"""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(	a   ,    a=0.9_99   ,    a="cosine"   ,    ):





       if alpha_transform_type == "cosine":

              def alpha_bar_fn(a				):
                     return math.cos((t + 0.0_08) / 1.0_08 * 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}"				)

       __a       =    []
       for i in range(SCREAMING_SNAKE_CASE_				):
              __a       =    i / num_diffusion_timesteps
              __a       =    (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  snake_case__      (       snake_case_,						snake_case_       ):
 _snake_case       :					Optional[Any]							  =						[e.name for e in KarrasDiffusionSchedulers]
 _snake_case       :					Any							  =						2


 @register_to_config
 def __init__( self    ,		lowerCamelCase = 1000    ,		lowerCamelCase = 0.0_0085    ,		lowerCamelCase = 0.012    ,		lowerCamelCase = "linear"    ,		lowerCamelCase = None    ,		lowerCamelCase = "epsilon"    ,		lowerCamelCase = False    ,		lowerCamelCase = False    ,		lowerCamelCase = 1.0    ,		lowerCamelCase = "linspace"    ,		lowerCamelCase = 0    ,		):
        if trained_betas is not None:
               __a       =    torch.tensor(_lowerCAmelCase    ,		dtype=torch.floataa						)
        elif beta_schedule == "linear":
               __a       =    torch.linspace(_lowerCAmelCase    ,		_lowerCAmelCase    ,		_lowerCAmelCase    ,		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    ,		_lowerCAmelCase    ,		dtype=torch.floataa						) ** 2
               )
        elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               __a       =    betas_for_alpha_bar(_lowerCAmelCase    ,		alpha_transform_type="cosine"						)
        elif beta_schedule == "exp":
               __a       =    betas_for_alpha_bar(_lowerCAmelCase    ,		alpha_transform_type="exp"						)
        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						)

        #  set all values
        self.set_timesteps(_lowerCAmelCase    ,		_lowerCAmelCase    ,		_lowerCAmelCase						)
        __a       =    use_karras_sigmas


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=None						):
        if schedule_timesteps is None:
               __a       =    self.timesteps

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

        return indices[pos].item()


 @property
 def 						a__   ( self						):
        # 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 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		):
        __a       =    self.index_for_timestep(_lowerCAmelCase						)

        __a       =    self.sigmas[step_index]
        __a       =    sample / ((sigma**2 + 1) ** 0.5)
        return sample


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		):
        __a       =    num_inference_steps

        __a       =    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":
               __a       =    np.linspace(0    ,		num_train_timesteps - 1    ,		_lowerCAmelCase    ,		dtype=_lowerCAmelCase						)[::-1].copy()
        elif self.config.timestep_spacing == "leading":
               __a       =    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    ,		_lowerCAmelCase						) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase						)
               timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
               __a       =    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(_lowerCAmelCase    ,		0    ,		-step_ratio						)).round().copy().astype(_lowerCAmelCase						)
               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(_lowerCAmelCase						)
        __a       =    np.interp(_lowerCAmelCase    ,		np.arange(0    ,		len(_lowerCAmelCase						)						)    ,		_lowerCAmelCase						)

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

        __a       =    np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
        __a       =    torch.from_numpy(_lowerCAmelCase						).to(device=_lowerCAmelCase						)
        __a       =    torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2						), sigmas[-1:]]						)

        __a       =    torch.from_numpy(_lowerCAmelCase						)
        __a       =    torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2						)]						)

        if str(_lowerCAmelCase						).startswith("mps"						):
               # mps does not support float64
               __a       =    timesteps.to(_lowerCAmelCase    ,		dtype=torch.floataa						)
        else:
               __a       =    timesteps.to(device=_lowerCAmelCase						)

        # empty dt and derivative
        __a       =    None
        __a       =    None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        __a       =    defaultdict(_lowerCAmelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        # get log sigma
        __a       =    np.log(_lowerCAmelCase						)

        # 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(_lowerCAmelCase    ,		0    ,		1						)

        # transform interpolation to time range
        __a       =    (1 - w) * low_idx + w * high_idx
        __a       =    t.reshape(sigma.shape						)
        return t


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        __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    ,		_lowerCAmelCase						)
        __a       =    sigma_min ** (1 / rho)
        __a       =    sigma_max ** (1 / rho)
        __a       =    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas


 @property
 def 						a__   ( self						):
        return self.dt is None


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = True    ,		):
        __a       =    self.index_for_timestep(_lowerCAmelCase						)

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

        if self.state_in_first_order:
               __a       =    self.sigmas[step_index]
               __a       =    self.sigmas[step_index + 1]
        else:
               # 2nd order / Heun's method
               __a       =    self.sigmas[step_index - 1]
               __a       =    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
        __a       =    0
        __a       =    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":
               __a       =    sigma_hat if self.state_in_first_order else sigma_next
               __a       =    sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
               __a       =    sigma_hat if self.state_in_first_order else sigma_next
               __a       =    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                   sample / (sigma_input**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`"						)

        if self.config.clip_sample:
               __a       =    pred_original_sample.clamp(
                   -self.config.clip_sample_range    ,		self.config.clip_sample_range						)

        if self.state_in_first_order:
               # 2. Convert to an ODE derivative for 1st order
               __a       =    (sample - pred_original_sample) / sigma_hat
               # 3. delta timestep
               __a       =    sigma_next - sigma_hat

               # store for 2nd order step
               __a       =    derivative
               __a       =    dt
               __a       =    sample
        else:
               # 2. 2nd order / Heun's method
               __a       =    (sample - pred_original_sample) / sigma_next
               __a       =    (self.prev_derivative + derivative) / 2

               # 3. take prev timestep & sample
               __a       =    self.dt
               __a       =    self.sample

               # free dt and derivative
               # Note, this puts the scheduler in "first order mode"
               __a       =    None
               __a       =    None
               __a       =    None

        __a       =    sample + derivative * dt

        if not return_dict:
               return (prev_sample,)

        return SchedulerOutput(prev_sample=_lowerCAmelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		):
        # 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(_lowerCAmelCase						):
               # 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       =    [self.index_for_timestep(_lowerCAmelCase    ,		_lowerCAmelCase						) 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						):
        return self.config.num_train_timesteps







'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

class  lowerCAmelCase_		(		__magic_name__		):




 def __init__(						self      ,  *_lowerCAmelCase      ,  **_lowerCAmelCase		)     ->				None:
     warnings.warn(
         "The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
         " use YolosImageProcessor instead."      ,  _lowerCAmelCase      ,  )
     super().__init__(*_lowerCAmelCase      ,  **_lowerCAmelCase		)


