





'''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








'''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



