


"""simple docstring"""






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def    A_ ( _lowerCAmelCase  :       str,  _lowerCAmelCase  :       Union[str, Any]=0.9_9_9,  _lowerCAmelCase  :       List[Any]="cosine",  ):






     """simple docstring"""

     if alpha_transform_type == "cosine":

          def alpha_bar_fn(_lowerCAmelCase  :       Optional[Any]							):
               return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2							) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(_lowerCAmelCase  :       int							):
               return math.exp(t * -1_2.0							)

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

     _a     						=					[]
     for i in range(_lowerCAmelCase							):
          _a     						=					i / num_diffusion_timesteps
          _a     						=					(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      __lowerCamelCase	(       a__	,       a__					):

     '''simple docstring'''
     A_							:						int     =		[e.name for e in KarrasDiffusionSchedulers]
     A_							:						List[Any]     =		2


     @register_to_config
     def __init__(   self     ,					__UpperCAmelCase = 1000     ,					__UpperCAmelCase = 0.00085     ,					__UpperCAmelCase = 0.012     ,					__UpperCAmelCase = "linear"     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = "epsilon"     ,					__UpperCAmelCase = "linspace"     ,					__UpperCAmelCase = 0     ,					)							->				str:
          if trained_betas is not None:
               _a     						=					torch.tensor(__UpperCAmelCase     ,					dtype=torch.floataa				)
          elif beta_schedule == "linear":
               _a     						=					torch.linspace(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					dtype=torch.floataa				)
          elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _a     						=					(
                   torch.linspace(beta_start**0.5     ,					beta_end**0.5     ,					__UpperCAmelCase     ,					dtype=torch.floataa				) ** 2
               )
          elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _a     						=					betas_for_alpha_bar(__UpperCAmelCase				)
          else:
               raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'				)

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

          #  set all values
          self.set_timesteps(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=None				)							->				str:
          if schedule_timesteps is None:
               _a     						=					self.timesteps

          _a     						=					(schedule_timesteps == timestep).nonzero()

          # The sigma index that is taken for the **very** first `step`
          # is always the second index (or the last index if there is only 1)
          # This way we can ensure we don't accidentally skip a sigma in
          # case we start in the middle of the denoising schedule (e.g. for image-to-image)
          if len(self._index_counter				) == 0:
               _a     						=					1 if len(__UpperCAmelCase				) > 1 else 0
          else:
               _a     						=					timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase				) else timestep
               _a     						=					self._index_counter[timestep_int]

          return indices[pos].item()


     @property
     def 				_UpperCAmelCase   (   self				)							->				int:
          # standard deviation of the initial noise distribution
          if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

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


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					)							->				torch.FloatTensor:
          _a     						=					self.index_for_timestep(__UpperCAmelCase				)

          if self.state_in_first_order:
               _a     						=					self.sigmas[step_index]
          else:
               _a     						=					self.sigmas_interpol[step_index - 1]

          _a     						=					sample / ((sigma**2 + 1) ** 0.5)
          return sample


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					)							->				Tuple:
          _a     						=					num_inference_steps

          _a     						=					num_train_timesteps or self.config.num_train_timesteps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
               _a     						=					np.linspace(0     ,					num_train_timesteps - 1     ,					__UpperCAmelCase     ,					dtype=__UpperCAmelCase				)[::-1].copy()
          elif self.config.timestep_spacing == "leading":
               _a     						=					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
               _a     						=					(np.arange(0     ,					__UpperCAmelCase				) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase				)
               timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
               _a     						=					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
               _a     						=					(np.arange(__UpperCAmelCase     ,					0     ,					-step_ratio				)).round().copy().astype(__UpperCAmelCase				)
               timesteps -= 1
          else:
               raise ValueError(
                   F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'				)

          _a     						=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
          _a     						=					torch.from_numpy(np.log(__UpperCAmelCase				)				).to(__UpperCAmelCase				)

          _a     						=					np.interp(__UpperCAmelCase     ,					np.arange(0     ,					len(__UpperCAmelCase				)				)     ,					__UpperCAmelCase				)
          _a     						=					np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
          _a     						=					torch.from_numpy(__UpperCAmelCase				).to(device=__UpperCAmelCase				)

          # compute up and down sigmas
          _a     						=					sigmas.roll(-1				)
          _a     						=					0.0
          _a     						=					(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
          _a     						=					(sigmas_next**2 - sigmas_up**2) ** 0.5
          _a     						=					0.0

          # compute interpolated sigmas
          _a     						=					sigmas.log().lerp(sigmas_down.log()     ,					0.5				).exp()
          _a     						=					0.0

          # set sigmas
          _a     						=					torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2				), sigmas[-1:]]				)
          _a     						=					torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2				), sigmas_interpol[-1:]]				)
          _a     						=					torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2				), sigmas_up[-1:]]				)
          _a     						=					torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2				), sigmas_down[-1:]]				)

          if str(__UpperCAmelCase				).startswith('''mps'''				):
               # mps does not support float64
               _a     						=					torch.from_numpy(__UpperCAmelCase				).to(__UpperCAmelCase     ,					dtype=torch.floataa				)
          else:
               _a     						=					torch.from_numpy(__UpperCAmelCase				).to(__UpperCAmelCase				)

          _a     						=					self.sigma_to_t(__UpperCAmelCase				).to(__UpperCAmelCase     ,					dtype=timesteps.dtype				)
          _a     						=					torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])     ,					dim=-1				).flatten()

          _a     						=					torch.cat([timesteps[:1], interleaved_timesteps]				)

          _a     						=					None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          _a     						=					defaultdict(__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				List[str]:
          # get log sigma
          _a     						=					sigma.log()

          # get distribution
          _a     						=					log_sigma - self.log_sigmas[:, None]

          # get sigmas range
          _a     						=					dists.ge(0				).cumsum(dim=0				).argmax(dim=0				).clamp(max=self.log_sigmas.shape[0] - 2				)
          _a     						=					low_idx + 1

          _a     						=					self.log_sigmas[low_idx]
          _a     						=					self.log_sigmas[high_idx]

          # interpolate sigmas
          _a     						=					(low - log_sigma) / (low - high)
          _a     						=					w.clamp(0     ,					1				)

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


     @property
     def 				_UpperCAmelCase   (   self				)							->				Optional[int]:
          return self.sample is None


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = True     ,					)							->				Union[SchedulerOutput, Tuple]:
          _a     						=					self.index_for_timestep(__UpperCAmelCase				)

          # advance index counter by 1
          _a     						=					timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase				) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
               _a     						=					self.sigmas[step_index]
               _a     						=					self.sigmas_interpol[step_index]
               _a     						=					self.sigmas_up[step_index]
               _a     						=					self.sigmas_down[step_index - 1]
          else:
               # 2nd order / KPDM2's method
               _a     						=					self.sigmas[step_index - 1]
               _a     						=					self.sigmas_interpol[step_index - 1]
               _a     						=					self.sigmas_up[step_index - 1]
               _a     						=					self.sigmas_down[step_index - 1]

          # currently only gamma=0 is supported. This usually works best anyways.
          # We can support gamma in the future but then need to scale the timestep before
          # passing it to the model which requires a change in API
          _a     						=					0
          _a     						=					sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          _a     						=					model_output.device
          _a     						=					randn_tensor(model_output.shape     ,					dtype=model_output.dtype     ,					device=__UpperCAmelCase     ,					generator=__UpperCAmelCase				)

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
               _a     						=					sigma_hat if self.state_in_first_order else sigma_interpol
               _a     						=					sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
               _a     						=					sigma_hat if self.state_in_first_order else sigma_interpol
               _a     						=					model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                   sample / (sigma_input**2 + 1)
               )
          elif self.config.prediction_type == "sample":
               raise NotImplementedError('''prediction_type not implemented yet: sample'''				)
          else:
               raise ValueError(
                   F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'				)

          if self.state_in_first_order:
               # 2. Convert to an ODE derivative for 1st order
               _a     						=					(sample - pred_original_sample) / sigma_hat
               # 3. delta timestep
               _a     						=					sigma_interpol - sigma_hat

               # store for 2nd order step
               _a     						=					sample
               _a     						=					dt
               _a     						=					sample + derivative * dt
          else:
               # DPM-Solver-2
               # 2. Convert to an ODE derivative for 2nd order
               _a     						=					(sample - pred_original_sample) / sigma_interpol
               # 3. delta timestep
               _a     						=					sigma_down - sigma_hat

               _a     						=					self.sample
               _a     						=					None

               _a     						=					sample + derivative * dt
               _a     						=					prev_sample + noise * sigma_up

          if not return_dict:
               return (prev_sample,)

          return SchedulerOutput(prev_sample=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					)							->				torch.FloatTensor:
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          _a     						=					self.sigmas.to(device=original_samples.device     ,					dtype=original_samples.dtype				)
          if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCAmelCase				):
               # mps does not support float64
               _a     						=					self.timesteps.to(original_samples.device     ,					dtype=torch.floataa				)
               _a     						=					timesteps.to(original_samples.device     ,					dtype=torch.floataa				)
          else:
               _a     						=					self.timesteps.to(original_samples.device				)
               _a     						=					timesteps.to(original_samples.device				)

          _a     						=					[self.index_for_timestep(__UpperCAmelCase     ,					__UpperCAmelCase				) for t in timesteps]

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

          _a     						=					original_samples + noise * sigma
          return noisy_samples





     def __len__(   self				)							->				Any:
          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