




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
class 				__UpperCamelCase      (			a__	):





				'''simple docstring'''



				__a				:			torch.FloatTensor
				__a				:			torch.FloatTensor




def    UpperCAmelCase							(  _snake_case    ,			_snake_case=0.999    ,			_snake_case="cosine"    ,			):

							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_snake_case				):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

							elif alpha_transform_type == "exp":

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

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

							lowerCAmelCase =		[]
							for i in range(_lowerCAmelCase				):
														lowerCAmelCase =		i / num_diffusion_timesteps
														lowerCAmelCase =		(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'''



				__a				:			List[str]					    =1



				@register_to_config
				def __init__(			self							,			UpperCAmelCase_ = 10_00							,			UpperCAmelCase_ = 0.0001							,			UpperCAmelCase_ = 0.02							,			UpperCAmelCase_ = "linear"							,			UpperCAmelCase_ = 0.0							,			UpperCAmelCase_ = None							,			UpperCAmelCase_ = True							,			):
											if trained_betas is not None:
																		lowerCAmelCase =		torch.from_numpy(__UpperCAmelCase    )
											elif beta_schedule == "linear":
																		lowerCAmelCase =		torch.linspace(__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			dtype=torch.floataa    )
											elif beta_schedule == "scaled_linear":
																		# this schedule is very specific to the latent diffusion model.
																		lowerCAmelCase =		(
																		    torch.linspace(beta_start**0.5							,			beta_end**0.5							,			__UpperCAmelCase							,			dtype=torch.floataa    ) ** 2
																		)
											elif beta_schedule == "squaredcos_cap_v2":
																		# Glide cosine schedule
																		lowerCAmelCase =		betas_for_alpha_bar(__UpperCAmelCase    )
											elif beta_schedule == "sigmoid":
																		# GeoDiff sigmoid schedule
																		lowerCAmelCase =		torch.linspace(-6							,			6							,			__UpperCAmelCase    )
																		lowerCAmelCase =		torch.sigmoid(__UpperCAmelCase    ) * (beta_end - beta_start) + beta_start
											else:
																		raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""    )

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

											lowerCAmelCase =		torch.tensor(1.0    )

											# standard deviation of the initial noise distribution
											lowerCAmelCase =		1.0

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

											lowerCAmelCase =		eta



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_ = None    ):
											return sample



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_ = 10							,			UpperCAmelCase_ = 10							,			UpperCAmelCase_ = None							,			):
											lowerCAmelCase =		min(self.config.num_train_timesteps							,			__UpperCAmelCase    )
											lowerCAmelCase =		num_inference_steps

											lowerCAmelCase =		[]

											lowerCAmelCase =		{}
											for j in range(0							,			num_inference_steps - jump_length							,			__UpperCAmelCase    ):
																		lowerCAmelCase =		jump_n_sample - 1

											lowerCAmelCase =		num_inference_steps
											while t >= 1:
																		lowerCAmelCase =		t - 1
																		timesteps.append(__UpperCAmelCase    )

																		if jumps.get(__UpperCAmelCase							,			0    ) > 0:
																									lowerCAmelCase =		jumps[t] - 1
																									for _ in range(__UpperCAmelCase    ):
																																lowerCAmelCase =		t + 1
																																timesteps.append(__UpperCAmelCase    )

											lowerCAmelCase =		np.array(__UpperCAmelCase    ) * (self.config.num_train_timesteps // self.num_inference_steps)
											lowerCAmelCase =		torch.from_numpy(__UpperCAmelCase    ).to(__UpperCAmelCase    )



				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											lowerCAmelCase =		t - self.config.num_train_timesteps // self.num_inference_steps

											lowerCAmelCase =		self.alphas_cumprod[t]
											lowerCAmelCase =		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
											lowerCAmelCase =		1 - alpha_prod_t
											lowerCAmelCase =		1 - 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
											# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
											# without eta.
											# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
											lowerCAmelCase =		(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

											return variance



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_ = None							,			UpperCAmelCase_ = True							,			):
											lowerCAmelCase =		timestep
											lowerCAmelCase =		timestep - self.config.num_train_timesteps // self.num_inference_steps

											# 1. compute alphas, betas
											lowerCAmelCase =		self.alphas_cumprod[t]
											lowerCAmelCase =		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
											lowerCAmelCase =		1 - alpha_prod_t

											# 2. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
											lowerCAmelCase =		(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
																		lowerCAmelCase =		torch.clamp(__UpperCAmelCase							,			-1							,			1    )

											# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
											# substitute formula (7) in the algorithm coming from DDPM paper
											# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
											# DDIM schedule gives the same results as DDPM with eta = 1.0
											# Noise is being reused in 7. and 8., but no impact on quality has
											# been observed.

											# 5. Add noise
											lowerCAmelCase =		model_output.device
											lowerCAmelCase =		randn_tensor(model_output.shape							,			generator=__UpperCAmelCase							,			device=__UpperCAmelCase							,			dtype=model_output.dtype    )
											lowerCAmelCase =		self.eta * self._get_variance(__UpperCAmelCase    ) ** 0.5

											lowerCAmelCase =		0
											if t > 0 and self.eta > 0:
																		lowerCAmelCase =		std_dev_t * noise

											# 6. compute "direction pointing to x_t" of formula (12)
											# from https://arxiv.org/pdf/2010.02502.pdf
											lowerCAmelCase =		(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

											# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											lowerCAmelCase =		alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

											# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
											lowerCAmelCase =		(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

											# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
											lowerCAmelCase =		mask * prev_known_part + (1.0 - mask) * prev_unknown_part

											if not return_dict:
																		return (
																		    pred_prev_sample,
																		    pred_original_sample,
																		)

											return RePaintSchedulerOutput(prev_sample=__UpperCAmelCase							,			pred_original_sample=__UpperCAmelCase    )



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_=None    ):
											lowerCAmelCase =		self.config.num_train_timesteps // self.num_inference_steps

											for i in range(__UpperCAmelCase    ):
																		lowerCAmelCase =		self.betas[timestep + i]
																		if sample.device.type == "mps":
																									# randn does not work reproducibly on mps
																									lowerCAmelCase =		randn_tensor(sample.shape							,			dtype=sample.dtype							,			generator=__UpperCAmelCase    )
																									lowerCAmelCase =		noise.to(sample.device    )
																		else:
																									lowerCAmelCase =		randn_tensor(sample.shape							,			generator=__UpperCAmelCase							,			device=sample.device							,			dtype=sample.dtype    )

																		# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
																		lowerCAmelCase =		(1 - beta) ** 0.5 * sample + beta**0.5 * noise

											return sample



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			):
											raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''    )






				def __len__(			self    ):
											return self.config.num_train_timesteps







"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__snake_case												=							False


@nightly
@require_torch_gpu
class      __lowerCamelCase	(       unittest.TestCase					):

     '''simple docstring'''


     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCAmelCase				)
          pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

          _a     						=					load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)

          _a     						=					torch.manual_seed(0				)
          _a     						=					pipe(
              prompt='''first prompt'''     ,					image=__UpperCAmelCase     ,					text_to_image_strength=0.75     ,					generator=__UpperCAmelCase     ,					guidance_scale=7.5     ,					num_inference_steps=2     ,					output_type='''numpy'''     ,					).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCAmelCase				)
               _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCAmelCase				)

          pipe.to(__UpperCAmelCase				)
          pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

          _a     						=					generator.manual_seed(0				)
          _a     						=					pipe(
              prompt='''first prompt'''     ,					image=__UpperCAmelCase     ,					text_to_image_strength=0.75     ,					generator=__UpperCAmelCase     ,					guidance_scale=7.5     ,					num_inference_steps=2     ,					output_type='''numpy'''     ,					).images

          assert np.abs(image - new_image				).sum() < 1e-5, "Models don't have the same forward pass"





     def 				_UpperCAmelCase   (   self				)							->				Dict:
          _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
          pipe.remove_unused_weights()
          pipe.to(__UpperCAmelCase				)
          pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

          _a     						=					'''cyberpunk 2077'''
          _a     						=					load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
          _a     						=					torch.manual_seed(0				)
          _a     						=					pipe(
              prompt=__UpperCAmelCase     ,					image=__UpperCAmelCase     ,					text_to_image_strength=0.75     ,					generator=__UpperCAmelCase     ,					guidance_scale=7.5     ,					num_inference_steps=50     ,					output_type='''numpy'''     ,					).images

          _a     						=					image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a     						=					np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]				)

          assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2