





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

import numpy as np
import torch

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


_UpperCAmelCase										=					logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 				_UpperCamelCase       (						lowerCAmelCase_  ):
       _UpperCamelCase					:       torch.FloatTensor
       _UpperCamelCase					:       Optional[torch.FloatTensor]       = None




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_=0.9_99       ,		UpperCamelCase_="cosine"       ,		)  ->		int:
       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

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

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

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







class 				_UpperCamelCase       (						lowerCAmelCase_	,    lowerCAmelCase_  ):
       _UpperCamelCase					:       str       = [e.name for e in KarrasDiffusionSchedulers]
       _UpperCamelCase					:       Tuple       = 1

       @register_to_config
       def __init__(		self:			str       , _SCREAMING_SNAKE_CASE:			int = 1000       , _SCREAMING_SNAKE_CASE:			float = 0.00_01       , _SCREAMING_SNAKE_CASE:			float = 0.02       , _SCREAMING_SNAKE_CASE:			str = "linear"       , _SCREAMING_SNAKE_CASE:			Optional[Union[np.ndarray, List[float]]] = None       , _SCREAMING_SNAKE_CASE:			str = "epsilon"       , _SCREAMING_SNAKE_CASE:			str = "linear"       , _SCREAMING_SNAKE_CASE:			Optional[bool] = False       , _SCREAMING_SNAKE_CASE:			str = "linspace"       , _SCREAMING_SNAKE_CASE:			int = 0       , )							->			Dict:




              """simple docstring"""


              if trained_betas is not None:
                     UpperCamelCase_ 							=	torch.tensor(_SCREAMING_SNAKE_CASE       , dtype=torch.floataa		)
              elif beta_schedule == "linear":
                     UpperCamelCase_ 							=	torch.linspace(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , dtype=torch.floataa		)
              elif beta_schedule == "scaled_linear":
                     # this schedule is very specific to the latent diffusion model.
                     UpperCamelCase_ 							=	(
                         torch.linspace(beta_start**0.5       , beta_end**0.5       , _SCREAMING_SNAKE_CASE       , dtype=torch.floataa		) ** 2
                     )
              elif beta_schedule == "squaredcos_cap_v2":
                     # Glide cosine schedule
                     UpperCamelCase_ 							=	betas_for_alpha_bar(_SCREAMING_SNAKE_CASE		)
              else:
                     raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''		)

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

              UpperCamelCase_ 							=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
              UpperCamelCase_ 							=	np.concatenate([sigmas[::-1], [0.0]]		).astype(np.floataa		)
              UpperCamelCase_ 							=	torch.from_numpy(_SCREAMING_SNAKE_CASE		)

              # setable values
              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	np.linspace(0       , num_train_timesteps - 1       , _SCREAMING_SNAKE_CASE       , dtype=_SCREAMING_SNAKE_CASE		)[::-1].copy()
              UpperCamelCase_ 							=	torch.from_numpy(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	False
              UpperCamelCase_ 							=	use_karras_sigmas

       @property
       def lowercase			(		self:			Optional[Any]		)							->			List[Any]:




              """simple docstring"""


              if self.config.timestep_spacing in ["linspace", "trailing"]:
                     return self.sigmas.max()

              return (self.sigmas.max() ** 2 + 1) ** 0.5

       def lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			torch.FloatTensor       , _SCREAMING_SNAKE_CASE:			Union[float, torch.FloatTensor]		)							->			torch.FloatTensor:




              """simple docstring"""


              if isinstance(_SCREAMING_SNAKE_CASE       , torch.Tensor		):
                     UpperCamelCase_ 							=	timestep.to(self.timesteps.device		)
              UpperCamelCase_ 							=	(self.timesteps == timestep).nonzero().item()
              UpperCamelCase_ 							=	self.sigmas[step_index]

              UpperCamelCase_ 							=	sample / ((sigma**2 + 1) ** 0.5)

              UpperCamelCase_ 							=	True
              return sample

       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			Union[str, torch.device] = None		)							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	num_inference_steps

              # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
              if self.config.timestep_spacing == "linspace":
                     UpperCamelCase_ 							=	np.linspace(0       , self.config.num_train_timesteps - 1       , _SCREAMING_SNAKE_CASE       , dtype=_SCREAMING_SNAKE_CASE		)[
                         ::-1
                     ].copy()
              elif self.config.timestep_spacing == "leading":
                     UpperCamelCase_ 							=	self.config.num_train_timesteps // self.num_inference_steps
                     # creates integer timesteps by multiplying by ratio
                     # casting to int to avoid issues when num_inference_step is power of 3
                     UpperCamelCase_ 							=	(np.arange(0       , _SCREAMING_SNAKE_CASE		) * step_ratio).round()[::-1].copy().astype(_SCREAMING_SNAKE_CASE		)
                     timesteps += self.config.steps_offset
              elif self.config.timestep_spacing == "trailing":
                     UpperCamelCase_ 							=	self.config.num_train_timesteps / self.num_inference_steps
                     # creates integer timesteps by multiplying by ratio
                     # casting to int to avoid issues when num_inference_step is power of 3
                     UpperCamelCase_ 							=	(np.arange(self.config.num_train_timesteps       , 0       , -step_ratio		)).round().copy().astype(_SCREAMING_SNAKE_CASE		)
                     timesteps -= 1
              else:
                     raise ValueError(
                         f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''		)

              UpperCamelCase_ 							=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
              UpperCamelCase_ 							=	np.log(_SCREAMING_SNAKE_CASE		)

              if self.config.interpolation_type == "linear":
                     UpperCamelCase_ 							=	np.interp(_SCREAMING_SNAKE_CASE       , np.arange(0       , len(_SCREAMING_SNAKE_CASE		)		)       , _SCREAMING_SNAKE_CASE		)
              elif self.config.interpolation_type == "log_linear":
                     UpperCamelCase_ 							=	torch.linspace(np.log(sigmas[-1]		)       , np.log(sigmas[0]		)       , num_inference_steps + 1		).exp()
              else:
                     raise ValueError(
                         f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
                         " 'linear' or 'log_linear'"		)

              if self.use_karras_sigmas:
                     UpperCamelCase_ 							=	self._convert_to_karras(in_sigmas=_SCREAMING_SNAKE_CASE       , num_inference_steps=self.num_inference_steps		)
                     UpperCamelCase_ 							=	np.array([self._sigma_to_t(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		) for sigma in sigmas]		)

              UpperCamelCase_ 							=	np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
              UpperCamelCase_ 							=	torch.from_numpy(_SCREAMING_SNAKE_CASE		).to(device=_SCREAMING_SNAKE_CASE		)
              if str(_SCREAMING_SNAKE_CASE		).startswith("mps"		):
                     # mps does not support float64
                     UpperCamelCase_ 							=	torch.from_numpy(_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE       , dtype=torch.floataa		)
              else:
                     UpperCamelCase_ 							=	torch.from_numpy(_SCREAMING_SNAKE_CASE		).to(device=_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			Optional[int]		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	np.log(_SCREAMING_SNAKE_CASE		)

              # get distribution
              UpperCamelCase_ 							=	log_sigma - log_sigmas[:, np.newaxis]

              # get sigmas range
              UpperCamelCase_ 							=	np.cumsum((dists >= 0)       , axis=0		).argmax(axis=0		).clip(max=log_sigmas.shape[0] - 2		)
              UpperCamelCase_ 							=	low_idx + 1

              UpperCamelCase_ 							=	log_sigmas[low_idx]
              UpperCamelCase_ 							=	log_sigmas[high_idx]

              # interpolate sigmas
              UpperCamelCase_ 							=	(low - log_sigma) / (low - high)
              UpperCamelCase_ 							=	np.clip(_SCREAMING_SNAKE_CASE       , 0       , 1		)

              # transform interpolation to time range
              UpperCamelCase_ 							=	(1 - w) * low_idx + w * high_idx
              UpperCamelCase_ 							=	t.reshape(sigma.shape		)
              return t

       def lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			torch.FloatTensor       , _SCREAMING_SNAKE_CASE:			List[Any]		)							->			torch.FloatTensor:




              """simple docstring"""


              UpperCamelCase_ 							=	in_sigmas[-1].item()
              UpperCamelCase_ 							=	in_sigmas[0].item()

              UpperCamelCase_ 							=	7.0  # 7.0 is the value used in the paper
              UpperCamelCase_ 							=	np.linspace(0       , 1       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	sigma_min ** (1 / rho)
              UpperCamelCase_ 							=	sigma_max ** (1 / rho)
              UpperCamelCase_ 							=	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
              return sigmas

       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			torch.FloatTensor       , _SCREAMING_SNAKE_CASE:			Union[float, torch.FloatTensor]       , _SCREAMING_SNAKE_CASE:			torch.FloatTensor       , _SCREAMING_SNAKE_CASE:			float = 0.0       , _SCREAMING_SNAKE_CASE:			float = 0.0       , _SCREAMING_SNAKE_CASE:			float = float("inf"		)       , _SCREAMING_SNAKE_CASE:			float = 1.0       , _SCREAMING_SNAKE_CASE:			Optional[torch.Generator] = None       , _SCREAMING_SNAKE_CASE:			bool = True       , )							->			Union[EulerDiscreteSchedulerOutput, Tuple]:




              """simple docstring"""


              if (
                  isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
                  or isinstance(_SCREAMING_SNAKE_CASE       , torch.IntTensor		)
                  or isinstance(_SCREAMING_SNAKE_CASE       , torch.LongTensor		)
              ):
                     raise ValueError(
                         (
                             "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                             " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                             " one of the `scheduler.timesteps` as a timestep."
                         )       , )

              if not self.is_scale_input_called:
                     logger.warning(
                         "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
                         "See `StableDiffusionPipeline` for a usage example."		)

              if isinstance(_SCREAMING_SNAKE_CASE       , torch.Tensor		):
                     UpperCamelCase_ 							=	timestep.to(self.timesteps.device		)

              UpperCamelCase_ 							=	(self.timesteps == timestep).nonzero().item()
              UpperCamelCase_ 							=	self.sigmas[step_index]

              UpperCamelCase_ 							=	min(s_churn / (len(self.sigmas		) - 1)       , 2**0.5 - 1		) if s_tmin <= sigma <= s_tmax else 0.0

              UpperCamelCase_ 							=	randn_tensor(
                  model_output.shape       , dtype=model_output.dtype       , device=model_output.device       , generator=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	noise * s_noise
              UpperCamelCase_ 							=	sigma * (gamma + 1)

              if gamma > 0:
                     UpperCamelCase_ 							=	sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

              # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
              # NOTE: "original_sample" should not be an expected prediction_type but is left in for
              # backwards compatibility
              if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
                     UpperCamelCase_ 							=	model_output
              elif self.config.prediction_type == "epsilon":
                     UpperCamelCase_ 							=	sample - sigma_hat * model_output
              elif self.config.prediction_type == "v_prediction":
                     # * c_out + input * c_skip
                     UpperCamelCase_ 							=	model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
              else:
                     raise ValueError(
                         f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''		)

              # 2. Convert to an ODE derivative
              UpperCamelCase_ 							=	(sample - pred_original_sample) / sigma_hat

              UpperCamelCase_ 							=	self.sigmas[step_index + 1] - sigma_hat

              UpperCamelCase_ 							=	sample + derivative * dt

              if not return_dict:
                     return (prev_sample,)

              return EulerDiscreteSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE       , pred_original_sample=_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			torch.FloatTensor       , _SCREAMING_SNAKE_CASE:			torch.FloatTensor       , _SCREAMING_SNAKE_CASE:			torch.FloatTensor       , )							->			torch.FloatTensor:




              """simple docstring"""


              UpperCamelCase_ 							=	self.sigmas.to(device=original_samples.device       , dtype=original_samples.dtype		)
              if original_samples.device.type == "mps" and torch.is_floating_point(_SCREAMING_SNAKE_CASE		):
                     # mps does not support float64
                     UpperCamelCase_ 							=	self.timesteps.to(original_samples.device       , dtype=torch.floataa		)
                     UpperCamelCase_ 							=	timesteps.to(original_samples.device       , dtype=torch.floataa		)
              else:
                     UpperCamelCase_ 							=	self.timesteps.to(original_samples.device		)
                     UpperCamelCase_ 							=	timesteps.to(original_samples.device		)

              UpperCamelCase_ 							=	[(schedule_timesteps == t).nonzero().item() for t in timesteps]

              UpperCamelCase_ 							=	sigmas[step_indices].flatten()
              while len(sigma.shape		) < len(original_samples.shape		):
                     UpperCamelCase_ 							=	sigma.unsqueeze(-1		)

              UpperCamelCase_ 							=	original_samples + noise * sigma
              return noisy_samples



       def __len__(		self:			List[str]		)							->			List[Any]:




              """simple docstring"""


              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


_lowerCamelCase  =	False


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





				"""simple docstring"""







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



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"  )
								# remove text_unet
								pipe.remove_unused_weights()
								pipe.to(UpperCAmelCase__  )
								pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

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

								SCREAMING_SNAKE_CASE__  				=					torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					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__  )
												SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCAmelCase__  )

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

								SCREAMING_SNAKE_CASE__  				=					generator.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					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 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"  )
								pipe.remove_unused_weights()
								pipe.to(UpperCAmelCase__  )
								pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					"cyberpunk 2077"
								SCREAMING_SNAKE_CASE__  				=					load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"  )
								SCREAMING_SNAKE_CASE__  				=					torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					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

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

								assert image.shape == (1, 512, 512, 3)
								SCREAMING_SNAKE_CASE__  				=					np.array([0.0_787, 0.0_849, 0.0_826, 0.0_812, 0.0_807, 0.0_795, 0.0_818, 0.0_798, 0.0_779]  )

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