




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 		a_							( __lowerCAmelCase   , __lowerCAmelCase=0.999   , __lowerCAmelCase="cosine"   , ):
  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

  lowerCAmelCase__										=      []
  for i in range(__lowerCAmelCase				):
    lowerCAmelCase__										=      i / num_diffusion_timesteps
    lowerCAmelCase__										=      (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     SCREAMING_SNAKE_CASE__		(_a    ,					_a			):
     lowercase_ :      List[str]				= [e.name for e in KarrasDiffusionSchedulers]
     lowercase_ :      Optional[Any]				= 2
     @register_to_config
     def __init__(						self	:		Optional[Any]       ,						__lowerCamelCase	:		int = 10_00       ,						__lowerCamelCase	:		float = 0.0_0085       ,						__lowerCamelCase	:		float = 0.012       ,						__lowerCamelCase	:		str = "linear"       ,						__lowerCamelCase	:		Optional[Union[np.ndarray, List[float]]] = None       ,						__lowerCamelCase	:		str = "epsilon"       ,						__lowerCamelCase	:		Optional[bool] = False       ,						__lowerCamelCase	:		Optional[bool] = False       ,						__lowerCamelCase	:		float = 1.0       ,						__lowerCamelCase	:		str = "linspace"       ,						__lowerCamelCase	:		int = 0       ,						):



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

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

       #  set all values
       self.set_timesteps(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase						)
       lowerCAmelCase__										=      use_karras_sigmas
     def        A__      (						self	:		Optional[Any]       ,						__lowerCamelCase	:		Any       ,						__lowerCamelCase	:		Optional[int]=None						):



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

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

       return indices[pos].item()
     @property
     def        A__      (						self	:		Dict						):



       """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        A__      (						self	:		Optional[Any]       ,						__lowerCamelCase	:		torch.FloatTensor       ,						__lowerCamelCase	:		Union[float, torch.FloatTensor]       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      self.index_for_timestep(__lowerCamelCase						)

       lowerCAmelCase__										=      self.sigmas[step_index]
       lowerCAmelCase__										=      sample / ((sigma**2 + 1) ** 0.5)
       return sample
     def        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		Union[str, torch.device] = None       ,						__lowerCamelCase	:		Optional[int] = None       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      num_inference_steps

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

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

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

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

       lowerCAmelCase__										=      torch.from_numpy(__lowerCamelCase						)
       lowerCAmelCase__										=      torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2						)]						)

       if str(__lowerCamelCase						).startswith('''mps'''						):
         # mps does not support float64
         lowerCAmelCase__										=      timesteps.to(__lowerCamelCase       ,						dtype=torch.floataa						)
       else:
         lowerCAmelCase__										=      timesteps.to(device=__lowerCamelCase						)

       # empty dt and derivative
       lowerCAmelCase__										=      None
       lowerCAmelCase__										=      None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       lowerCAmelCase__										=      defaultdict(__lowerCamelCase						)
     def        A__      (						self	:		List[str]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		Optional[Any]						):



       """simple docstring"""
       # get log sigma
       lowerCAmelCase__										=      np.log(__lowerCamelCase						)

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

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

       lowerCAmelCase__										=      log_sigmas[low_idx]
       lowerCAmelCase__										=      log_sigmas[high_idx]

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

       # transform interpolation to time range
       lowerCAmelCase__										=      (1 - w) * low_idx + w * high_idx
       lowerCAmelCase__										=      t.reshape(sigma.shape						)
       return t
     def        A__      (						self	:		str       ,						__lowerCamelCase	:		torch.FloatTensor       ,						__lowerCamelCase	:		List[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      in_sigmas[-1].item()
       lowerCAmelCase__										=      in_sigmas[0].item()

       lowerCAmelCase__										=      7.0  # 7.0 is the value used in the paper
       lowerCAmelCase__										=      np.linspace(0       ,						1       ,						__lowerCamelCase						)
       lowerCAmelCase__										=      sigma_min ** (1 / rho)
       lowerCAmelCase__										=      sigma_max ** (1 / rho)
       lowerCAmelCase__										=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas
     @property
     def        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       return self.dt is None
     def        A__      (						self	:		Dict       ,						__lowerCamelCase	:		Union[torch.FloatTensor, np.ndarray]       ,						__lowerCamelCase	:		Union[float, torch.FloatTensor]       ,						__lowerCamelCase	:		Union[torch.FloatTensor, np.ndarray]       ,						__lowerCamelCase	:		bool = True       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      self.index_for_timestep(__lowerCamelCase						)

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

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

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

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

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

       lowerCAmelCase__										=      sample + derivative * dt

       if not return_dict:
         return (prev_sample,)

       return SchedulerOutput(prev_sample=__lowerCamelCase						)
     def        A__      (						self	:		List[Any]       ,						__lowerCamelCase	:		torch.FloatTensor       ,						__lowerCamelCase	:		torch.FloatTensor       ,						__lowerCamelCase	:		torch.FloatTensor       ,						):



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

       lowerCAmelCase__										=      [self.index_for_timestep(__lowerCamelCase       ,						__lowerCamelCase						) for t in timesteps]

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

       lowerCAmelCase__										=      original_samples + noise * sigma
       return noisy_samples



     def __len__(						self	:		Tuple						):



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







import logging

from transformers.configuration_utils import PretrainedConfig


__magic_name__  :  Tuple					=       logging.getLogger(__name__)





class     SCREAMING_SNAKE_CASE__		(_a			):
     lowercase_ :      Tuple				= "masked_bert"
     def __init__(						self	:		List[Any]       ,						__lowerCamelCase	:		Optional[Any]=3_05_22       ,						__lowerCamelCase	:		Dict=7_68       ,						__lowerCamelCase	:		Tuple=12       ,						__lowerCamelCase	:		int=12       ,						__lowerCamelCase	:		Tuple=30_72       ,						__lowerCamelCase	:		List[Any]="gelu"       ,						__lowerCamelCase	:		Union[str, Any]=0.1       ,						__lowerCamelCase	:		Dict=0.1       ,						__lowerCamelCase	:		Optional[int]=5_12       ,						__lowerCamelCase	:		List[Any]=2       ,						__lowerCamelCase	:		Union[str, Any]=0.02       ,						__lowerCamelCase	:		Optional[int]=1e-12       ,						__lowerCamelCase	:		Optional[int]=0       ,						__lowerCamelCase	:		List[str]="topK"       ,						__lowerCamelCase	:		Union[str, Any]="constant"       ,						__lowerCamelCase	:		Union[str, Any]=0.0       ,						**__lowerCamelCase	:		Tuple       ,						):



       """simple docstring"""
       super().__init__(pad_token_id=__lowerCamelCase       ,						**__lowerCamelCase						)

       lowerCAmelCase__										=      vocab_size
       lowerCAmelCase__										=      hidden_size
       lowerCAmelCase__										=      num_hidden_layers
       lowerCAmelCase__										=      num_attention_heads
       lowerCAmelCase__										=      hidden_act
       lowerCAmelCase__										=      intermediate_size
       lowerCAmelCase__										=      hidden_dropout_prob
       lowerCAmelCase__										=      attention_probs_dropout_prob
       lowerCAmelCase__										=      max_position_embeddings
       lowerCAmelCase__										=      type_vocab_size
       lowerCAmelCase__										=      initializer_range
       lowerCAmelCase__										=      layer_norm_eps
       lowerCAmelCase__										=      pruning_method
       lowerCAmelCase__										=      mask_init
       lowerCAmelCase__										=      mask_scale

