





'''simple docstring'''

import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class      UpperCAmelCase			( UpperCamelCase__       ):
 __lowercase     	=       42
 __lowercase     	=       None





def 		UpperCamelCase       (							_lowerCamelCase      :      int		, _lowerCamelCase      :      Tuple=0.9_9_9		, _lowerCamelCase      :      Optional[Any]="cosine"		, ):
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(_lowerCamelCase      :      List[str] ):
            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(_lowerCamelCase      :      Tuple ):
            return math.exp(t * -1_2.0 )

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

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






class      UpperCAmelCase			( UpperCamelCase__    , UpperCamelCase__       ):
 __lowercase     	=       [e.name for e in KarrasDiffusionSchedulers]
 __lowercase     	=       1
 @register_to_config
 def __init__(       self   :Union[str, Any]		, lowercase_   :int = 10_00		, lowercase_   :float = 0.0_0_0_1		, lowercase_   :float = 0.0_2		, lowercase_   :str = "linear"		, lowercase_   :Optional[Union[np.ndarray, List[float]]] = None		, lowercase_   :Optional[bool] = False		, lowercase_   :str = "epsilon"		, lowercase_   :str = "linspace"		, lowercase_   :int = 0		, )->							Tuple:
     if trained_betas is not None:
         A__       =    torch.tensor(lowercase_		, dtype=torch.floataa							)
     elif beta_schedule == "linear":
         A__       =    torch.linspace(lowercase_		, lowercase_		, lowercase_		, dtype=torch.floataa							)
     elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         A__       =    (
             torch.linspace(beta_start**0.5		, beta_end**0.5		, lowercase_		, dtype=torch.floataa							) ** 2
         )
     elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         A__       =    betas_for_alpha_bar(lowercase_							)
     else:
         raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"							)

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

     A__       =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
     A__       =    np.concatenate([sigmas[::-1], [0.0]]							).astype(np.floataa							)
     A__       =    torch.from_numpy(lowercase_							)

     # setable values
     A__       =    None
     A__       =    use_karras_sigmas
     self.set_timesteps(lowercase_		, lowercase_							)
     A__       =    []
     A__       =    False
 @property
 def   UpperCAmelCase_							(       self   :List[Any]							)->							Optional[int]:
     # 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   :Optional[Any]		, lowercase_   :torch.FloatTensor		, lowercase_   :Union[float, torch.FloatTensor]							)->							torch.FloatTensor:
     if isinstance(lowercase_		, torch.Tensor							):
         A__       =    timestep.to(self.timesteps.device							)
     A__       =    (self.timesteps == timestep).nonzero().item()
     A__       =    self.sigmas[step_index]
     A__       =    sample / ((sigma**2 + 1) ** 0.5)
     A__       =    True
     return sample
 def   UpperCAmelCase_							(       self   :List[Any]		, lowercase_   :int		, lowercase_   :Dict		, lowercase_   :Optional[int]							)->							Dict:

     def lms_derivative(lowercase_   :Optional[int]							):
         A__       =    1.0
         for k in range(lowercase_							):
             if current_order == k:
                 continue
             prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
         return prod

     A__       =    integrate.quad(lowercase_		, self.sigmas[t]		, self.sigmas[t + 1]		, epsrel=1E-4							)[0]

     return integrated_coeff
 def   UpperCAmelCase_							(       self   :Optional[Any]		, lowercase_   :int		, lowercase_   :Union[str, torch.device] = None							)->							Dict:
     A__       =    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":
         A__       =    np.linspace(0		, self.config.num_train_timesteps - 1		, lowercase_		, dtype=lowercase_							)[
             ::-1
         ].copy()
     elif self.config.timestep_spacing == "leading":
         A__       =    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
         A__       =    (np.arange(0		, lowercase_							) * step_ratio).round()[::-1].copy().astype(lowercase_							)
         timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
         A__       =    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
         A__       =    (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'."							)

     A__       =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
     A__       =    np.log(lowercase_							)
     A__       =    np.interp(lowercase_		, np.arange(0		, len(lowercase_							)							)		, lowercase_							)

     if self.use_karras_sigmas:
         A__       =    self._convert_to_karras(in_sigmas=lowercase_							)
         A__       =    np.array([self._sigma_to_t(lowercase_		, lowercase_							) for sigma in sigmas]							)

     A__       =    np.concatenate([sigmas, [0.0]]							).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_							)

     A__       =    []
 def   UpperCAmelCase_							(       self   :List[Any]		, lowercase_   :int		, lowercase_   :List[Any]							)->							Dict:
     # get log sigma
     A__       =    np.log(lowercase_							)

     # get distribution
     A__       =    log_sigma - log_sigmas[:, np.newaxis]

     # get sigmas range
     A__       =    np.cumsum((dists >= 0)		, axis=0							).argmax(axis=0							).clip(max=log_sigmas.shape[0] - 2							)
     A__       =    low_idx + 1

     A__       =    log_sigmas[low_idx]
     A__       =    log_sigmas[high_idx]

     # interpolate sigmas
     A__       =    (low - log_sigma) / (low - high)
     A__       =    np.clip(lowercase_		, 0		, 1							)

     # transform interpolation to time range
     A__       =    (1 - w) * low_idx + w * high_idx
     A__       =    t.reshape(sigma.shape							)
     return t
 def   UpperCAmelCase_							(       self   :Optional[Any]		, lowercase_   :torch.FloatTensor							)->							torch.FloatTensor:
     A__       =    in_sigmas[-1].item()
     A__       =    in_sigmas[0].item()

     A__       =    7.0  # 7.0 is the value used in the paper
     A__       =    np.linspace(0		, 1		, self.num_inference_steps							)
     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   :List[Any]		, lowercase_   :torch.FloatTensor		, lowercase_   :Union[float, torch.FloatTensor]		, lowercase_   :torch.FloatTensor		, lowercase_   :int = 4		, lowercase_   :bool = True		, )->							Union[LMSDiscreteSchedulerOutput, Tuple]:
     if not self.is_scale_input_called:
         warnings.warn(
             "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.timesteps == timestep).nonzero().item()
     A__       =    self.sigmas[step_index]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
         A__       =    sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
         # * c_out + input * c_skip
         A__       =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
     elif self.config.prediction_type == "sample":
         A__       =    model_output
     else:
         raise ValueError(
             F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"							)

     # 2. Convert to an ODE derivative
     A__       =    (sample - pred_original_sample) / sigma
     self.derivatives.append(lowercase_							)
     if len(self.derivatives							) > order:
         self.derivatives.pop(0							)

     # 3. Compute linear multistep coefficients
     A__       =    min(step_index + 1		, lowercase_							)
     A__       =    [self.get_lms_coefficient(lowercase_		, lowercase_		, lowercase_							) for curr_order in range(lowercase_							)]

     # 4. Compute previous sample based on the derivatives path
     A__       =    sample + sum(
         coeff * derivative for coeff, derivative in zip(lowercase_		, reversed(self.derivatives							)							)							)

     if not return_dict:
         return (prev_sample,)

     return LMSDiscreteSchedulerOutput(prev_sample=lowercase_		, pred_original_sample=lowercase_							)
 def   UpperCAmelCase_							(       self   :Tuple		, 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   :Optional[int]							)->							Union[str, 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



