


'''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      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     List[str]   ,_UpperCAmelCase     :     List[Any]=0.9_9_9   ,_UpperCAmelCase     :     Any="cosine"   ,)  ->      List[str]:
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(_UpperCAmelCase     :     Union[str, 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(_UpperCAmelCase     :     int  ):
                     return math.exp(t * -12.0  )

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

       _a   :			str					 =[]
       for i in range(_UpperCAmelCase  ):
              _a   :			List[Any]					 =i / num_diffusion_timesteps
              _a   :			Union[str, Any]					 =(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 A__	(						UpperCAmelCase__			,					UpperCAmelCase__				):
      __UpperCamelCase	:  int     	=							[e.name for e in KarrasDiffusionSchedulers]
      __UpperCamelCase	:  int     	=							2






      @register_to_config
      def __init__(			self      :str					,						SCREAMING_SNAKE_CASE      :int = 1_0_0_0					,						SCREAMING_SNAKE_CASE      :float = 0.00_085					,						SCREAMING_SNAKE_CASE      :float = 0.012					,						SCREAMING_SNAKE_CASE      :str = "linear"					,						SCREAMING_SNAKE_CASE      :Optional[Union[np.ndarray, List[float]]] = None					,						SCREAMING_SNAKE_CASE      :str = "epsilon"					,						SCREAMING_SNAKE_CASE      :str = "linspace"					,						SCREAMING_SNAKE_CASE      :int = 0					,						)	-> List[Any]:




             '''simple docstring'''
             if trained_betas is not None:
                    _a   :			str					 =torch.tensor(SCREAMING_SNAKE_CASE					,						dtype=torch.floataa     )
             elif beta_schedule == "linear":
                    _a   :			Union[str, Any]					 =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.
                    _a   :			int					 =(
                        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
                    _a   :			int					 =betas_for_alpha_bar(SCREAMING_SNAKE_CASE     )
             else:
                    raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"     )

             _a   :			Tuple					 =1.0 - self.betas
             _a   :			List[Any]					 =torch.cumprod(self.alphas					,						dim=0     )

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






      def __UpperCAmelCase	(			self      :int					,						SCREAMING_SNAKE_CASE      :Any					,						SCREAMING_SNAKE_CASE      :Optional[Any]=None     )	-> List[Any]:




             '''simple docstring'''
             if schedule_timesteps is None:
                    _a   :			Any					 =self.timesteps

             _a   :			Tuple					 =(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   :			Optional[int]					 =1 if len(SCREAMING_SNAKE_CASE     ) > 1 else 0
             else:
                    _a   :			Optional[Any]					 =timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE     ) else timestep
                    _a   :			Optional[int]					 =self._index_counter[timestep_int]

             return indices[pos].item()






      @property
      def __UpperCAmelCase	(			self      :List[Any]     )	-> str:




             '''simple docstring'''
             # 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      :str					,						SCREAMING_SNAKE_CASE      :torch.FloatTensor					,						SCREAMING_SNAKE_CASE      :Union[float, torch.FloatTensor]					,						)	-> torch.FloatTensor:




             '''simple docstring'''
             _a   :			int					 =self.index_for_timestep(SCREAMING_SNAKE_CASE     )

             if self.state_in_first_order:
                    _a   :			List[Any]					 =self.sigmas[step_index]
             else:
                    _a   :			Union[str, Any]					 =self.sigmas_interpol[step_index - 1]

             _a   :			Optional[Any]					 =sample / ((sigma**2 + 1) ** 0.5)
             return sample






      def __UpperCAmelCase	(			self      :Optional[int]					,						SCREAMING_SNAKE_CASE      :int					,						SCREAMING_SNAKE_CASE      :Union[str, torch.device] = None					,						SCREAMING_SNAKE_CASE      :Optional[int] = None					,						)	-> List[str]:




             '''simple docstring'''
             _a   :			Union[str, Any]					 =num_inference_steps

             _a   :			Optional[int]					 =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   :			Any					 =np.linspace(0					,						num_train_timesteps - 1					,						SCREAMING_SNAKE_CASE					,						dtype=SCREAMING_SNAKE_CASE     )[::-1].copy()
             elif self.config.timestep_spacing == "leading":
                    _a   :			List[Any]					 =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   :			Optional[int]					 =(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":
                    _a   :			List[str]					 =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   :			Optional[int]					 =(np.arange(SCREAMING_SNAKE_CASE					,						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'."     )

             _a   :			int					 =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
             _a   :			Any					 =torch.from_numpy(np.log(SCREAMING_SNAKE_CASE     )     ).to(SCREAMING_SNAKE_CASE     )

             _a   :			Dict					 =np.interp(SCREAMING_SNAKE_CASE					,						np.arange(0					,						len(SCREAMING_SNAKE_CASE     )     )					,						SCREAMING_SNAKE_CASE     )
             _a   :			List[Any]					 =np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
             _a   :			int					 =torch.from_numpy(SCREAMING_SNAKE_CASE     ).to(device=SCREAMING_SNAKE_CASE     )

             # compute up and down sigmas
             _a   :			List[Any]					 =sigmas.roll(-1     )
             _a   :			Tuple					 =0.0
             _a   :			Any					 =(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
             _a   :			Optional[Any]					 =(sigmas_next**2 - sigmas_up**2) ** 0.5
             _a   :			int					 =0.0

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

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

             if str(SCREAMING_SNAKE_CASE     ).startswith("""mps"""     ):
                    # mps does not support float64
                    _a   :			Optional[int]					 =torch.from_numpy(SCREAMING_SNAKE_CASE     ).to(SCREAMING_SNAKE_CASE					,						dtype=torch.floataa     )
             else:
                    _a   :			List[str]					 =torch.from_numpy(SCREAMING_SNAKE_CASE     ).to(SCREAMING_SNAKE_CASE     )

             _a   :			Union[str, Any]					 =self.sigma_to_t(SCREAMING_SNAKE_CASE     ).to(SCREAMING_SNAKE_CASE					,						dtype=timesteps.dtype     )
             _a   :			Optional[Any]					 =torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])					,						dim=-1     ).flatten()

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

             _a   :			Tuple					 =None

             # for exp beta schedules, such as the one for `pipeline_shap_e.py`
             # we need an index counter
             _a   :			Optional[int]					 =defaultdict(SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :Any					,						SCREAMING_SNAKE_CASE      :List[Any]     )	-> Tuple:




             '''simple docstring'''
             _a   :			List[Any]					 =sigma.log()

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

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

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

             # interpolate sigmas
             _a   :			Tuple					 =(low - log_sigma) / (low - high)
             _a   :			Optional[Any]					 =w.clamp(0					,						1     )

             # transform interpolation to time range
             _a   :			Dict					 =(1 - w) * low_idx + w * high_idx
             _a   :			Union[str, Any]					 =t.view(sigma.shape     )
             return t






      @property
      def __UpperCAmelCase	(			self      :Tuple     )	-> Union[str, Any]:




             '''simple docstring'''
             return self.sample is None






      def __UpperCAmelCase	(			self      :str					,						SCREAMING_SNAKE_CASE      :Union[torch.FloatTensor, np.ndarray]					,						SCREAMING_SNAKE_CASE      :Union[float, torch.FloatTensor]					,						SCREAMING_SNAKE_CASE      :Union[torch.FloatTensor, np.ndarray]					,						SCREAMING_SNAKE_CASE      :Optional[torch.Generator] = None					,						SCREAMING_SNAKE_CASE      :bool = True					,						)	-> Union[SchedulerOutput, Tuple]:




             '''simple docstring'''
             _a   :			Optional[Any]					 =self.index_for_timestep(SCREAMING_SNAKE_CASE     )

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

             if self.state_in_first_order:
                    _a   :			str					 =self.sigmas[step_index]
                    _a   :			Tuple					 =self.sigmas_interpol[step_index]
                    _a   :			Dict					 =self.sigmas_up[step_index]
                    _a   :			str					 =self.sigmas_down[step_index - 1]
             else:
                    # 2nd order / KPDM2's method
                    _a   :			Optional[Any]					 =self.sigmas[step_index - 1]
                    _a   :			str					 =self.sigmas_interpol[step_index - 1]
                    _a   :			Tuple					 =self.sigmas_up[step_index - 1]
                    _a   :			List[str]					 =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   :			str					 =0
             _a   :			str					 =sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

             _a   :			List[Any]					 =model_output.device
             _a   :			Optional[int]					 =randn_tensor(model_output.shape					,						dtype=model_output.dtype					,						device=SCREAMING_SNAKE_CASE					,						generator=SCREAMING_SNAKE_CASE     )

             # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
             if self.config.prediction_type == "epsilon":
                    _a   :			List[Any]					 =sigma_hat if self.state_in_first_order else sigma_interpol
                    _a   :			int					 =sample - sigma_input * model_output
             elif self.config.prediction_type == "v_prediction":
                    _a   :			str					 =sigma_hat if self.state_in_first_order else sigma_interpol
                    _a   :			int					 =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   :			Optional[Any]					 =(sample - pred_original_sample) / sigma_hat
                    # 3. delta timestep
                    _a   :			List[Any]					 =sigma_interpol - sigma_hat

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

                    _a   :			Optional[int]					 =self.sample
                    _a   :			Tuple					 =None

                    _a   :			List[Any]					 =sample + derivative * dt
                    _a   :			List[Any]					 =prev_sample + noise * sigma_up

             if not return_dict:
                    return (prev_sample,)

             return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :Optional[int]					,						SCREAMING_SNAKE_CASE      :torch.FloatTensor					,						SCREAMING_SNAKE_CASE      :torch.FloatTensor					,						SCREAMING_SNAKE_CASE      :torch.FloatTensor					,						)	-> torch.FloatTensor:




             '''simple docstring'''
             _a   :			int					 =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
                    _a   :			Optional[int]					 =self.timesteps.to(original_samples.device					,						dtype=torch.floataa     )
                    _a   :			Union[str, Any]					 =timesteps.to(original_samples.device					,						dtype=torch.floataa     )
             else:
                    _a   :			Optional[int]					 =self.timesteps.to(original_samples.device     )
                    _a   :			Any					 =timesteps.to(original_samples.device     )

             _a   :			Optional[int]					 =[self.index_for_timestep(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     ) for t in timesteps]

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

             _a   :			List[str]					 =original_samples + noise * sigma
             return noisy_samples






      def __len__(			self      :str     )	-> str:




             '''simple docstring'''
             return self.config.num_train_timesteps










'''simple docstring'''







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment







class A__	(						unittest.TestCase				):






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




             '''simple docstring'''
             _a   :			Union[str, Any]					 =inspect.getfile(accelerate.test_utils     )
             _a   :			Any					 =os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ["""scripts""", """test_sync.py"""]     )






      @require_cpu
      def __UpperCAmelCase	(			self      :List[str]     )	-> Optional[Any]:




             '''simple docstring'''
             debug_launcher(test_sync.main					,						num_processes=1     )






      @require_cpu
      def __UpperCAmelCase	(			self      :int     )	-> Union[str, Any]:




             '''simple docstring'''
             debug_launcher(test_sync.main     )






      @require_single_gpu
      def __UpperCAmelCase	(			self      :Any     )	-> Optional[int]:




             '''simple docstring'''
             test_sync.main()






      @require_multi_gpu
      def __UpperCAmelCase	(			self      :Optional[Any]     )	-> int:




             '''simple docstring'''
             print(f"Found {torch.cuda.device_count()} devices."     )
             _a   :			int					 =["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
             with patch_environment(omp_num_threads=1     ):
                    execute_subprocess_async(SCREAMING_SNAKE_CASE					,						env=os.environ.copy()     )


