code: 
  
  
  
  
  
  '''simple docstring'''
  
  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 SchedulerMixin
  
  
  __lowerCAmelCase							:			Dict				=logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  
  
  @dataclass
  class      UpperCAmelCase			( UpperCamelCase__       ):
   __lowercase     	=       42
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__    , UpperCamelCase__       ):
   __lowercase     	=       1
   @register_to_config
   def __init__(       self   :Dict		, lowercase_   :int = 40		, lowercase_   :float = 0.0_0_2		, lowercase_   :float = 80.0		, lowercase_   :float = 0.5		, lowercase_   :float = 1.0		, lowercase_   :float = 7.0		, lowercase_   :bool = True		, )->							Any:
       # standard deviation of the initial noise distribution
       A__       =    sigma_max
  
       A__       =    np.linspace(0		, 1		, lowercase_							)
       A__       =    self._convert_to_karras(lowercase_							)
       A__       =    self.sigma_to_t(lowercase_							)
  
       # setable values
       A__       =    None
       A__       =    torch.from_numpy(lowercase_							)
       A__       =    torch.from_numpy(lowercase_							)
       A__       =    False
       A__       =    False
   def   UpperCAmelCase_							(       self   :str		, lowercase_   :Union[str, Any]		, lowercase_   :Optional[int]=None							)->							Union[str, Any]:
       if schedule_timesteps is None:
           A__       =    self.timesteps
  
       A__       =    (schedule_timesteps == timestep).nonzero()
       return indices.item()
   def   UpperCAmelCase_							(       self   :Any		, lowercase_   :torch.FloatTensor		, lowercase_   :Union[float, torch.FloatTensor]							)->							torch.FloatTensor:
       # Get sigma corresponding to timestep
       if isinstance(lowercase_		, torch.Tensor							):
           A__       =    timestep.to(self.timesteps.device							)
       A__       =    self.index_for_timestep(lowercase_							)
       A__       =    self.sigmas[step_idx]
  
       A__       =    sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
  
       A__       =    True
       return sample
   def   UpperCAmelCase_							(       self   :Optional[Any]		, lowercase_   :Union[float, np.ndarray]							)->							Dict:
       if not isinstance(lowercase_		, np.ndarray							):
           A__       =    np.array(lowercase_		, dtype=np.floataa							)
  
       A__       =    10_00 * 0.2_5 * np.log(sigmas + 1E-44							)
  
       return timesteps
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Optional[int] = None		, lowercase_   :Union[str, torch.device] = None		, lowercase_   :Optional[List[int]] = None		, )->							Optional[int]:
       if num_inference_steps is None and timesteps is None:
           raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."							)
  
       if num_inference_steps is not None and timesteps is not None:
           raise ValueError("Can only pass one of `num_inference_steps` or `timesteps`."							)
  
       # Follow DDPMScheduler custom timesteps logic
       if timesteps is not None:
           for i in range(1		, len(lowercase_							)							):
               if timesteps[i] >= timesteps[i - 1]:
                   raise ValueError("`timesteps` must be in descending order."							)
  
           if timesteps[0] >= self.config.num_train_timesteps:
               raise ValueError(
                   F"`timesteps` must start before `self.config.train_timesteps`:"
                   F" {self.config.num_train_timesteps}."							)
  
           A__       =    np.array(lowercase_		, dtype=np.intaa							)
           A__       =    True
       else:
           if num_inference_steps > self.config.num_train_timesteps:
               raise ValueError(
                   F"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
                   F" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
                   F" maximal {self.config.num_train_timesteps} timesteps."							)
  
           A__       =    num_inference_steps
  
           A__       =    self.config.num_train_timesteps // self.num_inference_steps
           A__       =    (np.arange(0		, lowercase_							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
           A__       =    False
  
       # Map timesteps to Karras sigmas directly for multistep sampling
       # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
       A__       =    self.config.num_train_timesteps
       A__       =    timesteps[::-1].copy()
       A__       =    ramp / (num_train_timesteps - 1)
       A__       =    self._convert_to_karras(lowercase_							)
       A__       =    self.sigma_to_t(lowercase_							)
  
       A__       =    np.concatenate([sigmas, [self.sigma_min]]							).astype(np.floataa							)
       A__       =    torch.from_numpy(lowercase_							).to(device=lowercase_							)
  
       if str(lowercase_							).startswith("mps"							):
           # mps does not support float64
           A__       =    torch.from_numpy(lowercase_							).to(lowercase_		, dtype=torch.floataa							)
       else:
           A__       =    torch.from_numpy(lowercase_							).to(device=lowercase_							)
   def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :str							)->							str:
       A__       =    self.config.sigma_min
       A__       =    self.config.sigma_max
  
       A__       =    self.config.rho
       A__       =    sigma_min ** (1 / rho)
       A__       =    sigma_max ** (1 / rho)
       A__       =    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas
   def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :Optional[Any]							)->							Optional[int]:
       A__       =    self.config.sigma_data
  
       A__       =    sigma_data**2 / (sigma**2 + sigma_data**2)
       A__       =    sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out
   def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :int							)->							Any:
       A__       =    self.config.sigma_min
       A__       =    self.config.sigma_data
  
       A__       =    sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
       A__       =    (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out
   def   UpperCAmelCase_							(       self   :str		, lowercase_   :torch.FloatTensor		, lowercase_   :Union[float, torch.FloatTensor]		, lowercase_   :torch.FloatTensor		, lowercase_   :Optional[torch.Generator] = None		, lowercase_   :bool = True		, )->							Union[CMStochasticIterativeSchedulerOutput, Tuple]:
  
       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"
                   F" `{self.__class__}.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							):
           A__       =    timestep.to(self.timesteps.device							)
  
       A__       =    self.config.sigma_min
       A__       =    self.config.sigma_max
  
       A__       =    self.index_for_timestep(lowercase_							)
  
       # sigma_next corresponds to next_t in original implementation
       A__       =    self.sigmas[step_index]
       if step_index + 1 < self.config.num_train_timesteps:
           A__       =    self.sigmas[step_index + 1]
       else:
           # Set sigma_next to sigma_min
           A__       =    self.sigmas[-1]
  
       # Get scalings for boundary conditions
       A__,			A__       =    self.get_scalings_for_boundary_condition(lowercase_							)
  
       # 1. Denoise model output using boundary conditions
       A__       =    c_out * model_output + c_skip * sample
       if self.config.clip_denoised:
           A__       =    denoised.clamp(-1		, 1							)
  
       # 2. Sample z ~ N(0, s_noise^2 * I)
       # Noise is not used for onestep sampling.
       if len(self.timesteps							) > 1:
           A__       =    randn_tensor(
               model_output.shape		, dtype=model_output.dtype		, device=model_output.device		, generator=lowercase_							)
       else:
           A__       =    torch.zeros_like(lowercase_							)
       A__       =    noise * self.config.s_noise
  
       A__       =    sigma_next.clamp(min=lowercase_		, max=lowercase_							)
  
       # 3. Return noisy sample
       # tau = sigma_hat, eps = sigma_min
       A__       =    denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5
  
       if not return_dict:
           return (prev_sample,)
  
       return CMStochasticIterativeSchedulerOutput(prev_sample=lowercase_							)
   def   UpperCAmelCase_							(       self   :List[str]		, lowercase_   :torch.FloatTensor		, lowercase_   :torch.FloatTensor		, lowercase_   :torch.FloatTensor		, )->							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(lowercase_							):
           # 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__       =    [(schedule_timesteps == t).nonzero().item() 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							)->							Any:
       return self.config.num_train_timesteps
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import bisect
  import copy
  from collections import defaultdict
  
  import numpy as np
  from torch.utils.data import BatchSampler, Sampler
  
  from utils import logger
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      Optional[Any] ):
      A__       =    copy.deepcopy(_lowerCamelCase )
      A__       =    sorted(_lowerCamelCase )
      A__       =    [bisect.bisect_right(_lowerCamelCase		, _lowerCamelCase ) for y in x]
      return quantized
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int]		, _lowerCamelCase      :      Tuple=0 ):
      A__       =    np.arange(start=3		, stop=_lowerCamelCase		, step=4 ).tolist() if k > 0 else [10]
      A__       =    _quantize(_lowerCamelCase		, _lowerCamelCase )
      # count number of elements per group
      A__       =    np.unique(_lowerCamelCase		, return_counts=_lowerCamelCase )[1]
      A__       =    [0] + bins + [np.inf]
      logger.info("Using {} as bins for aspect lengths quantization".format(_lowerCamelCase ) )
      logger.info("Count of instances per bin: {}".format(_lowerCamelCase ) )
      return groups
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :Optional[Any]		, lowercase_   :List[Any]		, lowercase_   :Optional[int]		, lowercase_   :str							)->							Dict:
       if not isinstance(lowercase_		, lowercase_							):
           raise ValueError(
               "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(lowercase_							)							)
       A__       =    sampler
       A__       =    group_ids
       A__       =    batch_size
   def __iter__(       self   :int							)->							Tuple:
       A__       =    defaultdict(lowercase_							)
       A__       =    defaultdict(lowercase_							)
  
       A__       =    0
       for idx in self.sampler:
           A__       =    self.group_ids[idx]
           buffer_per_group[group_id].append(lowercase_							)
           samples_per_group[group_id].append(lowercase_							)
           if len(buffer_per_group[group_id]							) == self.batch_size:
               yield buffer_per_group[group_id]  # TODO
               num_batches += 1
               del buffer_per_group[group_id]
           assert len(buffer_per_group[group_id]							) < self.batch_size
  
       # now we have run out of elements that satisfy
       # the group criteria, let's return the remaining
       # elements so that the size of the sampler is
       # deterministic
       A__       =    len(self							)
       A__       =    expected_num_batches - num_batches
       if num_remaining > 0:
           # for the remaining batches, group the batches by similar lengths
           A__       =    []
           for group_id, idxs in sorted(buffer_per_group.items()		, key=lambda lowercase_							: x[0]							):
               batch_idx.extend(lowercase_							)
               if len(lowercase_							) >= self.batch_size:
                   yield batch_idx[: self.batch_size]
                   A__       =    batch_idx[self.batch_size :]
                   num_remaining -= 1
           if len(lowercase_							) > 0:
               yield batch_idx
               num_remaining -= 1
       assert num_remaining == 0
  
  
  
   def __len__(       self   :int							)->							Tuple:
       return (len(self.sampler							) + self.batch_size - 1) // self.batch_size
  
  
  
  