

'''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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[str]							, SCREAMING_SNAKE_CASE   : Optional[int]=0.999							, SCREAMING_SNAKE_CASE   : int="cosine"							, ):
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(SCREAMING_SNAKE_CASE   : Optional[int]    ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

  elif alpha_transform_type == "exp":

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

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

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



class 							lowercase_						(		a			,							a			):

  '''simple docstring'''





  __lowerCAmelCase       :			List[str]     	= [e.name for e in KarrasDiffusionSchedulers]
  __lowerCAmelCase       :			Dict     	= 2
  @register_to_config
  def __init__(  self ,			a_ = 1_0_0_0 ,			a_ = 0.0_0085 ,			a_ = 0.012 ,			a_ = "linear" ,			a_ = None ,			a_ = "epsilon" ,			a_ = "linspace" ,			a_ = 0 ,			)				->					Tuple:


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

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

    #  set all values
    self.set_timesteps(a_ ,			a_ ,			a_  )
  def     snake_case_      (  self ,			a_ ,			a_=None  )				->					Optional[Any]:


    """simple docstring"""
    if schedule_timesteps is None:
      UpperCAmelCase    				=						self.timesteps

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

    return indices[pos].item()
  @property
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    # 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     snake_case_      (  self ,			a_ ,			a_ ,			)				->					torch.FloatTensor:


    """simple docstring"""
    UpperCAmelCase    				=						self.index_for_timestep(a_  )

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

    UpperCAmelCase    				=						sample / ((sigma**2 + 1) ** 0.5)
    return sample
  def     snake_case_      (  self ,			a_ ,			a_ = None ,			a_ = None ,			)				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						num_inference_steps

    UpperCAmelCase    				=						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":
      UpperCAmelCase    				=						np.linspace(0 ,			num_train_timesteps - 1 ,			a_ ,			dtype=a_  )[::-1].copy()
    elif self.config.timestep_spacing == "leading":
      UpperCAmelCase    				=						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
      UpperCAmelCase    				=						(np.arange(0 ,			a_  ) * step_ratio).round()[::-1].copy().astype(a_  )
      timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
      UpperCAmelCase    				=						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
      UpperCAmelCase    				=						(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\'.'''  )

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

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

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

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

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

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

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

    UpperCAmelCase    				=						None

    # for exp beta schedules, such as the one for `pipeline_shap_e.py`
    # we need an index counter
    UpperCAmelCase    				=						defaultdict(a_  )
  def     snake_case_      (  self ,			a_  )				->					Tuple:


    """simple docstring"""
    # get log sigma
    UpperCAmelCase    				=						sigma.log()

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

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

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

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

    # transform interpolation to time range
    UpperCAmelCase    				=						(1 - w) * low_idx + w * high_idx
    UpperCAmelCase    				=						t.view(sigma.shape  )
    return t
  @property
  def     snake_case_      (  self  )				->					Optional[Any]:


    """simple docstring"""
    return self.sample is None
  def     snake_case_      (  self ,			a_ ,			a_ ,			a_ ,			a_ = True ,			)				->					Union[SchedulerOutput, Tuple]:


    """simple docstring"""
    UpperCAmelCase    				=						self.index_for_timestep(a_  )

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

    if self.state_in_first_order:
      UpperCAmelCase    				=						self.sigmas[step_index]
      UpperCAmelCase    				=						self.sigmas_interpol[step_index + 1]
      UpperCAmelCase    				=						self.sigmas[step_index + 1]
    else:
      # 2nd order / KDPM2's method
      UpperCAmelCase    				=						self.sigmas[step_index - 1]
      UpperCAmelCase    				=						self.sigmas_interpol[step_index]
      UpperCAmelCase    				=						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
    UpperCAmelCase    				=						0
    UpperCAmelCase    				=						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":
      UpperCAmelCase    				=						sigma_hat if self.state_in_first_order else sigma_interpol
      UpperCAmelCase    				=						sample - sigma_input * model_output
    elif self.config.prediction_type == "v_prediction":
      UpperCAmelCase    				=						sigma_hat if self.state_in_first_order else sigma_interpol
      UpperCAmelCase    				=						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
      UpperCAmelCase    				=						(sample - pred_original_sample) / sigma_hat
      # 3. delta timestep
      UpperCAmelCase    				=						sigma_interpol - sigma_hat

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

      # 3. delta timestep
      UpperCAmelCase    				=						sigma_next - sigma_hat

      UpperCAmelCase    				=						self.sample
      UpperCAmelCase    				=						None

    UpperCAmelCase    				=						sample + derivative * dt

    if not return_dict:
      return (prev_sample,)

    return SchedulerOutput(prev_sample=a_  )
  def     snake_case_      (  self ,			a_ ,			a_ ,			a_ ,			)				->					torch.FloatTensor:


    """simple docstring"""
    # Make sure sigmas and timesteps have the same device and dtype as original_samples
    UpperCAmelCase    				=						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
      UpperCAmelCase    				=						self.timesteps.to(original_samples.device ,			dtype=torch.floataa  )
      UpperCAmelCase    				=						timesteps.to(original_samples.device ,			dtype=torch.floataa  )
    else:
      UpperCAmelCase    				=						self.timesteps.to(original_samples.device  )
      UpperCAmelCase    				=						timesteps.to(original_samples.device  )

    UpperCAmelCase    				=						[self.index_for_timestep(a_ ,			a_  ) for t in timesteps]

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

    UpperCAmelCase    				=						original_samples + noise * sigma
    return noisy_samples





  def __len__(  self  )				->					Dict:


    """simple docstring"""
    return self.config.num_train_timesteps





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_a					:			Any							=    {
    'configuration_groupvit': [
        'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'GroupViTConfig',
        'GroupViTOnnxConfig',
        'GroupViTTextConfig',
        'GroupViTVisionConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Union[str, Any]							=    [
      'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'GroupViTModel',
      'GroupViTPreTrainedModel',
      'GroupViTTextModel',
      'GroupViTVisionModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Tuple							=    [
      'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFGroupViTModel',
      'TFGroupViTPreTrainedModel',
      'TFGroupViTTextModel',
      'TFGroupViTVisionModel',
  ]

if TYPE_CHECKING:
  from .configuration_groupvit import (
      GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      GroupViTConfig,
      GroupViTOnnxConfig,
      GroupViTTextConfig,
      GroupViTVisionConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_groupvit import (
        GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        GroupViTModel,
        GroupViTPreTrainedModel,
        GroupViTTextModel,
        GroupViTVisionModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_groupvit import (
        TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFGroupViTModel,
        TFGroupViTPreTrainedModel,
        TFGroupViTTextModel,
        TFGroupViTVisionModel,
    )

else:
  import sys

  _a					:			Union[str, Any]							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


