
"""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


__lowerCAmelCase      :					Tuple   	=       logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		42
  a__   	=		None







def a__   (	A_,					A_=0.999,					A_="cosine",					):






  '''simple docstring'''





  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

  __magic_name__    					=	[]
  for i in range(A_ ):
    __magic_name__    					=	i / num_diffusion_timesteps
    __magic_name__    					=	(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 	UpperCAmelCase_      (			_A  ,	_A					):


  '''simple docstring'''







  a__   	=		[e.name for e in KarrasDiffusionSchedulers]
  a__   	=		1
  @register_to_config
  def __init__(			self      :			Tuple      ,							UpperCamelCase__      :			int = 1000      ,							UpperCamelCase__      :			float = 0.0001      ,							UpperCamelCase__      :			float = 0.02      ,							UpperCamelCase__      :			str = "linear"      ,							UpperCamelCase__      :			Optional[Union[np.ndarray, List[float]]] = None      ,							UpperCamelCase__      :			str = "epsilon"      ,							UpperCamelCase__      :			str = "linear"      ,							UpperCamelCase__      :			Optional[bool] = False      ,							UpperCamelCase__      :			str = "linspace"      ,							UpperCamelCase__      :			int = 0      ,							)      ->     Optional[Any]:







    """simple docstring"""






    if trained_betas is not None:
      __magic_name__    					=	torch.tensor(UpperCamelCase__      ,							dtype=torch.floataa   )
    elif beta_schedule == "linear":
      __magic_name__    					=	torch.linspace(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							dtype=torch.floataa   )
    elif beta_schedule == "scaled_linear":
      # this schedule is very specific to the latent diffusion model.
      __magic_name__    					=	(
          torch.linspace(beta_start**0.5      ,							beta_end**0.5      ,							UpperCamelCase__      ,							dtype=torch.floataa   ) ** 2
      )
    elif beta_schedule == "squaredcos_cap_v2":
      # Glide cosine schedule
      __magic_name__    					=	betas_for_alpha_bar(UpperCamelCase__   )
    else:
      raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''   )

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

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

    # setable values
    __magic_name__    					=	None
    __magic_name__    					=	np.linspace(0      ,							num_train_timesteps - 1      ,							UpperCamelCase__      ,							dtype=UpperCamelCase__   )[::-1].copy()
    __magic_name__    					=	torch.from_numpy(UpperCamelCase__   )
    __magic_name__    					=	False
    __magic_name__    					=	use_karras_sigmas
  @property
  def 							_lowercase							(			self      :			int   )      ->     int:







    """simple docstring"""






    if self.config.timestep_spacing in ["linspace", "trailing"]:
      return self.sigmas.max()

    return (self.sigmas.max() ** 2 + 1) ** 0.5
  def 							_lowercase							(			self      :			List[Any]      ,							UpperCamelCase__      :			torch.FloatTensor      ,							UpperCamelCase__      :			Union[float, torch.FloatTensor]   )      ->     torch.FloatTensor:







    """simple docstring"""






    if isinstance(UpperCamelCase__      ,							torch.Tensor   ):
      __magic_name__    					=	timestep.to(self.timesteps.device   )
    __magic_name__    					=	(self.timesteps == timestep).nonzero().item()
    __magic_name__    					=	self.sigmas[step_index]

    __magic_name__    					=	sample / ((sigma**2 + 1) ** 0.5)

    __magic_name__    					=	True
    return sample
  def 							_lowercase							(			self      :			List[Any]      ,							UpperCamelCase__      :			int      ,							UpperCamelCase__      :			Union[str, torch.device] = None   )      ->     int:







    """simple docstring"""






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

    __magic_name__    					=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
    __magic_name__    					=	np.log(UpperCamelCase__   )

    if self.config.interpolation_type == "linear":
      __magic_name__    					=	np.interp(UpperCamelCase__      ,							np.arange(0      ,							len(UpperCamelCase__   )   )      ,							UpperCamelCase__   )
    elif self.config.interpolation_type == "log_linear":
      __magic_name__    					=	torch.linspace(np.log(sigmas[-1]   )      ,							np.log(sigmas[0]   )      ,							num_inference_steps + 1   ).exp()
    else:
      raise ValueError(
          F'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
          """ 'linear' or 'log_linear'"""   )

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

    __magic_name__    					=	np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
    __magic_name__    					=	torch.from_numpy(UpperCamelCase__   ).to(device=UpperCamelCase__   )
    if str(UpperCamelCase__   ).startswith("""mps"""   ):
      # mps does not support float64
      __magic_name__    					=	torch.from_numpy(UpperCamelCase__   ).to(UpperCamelCase__      ,							dtype=torch.floataa   )
    else:
      __magic_name__    					=	torch.from_numpy(UpperCamelCase__   ).to(device=UpperCamelCase__   )
  def 							_lowercase							(			self      :			int      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			Any   )      ->     List[str]:







    """simple docstring"""






    __magic_name__    					=	np.log(UpperCamelCase__   )

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

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

    __magic_name__    					=	log_sigmas[low_idx]
    __magic_name__    					=	log_sigmas[high_idx]

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

    # transform interpolation to time range
    __magic_name__    					=	(1 - w) * low_idx + w * high_idx
    __magic_name__    					=	t.reshape(sigma.shape   )
    return t
  def 							_lowercase							(			self      :			Union[str, Any]      ,							UpperCamelCase__      :			torch.FloatTensor      ,							UpperCamelCase__      :			Any   )      ->     torch.FloatTensor:







    """simple docstring"""






    __magic_name__    					=	in_sigmas[-1].item()
    __magic_name__    					=	in_sigmas[0].item()

    __magic_name__    					=	7.0  # 7.0 is the value used in the paper
    __magic_name__    					=	np.linspace(0      ,							1      ,							UpperCamelCase__   )
    __magic_name__    					=	sigma_min ** (1 / rho)
    __magic_name__    					=	sigma_max ** (1 / rho)
    __magic_name__    					=	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return sigmas
  def 							_lowercase							(			self      :			str      ,							UpperCamelCase__      :			torch.FloatTensor      ,							UpperCamelCase__      :			Union[float, torch.FloatTensor]      ,							UpperCamelCase__      :			torch.FloatTensor      ,							UpperCamelCase__      :			float = 0.0      ,							UpperCamelCase__      :			float = 0.0      ,							UpperCamelCase__      :			float = float("""inf"""   )      ,							UpperCamelCase__      :			float = 1.0      ,							UpperCamelCase__      :			Optional[torch.Generator] = None      ,							UpperCamelCase__      :			bool = True      ,							)      ->     Union[EulerDiscreteSchedulerOutput, Tuple]:







    """simple docstring"""






    if (
        isinstance(UpperCamelCase__      ,							UpperCamelCase__   )
        or isinstance(UpperCamelCase__      ,							torch.IntTensor   )
        or isinstance(UpperCamelCase__      ,							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(UpperCamelCase__      ,							torch.Tensor   ):
      __magic_name__    					=	timestep.to(self.timesteps.device   )

    __magic_name__    					=	(self.timesteps == timestep).nonzero().item()
    __magic_name__    					=	self.sigmas[step_index]

    __magic_name__    					=	min(s_churn / (len(self.sigmas   ) - 1)      ,							2**0.5 - 1   ) if s_tmin <= sigma <= s_tmax else 0.0

    __magic_name__    					=	randn_tensor(
        model_output.shape      ,							dtype=model_output.dtype      ,							device=model_output.device      ,							generator=UpperCamelCase__   )

    __magic_name__    					=	noise * s_noise
    __magic_name__    					=	sigma * (gamma + 1)

    if gamma > 0:
      __magic_name__    					=	sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    # NOTE: "original_sample" should not be an expected prediction_type but is left in for
    # backwards compatibility
    if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
      __magic_name__    					=	model_output
    elif self.config.prediction_type == "epsilon":
      __magic_name__    					=	sample - sigma_hat * model_output
    elif self.config.prediction_type == "v_prediction":
      # * c_out + input * c_skip
      __magic_name__    					=	model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
    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
    __magic_name__    					=	(sample - pred_original_sample) / sigma_hat

    __magic_name__    					=	self.sigmas[step_index + 1] - sigma_hat

    __magic_name__    					=	sample + derivative * dt

    if not return_dict:
      return (prev_sample,)

    return EulerDiscreteSchedulerOutput(prev_sample=UpperCamelCase__      ,							pred_original_sample=UpperCamelCase__   )
  def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			torch.FloatTensor      ,							UpperCamelCase__      :			torch.FloatTensor      ,							UpperCamelCase__      :			torch.FloatTensor      ,							)      ->     torch.FloatTensor:







    """simple docstring"""






    __magic_name__    					=	self.sigmas.to(device=original_samples.device      ,							dtype=original_samples.dtype   )
    if original_samples.device.type == "mps" and torch.is_floating_point(UpperCamelCase__   ):
      # mps does not support float64
      __magic_name__    					=	self.timesteps.to(original_samples.device      ,							dtype=torch.floataa   )
      __magic_name__    					=	timesteps.to(original_samples.device      ,							dtype=torch.floataa   )
    else:
      __magic_name__    					=	self.timesteps.to(original_samples.device   )
      __magic_name__    					=	timesteps.to(original_samples.device   )

    __magic_name__    					=	[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

    __magic_name__    					=	original_samples + noise * sigma
    return noisy_samples





  def __len__(			self      :			Any   )      ->     Union[str, Any]:







    """simple docstring"""






    return self.config.num_train_timesteps



import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger

def a__   (	A_,					A_ ):






  '''simple docstring'''





  __magic_name__    					=	copy.deepcopy(A_ )
  __magic_name__    					=	sorted(A_ )
  __magic_name__    					=	[bisect.bisect_right(A_,					A_ ) for y in x]
  return quantized

def a__   (	A_,					A_=0 ):






  '''simple docstring'''





  __magic_name__    					=	np.arange(start=3,					stop=A_,					step=4 ).tolist() if k > 0 else [10]
  __magic_name__    					=	_quantize(A_,					A_ )
  # count number of elements per group
  __magic_name__    					=	np.unique(A_,					return_counts=A_ )[1]
  __magic_name__    					=	[0] + bins + [np.inf]
  logger.info("""Using {} as bins for aspect lengths quantization""".format(A_ ) )
  logger.info("""Count of instances per bin: {}""".format(A_ ) )
  return groups





class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  def __init__(			self      :			Dict      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Tuple   )      ->     Union[str, Any]:







    """simple docstring"""






    if not isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
      raise ValueError(
          """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(UpperCamelCase__   )   )
    __magic_name__    					=	sampler
    __magic_name__    					=	group_ids
    __magic_name__    					=	batch_size
  def __iter__(			self      :			Tuple   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	defaultdict(UpperCamelCase__   )
    __magic_name__    					=	defaultdict(UpperCamelCase__   )

    __magic_name__    					=	0
    for idx in self.sampler:
      __magic_name__    					=	self.group_ids[idx]
      buffer_per_group[group_id].append(UpperCamelCase__   )
      samples_per_group[group_id].append(UpperCamelCase__   )
      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
    __magic_name__    					=	len(self   )
    __magic_name__    					=	expected_num_batches - num_batches
    if num_remaining > 0:
      # for the remaining batches, group the batches by similar lengths
      __magic_name__    					=	[]
      for group_id, idxs in sorted(buffer_per_group.items()      ,							key=lambda UpperCamelCase__   : x[0]   ):
        batch_idx.extend(UpperCamelCase__   )
        if len(UpperCamelCase__   ) >= self.batch_size:
          yield batch_idx[: self.batch_size]
          __magic_name__    					=	batch_idx[self.batch_size :]
          num_remaining -= 1
      if len(UpperCamelCase__   ) > 0:
        yield batch_idx
        num_remaining -= 1
    assert num_remaining == 0





  def __len__(			self      :			List[Any]   )      ->     Any:







    """simple docstring"""






    return (len(self.sampler   ) + self.batch_size - 1) // self.batch_size

