



"""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     _lowercase       (						__snake_case      ,__snake_case=0.999      ,__snake_case="cosine"      ,)		->     Union[str, Any]:
			if alpha_transform_type == "cosine":

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

			elif alpha_transform_type == "exp":

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

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

			__lowerCAmelCase   :						Optional[int]				= []
			for i in range(__snake_case			):
						__lowerCAmelCase   :						List[str]				= i / num_diffusion_timesteps
						__lowerCAmelCase   :						Optional[int]				= (i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(__snake_case			) / alpha_bar_fn(__snake_case			)      ,__snake_case			)			)
			return torch.tensor(__snake_case      ,dtype=torch.floataa			)





class     A__     (						__SCREAMING_SNAKE_CASE						,	__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			[e.name for e in KarrasDiffusionSchedulers]
	SCREAMING_SNAKE_CASE					   =			2

	@register_to_config
	def __init__(  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	int = 1000 ,    _SCREAMING_SNAKE_CASE:	float = 0.0_0085 ,    _SCREAMING_SNAKE_CASE:	float = 0.012 ,    _SCREAMING_SNAKE_CASE:	str = "linear" ,    _SCREAMING_SNAKE_CASE:	Optional[Union[np.ndarray, List[float]]] = None ,    _SCREAMING_SNAKE_CASE:	str = "epsilon" ,    _SCREAMING_SNAKE_CASE:	Optional[bool] = False ,    _SCREAMING_SNAKE_CASE:	Optional[bool] = False ,    _SCREAMING_SNAKE_CASE:	float = 1.0 ,    _SCREAMING_SNAKE_CASE:	str = "linspace" ,    _SCREAMING_SNAKE_CASE:	int = 0 ,    )       -> Tuple:

				"""simple docstring"""

				if trained_betas is not None:
							__lowerCAmelCase   :						List[Any]				= torch.tensor(_SCREAMING_SNAKE_CASE ,    dtype=torch.floataa)
				elif beta_schedule == "linear":
							__lowerCAmelCase   :						Tuple				= torch.linspace(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    dtype=torch.floataa)
				elif beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							__lowerCAmelCase   :						str				= (
							    torch.linspace(beta_start**0.5 ,    beta_end**0.5 ,    _SCREAMING_SNAKE_CASE ,    dtype=torch.floataa) ** 2
							)
				elif beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							__lowerCAmelCase   :						Union[str, Any]				= betas_for_alpha_bar(_SCREAMING_SNAKE_CASE ,    alpha_transform_type="cosine")
				elif beta_schedule == "exp":
							__lowerCAmelCase   :						Dict				= betas_for_alpha_bar(_SCREAMING_SNAKE_CASE ,    alpha_transform_type="exp")
				else:
							raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

				__lowerCAmelCase   :						str				= 1.0 - self.betas
				__lowerCAmelCase   :						List[str]				= torch.cumprod(self.alphas ,    dim=0)

				#  set all values
				self.set_timesteps(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= use_karras_sigmas

	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	Optional[int]=None)       -> Any:

				"""simple docstring"""

				if schedule_timesteps is None:
							__lowerCAmelCase   :						str				= self.timesteps

				__lowerCAmelCase   :						List[Any]				= (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   :						Optional[int]				= 1 if len(_SCREAMING_SNAKE_CASE) > 1 else 0
				else:
							__lowerCAmelCase   :						List[Any]				= timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE) else timestep
							__lowerCAmelCase   :						Any				= self._index_counter[timestep_int]

				return indices[pos].item()

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> Any:

				"""simple docstring"""

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

				return (self.sigmas.max() ** 2 + 1) ** 0.5

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any ,    _SCREAMING_SNAKE_CASE:	torch.FloatTensor ,    _SCREAMING_SNAKE_CASE:	Union[float, torch.FloatTensor] ,    )       -> torch.FloatTensor:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= self.index_for_timestep(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[Any]				= self.sigmas[step_index]
				__lowerCAmelCase   :						int				= sample / ((sigma**2 + 1) ** 0.5)
				return sample

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	Union[str, torch.device] = None ,    _SCREAMING_SNAKE_CASE:	Optional[int] = None ,    )       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= num_inference_steps

				__lowerCAmelCase   :						Dict				= 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   :						int				= np.linspace(0 ,    num_train_timesteps - 1 ,    _SCREAMING_SNAKE_CASE ,    dtype=_SCREAMING_SNAKE_CASE)[::-1].copy()
				elif self.config.timestep_spacing == "leading":
							__lowerCAmelCase   :						str				= 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   :						Dict				= (np.arange(0 ,    _SCREAMING_SNAKE_CASE) * step_ratio).round()[::-1].copy().astype(_SCREAMING_SNAKE_CASE)
							timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
							__lowerCAmelCase   :						List[str]				= 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   :						Dict				= (np.arange(_SCREAMING_SNAKE_CASE ,    0 ,    -step_ratio)).round().copy().astype(_SCREAMING_SNAKE_CASE)
							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   :						List[str]				= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
				__lowerCAmelCase   :						Any				= np.log(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						List[Any]				= np.interp(_SCREAMING_SNAKE_CASE ,    np.arange(0 ,    len(_SCREAMING_SNAKE_CASE)) ,    _SCREAMING_SNAKE_CASE)

				if self.config.use_karras_sigmas:
							__lowerCAmelCase   :						int				= self._convert_to_karras(in_sigmas=_SCREAMING_SNAKE_CASE ,    num_inference_steps=self.num_inference_steps)
							__lowerCAmelCase   :						List[Any]				= np.array([self._sigma_to_t(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE) for sigma in sigmas])

				__lowerCAmelCase   :						List[Any]				= np.concatenate([sigmas, [0.0]]).astype(np.floataa)
				__lowerCAmelCase   :						Optional[Any]				= torch.from_numpy(_SCREAMING_SNAKE_CASE).to(device=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Any				= torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2), sigmas[-1:]])

				__lowerCAmelCase   :						Optional[Any]				= torch.from_numpy(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Tuple				= torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2)])

				if str(_SCREAMING_SNAKE_CASE).startswith("mps"):
							# mps does not support float64
							__lowerCAmelCase   :						Union[str, Any]				= timesteps.to(_SCREAMING_SNAKE_CASE ,    dtype=torch.floataa)
				else:
							__lowerCAmelCase   :						Union[str, Any]				= timesteps.to(device=_SCREAMING_SNAKE_CASE)

				# empty dt and derivative
				__lowerCAmelCase   :						Dict				= None
				__lowerCAmelCase   :						Dict				= None

				# for exp beta schedules, such as the one for `pipeline_shap_e.py`
				# we need an index counter
				__lowerCAmelCase   :						List[Any]				= defaultdict(_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	int ,    _SCREAMING_SNAKE_CASE:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Optional[int])       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= np.log(_SCREAMING_SNAKE_CASE)

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

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

				__lowerCAmelCase   :						str				= log_sigmas[low_idx]
				__lowerCAmelCase   :						Tuple				= log_sigmas[high_idx]

				# interpolate sigmas
				__lowerCAmelCase   :						Optional[int]				= (low - log_sigma) / (low - high)
				__lowerCAmelCase   :						int				= np.clip(_SCREAMING_SNAKE_CASE ,    0 ,    1)

				# transform interpolation to time range
				__lowerCAmelCase   :						Dict				= (1 - w) * low_idx + w * high_idx
				__lowerCAmelCase   :						List[str]				= t.reshape(sigma.shape)
				return t

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	torch.FloatTensor ,    _SCREAMING_SNAKE_CASE:	Any)       -> torch.FloatTensor:

				"""simple docstring"""

				__lowerCAmelCase   :						float				= in_sigmas[-1].item()
				__lowerCAmelCase   :						float				= in_sigmas[0].item()

				__lowerCAmelCase   :						str				= 7.0  # 7.0 is the value used in the paper
				__lowerCAmelCase   :						Union[str, Any]				= np.linspace(0 ,    1 ,    _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						List[str]				= sigma_min ** (1 / rho)
				__lowerCAmelCase   :						List[str]				= sigma_max ** (1 / rho)
				__lowerCAmelCase   :						str				= (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
				return sigmas

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Tuple:

				"""simple docstring"""

				return self.dt is None

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	Union[torch.FloatTensor, np.ndarray] ,    _SCREAMING_SNAKE_CASE:	Union[float, torch.FloatTensor] ,    _SCREAMING_SNAKE_CASE:	Union[torch.FloatTensor, np.ndarray] ,    _SCREAMING_SNAKE_CASE:	bool = True ,    )       -> Union[SchedulerOutput, Tuple]:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= self.index_for_timestep(_SCREAMING_SNAKE_CASE)

				# advance index counter by 1
				__lowerCAmelCase   :						Tuple				= timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE) else timestep
				self._index_counter[timestep_int] += 1

				if self.state_in_first_order:
							__lowerCAmelCase   :						Tuple				= self.sigmas[step_index]
							__lowerCAmelCase   :						Optional[int]				= self.sigmas[step_index + 1]
				else:
							# 2nd order / Heun's method
							__lowerCAmelCase   :						List[Any]				= self.sigmas[step_index - 1]
							__lowerCAmelCase   :						Union[str, Any]				= 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   :						List[Any]				= 0
				__lowerCAmelCase   :						str				= 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   :						str				= sigma_hat if self.state_in_first_order else sigma_next
							__lowerCAmelCase   :						Dict				= sample - sigma_input * model_output
				elif self.config.prediction_type == "v_prediction":
							__lowerCAmelCase   :						List[str]				= sigma_hat if self.state_in_first_order else sigma_next
							__lowerCAmelCase   :						Dict				= model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
							    sample / (sigma_input**2 + 1)
							)
				elif self.config.prediction_type == "sample":
							__lowerCAmelCase   :						Optional[int]				= 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   :						Tuple				= 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   :						Optional[Any]				= (sample - pred_original_sample) / sigma_hat
							# 3. delta timestep
							__lowerCAmelCase   :						int				= sigma_next - sigma_hat

							# store for 2nd order step
							__lowerCAmelCase   :						str				= derivative
							__lowerCAmelCase   :						str				= dt
							__lowerCAmelCase   :						Optional[int]				= sample
				else:
							# 2. 2nd order / Heun's method
							__lowerCAmelCase   :						List[Any]				= (sample - pred_original_sample) / sigma_next
							__lowerCAmelCase   :						Optional[Any]				= (self.prev_derivative + derivative) / 2

							# 3. take prev timestep & sample
							__lowerCAmelCase   :						str				= self.dt
							__lowerCAmelCase   :						List[str]				= self.sample

							# free dt and derivative
							# Note, this puts the scheduler in "first order mode"
							__lowerCAmelCase   :						List[Any]				= None
							__lowerCAmelCase   :						str				= None
							__lowerCAmelCase   :						Union[str, Any]				= None

				__lowerCAmelCase   :						Union[str, Any]				= sample + derivative * dt

				if not return_dict:
							return (prev_sample,)

				return SchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    _SCREAMING_SNAKE_CASE:	torch.FloatTensor ,    _SCREAMING_SNAKE_CASE:	torch.FloatTensor ,    _SCREAMING_SNAKE_CASE:	torch.FloatTensor ,    )       -> torch.FloatTensor:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= self.sigmas.to(device=original_samples.device ,    dtype=original_samples.dtype)
				if original_samples.device.type == "mps" and torch.is_floating_point(_SCREAMING_SNAKE_CASE):
							# mps does not support float64
							__lowerCAmelCase   :						int				= self.timesteps.to(original_samples.device ,    dtype=torch.floataa)
							__lowerCAmelCase   :						Tuple				= timesteps.to(original_samples.device ,    dtype=torch.floataa)
				else:
							__lowerCAmelCase   :						Dict				= self.timesteps.to(original_samples.device)
							__lowerCAmelCase   :						str				= timesteps.to(original_samples.device)

				__lowerCAmelCase   :						Union[str, Any]				= [self.index_for_timestep(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE) for t in timesteps]

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

				__lowerCAmelCase   :						Union[str, Any]				= original_samples + noise * sigma
				return noisy_samples







	def __len__(  self:	str)       -> str:

				"""simple docstring"""

				return self.config.num_train_timesteps




"""simple docstring"""






import logging

from transformers.configuration_utils import PretrainedConfig


__snake_case							:     Tuple   =					logging.getLogger(__name__)



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'masked_bert'

	def __init__(  self:	str ,    _SCREAMING_SNAKE_CASE:	Tuple=3_0522 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=768 ,    _SCREAMING_SNAKE_CASE:	List[str]=12 ,    _SCREAMING_SNAKE_CASE:	Any=12 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=3072 ,    _SCREAMING_SNAKE_CASE:	List[str]="gelu" ,    _SCREAMING_SNAKE_CASE:	List[str]=0.1 ,    _SCREAMING_SNAKE_CASE:	str=0.1 ,    _SCREAMING_SNAKE_CASE:	Dict=512 ,    _SCREAMING_SNAKE_CASE:	List[Any]=2 ,    _SCREAMING_SNAKE_CASE:	Tuple=0.02 ,    _SCREAMING_SNAKE_CASE:	int=1e-12 ,    _SCREAMING_SNAKE_CASE:	int=0 ,    _SCREAMING_SNAKE_CASE:	Tuple="topK" ,    _SCREAMING_SNAKE_CASE:	Any="constant" ,    _SCREAMING_SNAKE_CASE:	int=0.0 ,    **_SCREAMING_SNAKE_CASE:	Any ,    )       -> List[str]:

				"""simple docstring"""

				super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Union[str, Any]				= vocab_size
				__lowerCAmelCase   :						List[str]				= hidden_size
				__lowerCAmelCase   :						Any				= num_hidden_layers
				__lowerCAmelCase   :						Dict				= num_attention_heads
				__lowerCAmelCase   :						Union[str, Any]				= hidden_act
				__lowerCAmelCase   :						Optional[Any]				= intermediate_size
				__lowerCAmelCase   :						int				= hidden_dropout_prob
				__lowerCAmelCase   :						List[Any]				= attention_probs_dropout_prob
				__lowerCAmelCase   :						int				= max_position_embeddings
				__lowerCAmelCase   :						str				= type_vocab_size
				__lowerCAmelCase   :						Any				= initializer_range
				__lowerCAmelCase   :						Optional[int]				= layer_norm_eps
				__lowerCAmelCase   :						Tuple				= pruning_method
				__lowerCAmelCase   :						Union[str, Any]				= mask_init
				__lowerCAmelCase   :						str				= mask_scale