





'''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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def    __UpperCamelCase	(	a    : Any					,   a    : Any=0.999					,   a    : Any="cosine"					,   ) ->List[str]:
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(a    : Union[str, Any]     ):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

 elif alpha_transform_type == "exp":

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

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

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






class 			_lowercase						(       __a				,		__a					):
     _UpperCAmelCase										=					[e.name for e in KarrasDiffusionSchedulers]
     _UpperCAmelCase										=					2







     @register_to_config
     def __init__( self    ,   A__ = 10_00    ,   A__ = 0.0_0_0_8_5    ,   A__ = 0.0_1_2    ,   A__ = "linear"    ,   A__ = None    ,   A__ = "epsilon"    ,   A__ = "linspace"    ,   A__ = 0    ,   ):
      if trained_betas is not None:
       snake_case				=			torch.tensor(A__    ,   dtype=torch.floataa    )
      elif beta_schedule == "linear":
       snake_case				=			torch.linspace(A__    ,   A__    ,   A__    ,   dtype=torch.floataa    )
      elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       snake_case				=			(
           torch.linspace(beta_start**0.5    ,   beta_end**0.5    ,   A__    ,   dtype=torch.floataa    ) ** 2
       )
      elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       snake_case				=			betas_for_alpha_bar(A__    )
      else:
       raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""    )

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

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







     def     UpperCamelCase			( self    ,   A__    ,   A__=None    ):
      if schedule_timesteps is None:
       snake_case				=			self.timesteps

      snake_case				=			(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:
       snake_case				=			1 if len(A__    ) > 1 else 0
      else:
       snake_case				=			timestep.cpu().item() if torch.is_tensor(A__    ) else timestep
       snake_case				=			self._index_counter[timestep_int]

      return indices[pos].item()







     @property
     def     UpperCamelCase			( 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     UpperCamelCase			( self    ,   A__    ,   A__    ,   ):
      snake_case				=			self.index_for_timestep(A__    )

      if self.state_in_first_order:
       snake_case				=			self.sigmas[step_index]
      else:
       snake_case				=			self.sigmas_interpol[step_index]

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







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ,   A__ = None    ,   ):
      snake_case				=			num_inference_steps

      snake_case				=			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":
       snake_case				=			np.linspace(0    ,   num_train_timesteps - 1    ,   A__    ,   dtype=A__    )[::-1].copy()
      elif self.config.timestep_spacing == "leading":
       snake_case				=			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
       snake_case				=			(np.arange(0    ,   A__    ) * step_ratio).round()[::-1].copy().astype(A__    )
       timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
       snake_case				=			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
       snake_case				=			(np.arange(A__    ,   0    ,   -step_ratio    )).round().copy().astype(A__    )
       timesteps -= 1
      else:
       raise ValueError(
           F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""    )

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

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

      # interpolate sigmas
      snake_case				=			sigmas.log().lerp(sigmas.roll(1    ).log()    ,   0.5    ).exp()

      snake_case				=			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2    ), sigmas[-1:]]    )
      snake_case				=			torch.cat(
          [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2    ), sigmas_interpol[-1:]]    )

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

      # interpolate timesteps
      snake_case				=			self.sigma_to_t(A__    ).to(A__    ,   dtype=timesteps.dtype    )
      snake_case				=			torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])    ,   dim=-1    ).flatten()

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

      snake_case				=			None

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







     def     UpperCamelCase			( self    ,   A__    ):
      # get log sigma
      snake_case				=			sigma.log()

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

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

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

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

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







     @property
     def     UpperCamelCase			( self    ):
      return self.sample is None







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__ = True    ,   ):
      snake_case				=			self.index_for_timestep(A__    )

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

      if self.state_in_first_order:
       snake_case				=			self.sigmas[step_index]
       snake_case				=			self.sigmas_interpol[step_index + 1]
       snake_case				=			self.sigmas[step_index + 1]
      else:
       # 2nd order / KDPM2's method
       snake_case				=			self.sigmas[step_index - 1]
       snake_case				=			self.sigmas_interpol[step_index]
       snake_case				=			self.sigmas[step_index]

      # 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
      snake_case				=			0
      snake_case				=			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

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

       # store for 2nd order step
       snake_case				=			sample
      else:
       # DPM-Solver-2
       # 2. Convert to an ODE derivative for 2nd order
       snake_case				=			(sample - pred_original_sample) / sigma_interpol

       # 3. delta timestep
       snake_case				=			sigma_next - sigma_hat

       snake_case				=			self.sample
       snake_case				=			None

      snake_case				=			sample + derivative * dt

      if not return_dict:
       return (prev_sample,)

      return SchedulerOutput(prev_sample=A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   ):
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      snake_case				=			self.sigmas.to(device=original_samples.device    ,   dtype=original_samples.dtype    )
      if original_samples.device.type == "mps" and torch.is_floating_point(A__    ):
       # mps does not support float64
       snake_case				=			self.timesteps.to(original_samples.device    ,   dtype=torch.floataa    )
       snake_case				=			timesteps.to(original_samples.device    ,   dtype=torch.floataa    )
      else:
       snake_case				=			self.timesteps.to(original_samples.device    )
       snake_case				=			timesteps.to(original_samples.device    )

      snake_case				=			[self.index_for_timestep(A__    ,   A__    ) for t in timesteps]

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

      snake_case				=			original_samples + noise * sigma
      return noisy_samples







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








'''simple docstring'''







_lowercase       						=			{
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}

