'''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 _lowerCAmelCase (  unittest.TestCase				):







      def     lowerCamelCase__			(     self    ):
        lowercase							:       Dict											=      inspect.getfile(accelerate.test_utils    )
        lowercase							:       Optional[Any]											=      os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ['''scripts''', '''test_sync.py''']    )







      @require_cpu
      def     lowerCamelCase__			(     self    ):
        debug_launcher(test_sync.main     ,						num_processes=1    )







      @require_cpu
      def     lowerCamelCase__			(     self    ):
        debug_launcher(test_sync.main    )







      @require_single_gpu
      def     lowerCamelCase__			(     self    ):
        test_sync.main()







      @require_multi_gpu
      def     lowerCamelCase__			(     self    ):
        print(F"""Found {torch.cuda.device_count()} devices."""    )
        lowercase							:       Tuple											=      ['''torchrun''', F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
        with patch_environment(omp_num_threads=1    ):
          execute_subprocess_async(__snake_case     ,						env=os.environ.copy()    )
'''simple docstring'''
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


_A		:			Union[str, Any]	      =logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class _lowerCAmelCase (  snake_case				):
      UpperCAmelCase_  :	torch.FloatTensor
      UpperCAmelCase_  :	Optional[torch.FloatTensor]     =					None







def 							__UpperCamelCase   (				__UpperCAmelCase     :				Any   ,      __UpperCAmelCase     :				Tuple=0.999   ,      __UpperCAmelCase     :				List[Any]="cosine"   ,      )       ->  Dict:
  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

  lowercase							:       Optional[int]											=      []
  for i in range(__UpperCAmelCase ):
    lowercase							:       Union[str, Any]											=      i / num_diffusion_timesteps
    lowercase							:       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 _lowerCAmelCase (  snake_case       ,      snake_case				):
      UpperCAmelCase_  :	Dict     =					[e.name for e in KarrasDiffusionSchedulers]
      UpperCAmelCase_  :	List[str]     =					1







      @register_to_config
      def __init__(     self     ,						lowercase = 1000     ,						lowercase = 0.0001     ,						lowercase = 0.02     ,						lowercase = "linear"     ,						lowercase = None     ,						lowercase = "epsilon"     ,						lowercase = "linspace"     ,						lowercase = 0     ,						):
        if trained_betas is not None:
          lowercase							:       int											=      torch.tensor(lowercase     ,						dtype=torch.floataa    )
        elif beta_schedule == "linear":
          lowercase							:       Optional[int]											=      torch.linspace(lowercase     ,						lowercase     ,						lowercase     ,						dtype=torch.floataa    )
        elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          lowercase							:       Any											=      (
              torch.linspace(beta_start**0.5     ,						beta_end**0.5     ,						lowercase     ,						dtype=torch.floataa    ) ** 2
          )
        elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          lowercase							:       List[str]											=      betas_for_alpha_bar(lowercase    )
        else:
          raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""    )

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

        lowercase							:       Dict											=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
        lowercase							:       List[str]											=      np.concatenate([sigmas[::-1], [0.0]]    ).astype(np.floataa    )
        lowercase							:       List[Any]											=      torch.from_numpy(lowercase    )

        # setable values
        lowercase							:       str											=      None
        lowercase							:       Optional[int]											=      np.linspace(0     ,						num_train_timesteps - 1     ,						lowercase     ,						dtype=lowercase    )[::-1].copy()
        lowercase							:       str											=      torch.from_numpy(lowercase    )
        lowercase							:       Any											=      False







      @property
      def     lowerCamelCase__			(     self    ):
        # 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     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase    ):
        if isinstance(lowercase     ,						torch.Tensor    ):
          lowercase							:       Union[str, Any]											=      timestep.to(self.timesteps.device    )
        lowercase							:       int											=      (self.timesteps == timestep).nonzero().item()
        lowercase							:       str											=      self.sigmas[step_index]
        lowercase							:       Union[str, Any]											=      sample / ((sigma**2 + 1) ** 0.5)
        lowercase							:       Optional[int]											=      True
        return sample







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase = None    ):
        lowercase							:       Union[str, Any]											=      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":
          lowercase							:       Dict											=      np.linspace(0     ,						self.config.num_train_timesteps - 1     ,						lowercase     ,						dtype=lowercase    )[
              ::-1
          ].copy()
        elif self.config.timestep_spacing == "leading":
          lowercase							:       str											=      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
          lowercase							:       Optional[Any]											=      (np.arange(0     ,						lowercase    ) * step_ratio).round()[::-1].copy().astype(lowercase    )
          timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
          lowercase							:       Dict											=      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
          lowercase							:       Tuple											=      (np.arange(self.config.num_train_timesteps     ,						0     ,						-step_ratio    )).round().copy().astype(lowercase    )
          timesteps -= 1
        else:
          raise ValueError(
              F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""    )

        lowercase							:       Dict											=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
        lowercase							:       Union[str, Any]											=      np.interp(lowercase     ,						np.arange(0     ,						len(lowercase    )    )     ,						lowercase    )
        lowercase							:       str											=      np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
        lowercase							:       Any											=      torch.from_numpy(lowercase    ).to(device=lowercase    )
        if str(lowercase    ).startswith('''mps'''    ):
          # mps does not support float64
          lowercase							:       List[str]											=      torch.from_numpy(lowercase    ).to(lowercase     ,						dtype=torch.floataa    )
        else:
          lowercase							:       Tuple											=      torch.from_numpy(lowercase    ).to(device=lowercase    )







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase     ,						lowercase     ,						lowercase = None     ,						lowercase = True     ,						):

        if (
            isinstance(lowercase     ,						lowercase    )
            or isinstance(lowercase     ,						torch.IntTensor    )
            or isinstance(lowercase     ,						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(lowercase     ,						torch.Tensor    ):
          lowercase							:       Union[str, Any]											=      timestep.to(self.timesteps.device    )

        lowercase							:       str											=      (self.timesteps == timestep).nonzero().item()
        lowercase							:       int											=      self.sigmas[step_index]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
          lowercase							:       Tuple											=      sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          lowercase							:       Optional[Any]											=      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`"""    )

        lowercase							:       Optional[Any]											=      self.sigmas[step_index]
        lowercase							:       int											=      self.sigmas[step_index + 1]
        lowercase							:       List[str]											=      (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
        lowercase							:       int											=      (sigma_to**2 - sigma_up**2) ** 0.5

        # 2. Convert to an ODE derivative
        lowercase							:       List[Any]											=      (sample - pred_original_sample) / sigma

        lowercase							:       Union[str, Any]											=      sigma_down - sigma

        lowercase							:       Tuple											=      sample + derivative * dt

        lowercase							:       Any											=      model_output.device
        lowercase							:       Tuple											=      randn_tensor(model_output.shape     ,						dtype=model_output.dtype     ,						device=lowercase     ,						generator=lowercase    )

        lowercase							:       Dict											=      prev_sample + noise * sigma_up

        if not return_dict:
          return (prev_sample,)

        return EulerAncestralDiscreteSchedulerOutput(
            prev_sample=lowercase     ,						pred_original_sample=lowercase    )







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase     ,						lowercase     ,						):
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        lowercase							:       Optional[Any]											=      self.sigmas.to(device=original_samples.device     ,						dtype=original_samples.dtype    )
        if original_samples.device.type == "mps" and torch.is_floating_point(lowercase    ):
          # mps does not support float64
          lowercase							:       Union[str, Any]											=      self.timesteps.to(original_samples.device     ,						dtype=torch.floataa    )
          lowercase							:       str											=      timesteps.to(original_samples.device     ,						dtype=torch.floataa    )
        else:
          lowercase							:       List[str]											=      self.timesteps.to(original_samples.device    )
          lowercase							:       List[str]											=      timesteps.to(original_samples.device    )

        lowercase							:       List[Any]											=      [(schedule_timesteps == t).nonzero().item() for t in timesteps]

        lowercase							:       List[Any]											=      sigmas[step_indices].flatten()
        while len(sigma.shape    ) < len(original_samples.shape    ):
          lowercase							:       Dict											=      sigma.unsqueeze(-1    )

        lowercase							:       Dict											=      original_samples + noise * sigma
        return noisy_samples







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