''

													__lowercase       	=XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
													return tokenizer

						@property
						def 				__lowerCamelCase     (							self  :  str):







													'''simple docstring'''

													torch.manual_seed(0)
													__lowercase       	=MCLIPConfig(
													    numDims=self.cross_attention_dim  ,       transformerDimensions=self.text_embedder_hidden_size  ,       hidden_size=self.text_embedder_hidden_size  ,       intermediate_size=3_7  ,       num_attention_heads=4  ,       num_hidden_layers=5  ,       vocab_size=1_0_0_5  ,       )

													__lowercase       	=MultilingualCLIP(_lowerCAmelCase)
													__lowercase       	=text_encoder.eval()

													return text_encoder

						@property
						def 				__lowerCamelCase     (							self  :  Dict):







													'''simple docstring'''

													torch.manual_seed(0)

													__lowercase       	={
													    '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,
													}

													__lowercase       	=UNetaDConditionModel(**_lowerCAmelCase)
													return model

						@property
						def 				__lowerCamelCase     (							self  :  Dict):







													'''simple docstring'''

													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 				__lowerCamelCase     (							self  :  Any):







													'''simple docstring'''

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

						def 				__lowerCamelCase     (							self  :  Union[str, Any]):







													'''simple docstring'''

													__lowercase       	=self.dummy_text_encoder
													__lowercase       	=self.dummy_tokenizer
													__lowercase       	=self.dummy_unet
													__lowercase       	=self.dummy_movq

													__lowercase       	=DDIMScheduler(
													    num_train_timesteps=1_0_0_0  ,       beta_schedule='linear'  ,       beta_start=0.0_0085  ,       beta_end=0.012  ,       clip_sample=_lowerCAmelCase  ,       set_alpha_to_one=_lowerCAmelCase  ,       steps_offset=1  ,       prediction_type='epsilon'  ,       thresholding=_lowerCAmelCase  ,       )

													__lowercase       	={
													    'text_encoder': text_encoder,
													    'tokenizer': tokenizer,
													    'unet': unet,
													    'scheduler': scheduler,
													    'movq': movq,
													}

													return components

						def 				__lowerCamelCase     (							self  :  str  ,       _lowerCAmelCase  :  Union[str, Any]  ,       _lowerCAmelCase  :  List[str]=0):







													'''simple docstring'''

													__lowercase       	=floats_tensor((1, self.cross_attention_dim)  ,       rng=random.Random(_lowerCAmelCase)).to(_lowerCAmelCase)
													__lowercase       	=floats_tensor((1, self.cross_attention_dim)  ,       rng=random.Random(seed + 1)).to(_lowerCAmelCase)
													# create init_image
													__lowercase       	=floats_tensor((1, 3, 6_4, 6_4)  ,       rng=random.Random(_lowerCAmelCase)).to(_lowerCAmelCase)
													__lowercase       	=image.cpu().permute(0  ,       2  ,       3  ,       1)[0]
													__lowercase       	=Image.fromarray(np.uinta(_lowerCAmelCase)).convert('RGB').resize((2_5_6, 2_5_6))
													# create mask
													__lowercase       	=np.ones((6_4, 6_4)  ,       dtype=np.floataa)
													__lowercase       	=0

													if str(_lowerCAmelCase).startswith('mps'):
																				__lowercase       	=torch.manual_seed(_lowerCAmelCase)
													else:
																				__lowercase       	=torch.Generator(device=_lowerCAmelCase).manual_seed(_lowerCAmelCase)
													__lowercase       	={
													    'prompt': 'horse',
													    '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 				__lowerCamelCase     (							self  :  Optional[int]):







													'''simple docstring'''

													__lowercase       	='cpu'

													__lowercase       	=self.get_dummy_components()

													__lowercase       	=self.pipeline_class(**_lowerCAmelCase)
													__lowercase       	=pipe.to(_lowerCAmelCase)

													pipe.set_progress_bar_config(disable=_lowerCAmelCase)

													__lowercase       	=pipe(**self.get_dummy_inputs(_lowerCAmelCase))
													__lowercase       	=output.images

													__lowercase       	=pipe(
													    **self.get_dummy_inputs(_lowerCAmelCase)  ,       return_dict=_lowerCAmelCase  ,       )[0]

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

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

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

													__lowercase       	=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 				__lowerCamelCase     (							self  :  Dict):







													'''simple docstring'''

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





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





						'''simple docstring'''





						def 				__lowerCamelCase     (							self  :  Optional[int]):







													'''simple docstring'''

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

						def 				__lowerCamelCase     (							self  :  Optional[Any]):







													'''simple docstring'''

													__lowercase       	=load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')

													__lowercase       	=load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
													__lowercase       	=np.ones((7_6_8, 7_6_8)  ,       dtype=np.floataa)
													__lowercase       	=0

													__lowercase       	='a hat'

													__lowercase       	=KandinskyPriorPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-1-prior'  ,       torch_dtype=torch.floataa)
													pipe_prior.to(_lowerCAmelCase)

													__lowercase       	=KandinskyInpaintPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-1-inpaint'  ,       torch_dtype=torch.floataa)
													__lowercase       	=pipeline.to(_lowerCAmelCase)
													pipeline.set_progress_bar_config(disable=_lowerCAmelCase)

													__lowercase       	=torch.Generator(device='cpu').manual_seed(0)
													__lowercase	,				__lowercase       	=pipe_prior(
													    _lowerCAmelCase  ,       generator=_lowerCAmelCase  ,       num_inference_steps=5  ,       negative_prompt=''  ,       ).to_tuple()

													__lowercase       	=pipeline(
													    _lowerCAmelCase  ,       image=_lowerCAmelCase  ,       mask_image=_lowerCAmelCase  ,       image_embeds=_lowerCAmelCase  ,       negative_image_embeds=_lowerCAmelCase  ,       generator=_lowerCAmelCase  ,       num_inference_steps=1_0_0  ,       height=7_6_8  ,       width=7_6_8  ,       output_type='np'  ,       )

													__lowercase       	=output.images[0]

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

													assert_mean_pixel_difference(_lowerCAmelCase  ,       _lowerCAmelCase)








'''simple docstring'''



import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    None



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase=0.9_99  ,   _lowerCAmelCase="cosine"  ,   ):



							"""simple docstring"""



							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_lowerCAmelCase					):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(_lowerCAmelCase					):
																					return math.exp(t * -12.0					)

							else:
														raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""					)

							__lowercase       	=[]
							for i in range(_lowerCAmelCase					):
														__lowercase       	=i / num_diffusion_timesteps
														__lowercase       	=(i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(_lowerCAmelCase					) / alpha_bar_fn(_lowerCAmelCase					)  ,   _lowerCAmelCase					)					)
							return torch.tensor(_lowerCAmelCase  ,   dtype=torch.floataa					)





class _UpperCamelCase		(     A      ,       A		):





				'''simple docstring'''





				@register_to_config
				def __init__(							self  :  Any  ,       _lowerCAmelCase  :  int = 1_0_0_0  ,       _lowerCAmelCase  :  str = "fixed_small_log"  ,       _lowerCAmelCase  :  bool = True  ,       _lowerCAmelCase  :  Optional[float] = 1.0  ,       _lowerCAmelCase  :  str = "epsilon"  ,       _lowerCAmelCase  :  str = "squaredcos_cap_v2"  ,       ):







											'''simple docstring'''

											if beta_schedule != "squaredcos_cap_v2":
																		raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'')

											__lowercase       	=betas_for_alpha_bar(_lowerCAmelCase)

											__lowercase       	=1.0 - self.betas
											__lowercase       	=torch.cumprod(self.alphas  ,       dim=0)
											__lowercase       	=torch.tensor(1.0)

											# standard deviation of the initial noise distribution
											__lowercase       	=1.0

											# setable values
											__lowercase       	=None
											__lowercase       	=torch.from_numpy(np.arange(0  ,       _lowerCAmelCase)[::-1].copy())

											__lowercase       	=variance_type

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  Optional[int] = None):







											'''simple docstring'''

											return sample

				def 				__lowerCamelCase     (							self  :  Tuple  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  Union[str, torch.device] = None):







											'''simple docstring'''

											__lowercase       	=num_inference_steps
											__lowercase       	=(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
											__lowercase       	=(np.arange(0  ,       _lowerCAmelCase) * step_ratio).round()[::-1].copy().astype(np.intaa)
											__lowercase       	=torch.from_numpy(_lowerCAmelCase).to(_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  str  ,       _lowerCAmelCase  :  List[str]  ,       _lowerCAmelCase  :  Dict=None  ,       _lowerCAmelCase  :  Dict=None  ,       _lowerCAmelCase  :  int=None):







											'''simple docstring'''

											if prev_timestep is None:
																		__lowercase       	=t - 1

											__lowercase       	=self.alphas_cumprod[t]
											__lowercase       	=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
											__lowercase       	=1 - alpha_prod_t
											__lowercase       	=1 - alpha_prod_t_prev

											if prev_timestep == t - 1:
																		__lowercase       	=self.betas[t]
											else:
																		__lowercase       	=1 - alpha_prod_t / alpha_prod_t_prev

											# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
											# and sample from it to get previous sample
											# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
											__lowercase       	=beta_prod_t_prev / beta_prod_t * beta

											if variance_type is None:
																		__lowercase       	=self.config.variance_type

											# hacks - were probably added for training stability
											if variance_type == "fixed_small_log":
																		__lowercase       	=torch.log(torch.clamp(_lowerCAmelCase  ,       min=1e-20))
																		__lowercase       	=torch.exp(0.5 * variance)
											elif variance_type == "learned_range":
																		# NOTE difference with DDPM scheduler
																		__lowercase       	=variance.log()
																		__lowercase       	=beta.log()

																		__lowercase       	=(predicted_variance + 1) / 2
																		__lowercase       	=frac * max_log + (1 - frac) * min_log

											return variance

				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  Optional[int] = None  ,       _lowerCAmelCase  :  Optional[int]=None  ,       _lowerCAmelCase  :  bool = True  ,       ):







											'''simple docstring'''

											__lowercase       	=timestep

											if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
																		__lowercase	,				__lowercase       	=torch.split(_lowerCAmelCase  ,       sample.shape[1]  ,       dim=1)
											else:
																		__lowercase       	=None

											# 1. compute alphas, betas
											if prev_timestep is None:
																		__lowercase       	=t - 1

											__lowercase       	=self.alphas_cumprod[t]
											__lowercase       	=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
											__lowercase       	=1 - alpha_prod_t
											__lowercase       	=1 - alpha_prod_t_prev

											if prev_timestep == t - 1:
																		__lowercase       	=self.betas[t]
																		__lowercase       	=self.alphas[t]
											else:
																		__lowercase       	=1 - alpha_prod_t / alpha_prod_t_prev
																		__lowercase       	=1 - beta

											# 2. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
											if self.config.prediction_type == "epsilon":
																		__lowercase       	=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											elif self.config.prediction_type == "sample":
																		__lowercase       	=model_output
											else:
																		raise ValueError(
																		    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
																		    ' for the UnCLIPScheduler.')

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
																		__lowercase       	=torch.clamp(
																		    _lowerCAmelCase  ,       -self.config.clip_sample_range  ,       self.config.clip_sample_range)

											# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											__lowercase       	=(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
											__lowercase       	=alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

											# 5. Compute predicted previous sample µ_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											__lowercase       	=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

											# 6. Add noise
											__lowercase       	=0
											if t > 0:
																		__lowercase       	=randn_tensor(
																		    model_output.shape  ,       dtype=model_output.dtype  ,       generator=_lowerCAmelCase  ,       device=model_output.device)

																		__lowercase       	=self._get_variance(
																		    _lowerCAmelCase  ,       predicted_variance=_lowerCAmelCase  ,       prev_timestep=_lowerCAmelCase  ,       )

																		if self.variance_type == "fixed_small_log":
																									__lowercase       	=variance
																		elif self.variance_type == "learned_range":
																									__lowercase       	=(0.5 * variance).exp()
																		else:
																									raise ValueError(
																									    f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
																									    ' for the UnCLIPScheduler.')

																		__lowercase       	=variance * variance_noise

											__lowercase       	=pred_prev_sample + variance

											if not return_dict:
																		return (pred_prev_sample,)

											return UnCLIPSchedulerOutput(prev_sample=_lowerCAmelCase  ,       pred_original_sample=_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  torch.IntTensor  ,       ):







											'''simple docstring'''

											__lowercase       	=self.alphas_cumprod.to(device=original_samples.device  ,       dtype=original_samples.dtype)
											__lowercase       	=timesteps.to(original_samples.device)

											__lowercase       	=alphas_cumprod[timesteps] ** 0.5
											__lowercase       	=sqrt_alpha_prod.flatten()
											while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
																		__lowercase       	=sqrt_alpha_prod.unsqueeze(-1)

											__lowercase       	=(1 - alphas_cumprod[timesteps]) ** 0.5
											__lowercase       	=sqrt_one_minus_alpha_prod.flatten()
											while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
																		__lowercase       	=sqrt_one_minus_alpha_prod.unsqueeze(-1)

											__lowercase       	=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
											return noisy_samples



