
'''simple docstring'''







import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					4_2
					lowerCAmelCase_					       =					None







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Any				,				__SCREAMING_SNAKE_CASE :			int=0.999				,				__SCREAMING_SNAKE_CASE :			List[Any]="cosine"				,				):



						"""simple docstring"""





						if alpha_transform_type == "cosine":

												def alpha_bar_fn(__SCREAMING_SNAKE_CASE :			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 :			Optional[int]							):
																		return math.exp(t * -12.0							)

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

						lowercase_     :					str													=	[]
						for i in range(__SCREAMING_SNAKE_CASE							):
												lowercase_     :					str													=	i / num_diffusion_timesteps
												lowercase_     :					Union[str, Any]													=	(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 			lowerCAmelCase__       (  lowerCamelCase_   ,						lowerCamelCase_					):






					@register_to_config
					def __init__(       self    ,							__SCREAMING_SNAKE_CASE = 10_00    ,							__SCREAMING_SNAKE_CASE = "fixed_small_log"    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = 1.0    ,							__SCREAMING_SNAKE_CASE = "epsilon"    ,							__SCREAMING_SNAKE_CASE = "squaredcos_cap_v2"    ,							):


											"""simple docstring"""





											if beta_schedule != "squaredcos_cap_v2":
																	raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''		)

											lowercase_     :					Tuple													=	betas_for_alpha_bar(__SCREAMING_SNAKE_CASE		)

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

											# standard deviation of the initial noise distribution
											lowercase_     :					int													=	1.0

											# setable values
											lowercase_     :					Optional[int]													=	None
											lowercase_     :					Tuple													=	torch.from_numpy(np.arange(0    ,							__SCREAMING_SNAKE_CASE		)[::-1].copy()		)

											lowercase_     :					Union[str, Any]													=	variance_type






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											return sample






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	num_inference_steps
											lowercase_     :					Union[str, Any]													=	(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
											lowercase_     :					Union[str, Any]													=	(np.arange(0    ,							__SCREAMING_SNAKE_CASE		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
											lowercase_     :					Any													=	torch.from_numpy(__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None		):


											"""simple docstring"""





											if prev_timestep is None:
																	lowercase_     :					Optional[Any]													=	t - 1

											lowercase_     :					str													=	self.alphas_cumprod[t]
											lowercase_     :					Dict													=	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
											lowercase_     :					Optional[Any]													=	1 - alpha_prod_t
											lowercase_     :					Optional[int]													=	1 - alpha_prod_t_prev

											if prev_timestep == t - 1:
																	lowercase_     :					int													=	self.betas[t]
											else:
																	lowercase_     :					Dict													=	1 - alpha_prod_t / alpha_prod_t_prev

											# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
											# and sample from it to get previous sample
											# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
											lowercase_     :					List[Any]													=	beta_prod_t_prev / beta_prod_t * beta

											if variance_type is None:
																	lowercase_     :					List[Any]													=	self.config.variance_type

											# hacks - were probably added for training stability
											if variance_type == "fixed_small_log":
																	lowercase_     :					Dict													=	torch.log(torch.clamp(__SCREAMING_SNAKE_CASE    ,							min=1E-2_0		)		)
																	lowercase_     :					Optional[Any]													=	torch.exp(0.5 * variance		)
											elif variance_type == "learned_range":
																	# NOTE difference with DDPM scheduler
																	lowercase_     :					Optional[Any]													=	variance.log()
																	lowercase_     :					List[Any]													=	beta.log()

																	lowercase_     :					int													=	(predicted_variance + 1) / 2
																	lowercase_     :					Dict													=	frac * max_log + (1 - frac) * min_log

											return variance






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE = True    ,							):


											"""simple docstring"""





											lowercase_     :					int													=	timestep

											if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
																	lowercase_     :					int													=	torch.split(__SCREAMING_SNAKE_CASE    ,							sample.shape[1]    ,							dim=1		)
											else:
																	lowercase_     :					str													=	None

											# 1. compute alphas, betas
											if prev_timestep is None:
																	lowercase_     :					Any													=	t - 1

											lowercase_     :					int													=	self.alphas_cumprod[t]
											lowercase_     :					Optional[Any]													=	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
											lowercase_     :					Tuple													=	1 - alpha_prod_t
											lowercase_     :					Optional[Any]													=	1 - alpha_prod_t_prev

											if prev_timestep == t - 1:
																	lowercase_     :					List[str]													=	self.betas[t]
																	lowercase_     :					Union[str, Any]													=	self.alphas[t]
											else:
																	lowercase_     :					List[Any]													=	1 - alpha_prod_t / alpha_prod_t_prev
																	lowercase_     :					int													=	1 - beta

											# 2. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
											if self.config.prediction_type == "epsilon":
																	lowercase_     :					Tuple													=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											elif self.config.prediction_type == "sample":
																	lowercase_     :					Any													=	model_output
											else:
																	raise ValueError(
																	    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
																	    ''' for the UnCLIPScheduler.'''		)

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
																	lowercase_     :					Any													=	torch.clamp(
																	    __SCREAMING_SNAKE_CASE    ,							-self.config.clip_sample_range    ,							self.config.clip_sample_range		)

											# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											lowercase_     :					int													=	(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
											lowercase_     :					Optional[int]													=	alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

											# 5. Compute predicted previous sample µ_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											lowercase_     :					Optional[Any]													=	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

											# 6. Add noise
											lowercase_     :					Union[str, Any]													=	0
											if t > 0:
																	lowercase_     :					List[Any]													=	randn_tensor(
																	    model_output.shape    ,							dtype=model_output.dtype    ,							generator=__SCREAMING_SNAKE_CASE    ,							device=model_output.device		)

																	lowercase_     :					Union[str, Any]													=	self._get_variance(
																	    __SCREAMING_SNAKE_CASE    ,							predicted_variance=__SCREAMING_SNAKE_CASE    ,							prev_timestep=__SCREAMING_SNAKE_CASE    ,							)

																	if self.variance_type == "fixed_small_log":
																							lowercase_     :					str													=	variance
																	elif self.variance_type == "learned_range":
																							lowercase_     :					Optional[Any]													=	(0.5 * variance).exp()
																	else:
																							raise ValueError(
																							    F'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
																							    ''' for the UnCLIPScheduler.'''		)

																	lowercase_     :					Dict													=	variance * variance_noise

											lowercase_     :					Tuple													=	pred_prev_sample + variance

											if not return_dict:
																	return (pred_prev_sample,)

											return UnCLIPSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE    ,							pred_original_sample=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											lowercase_     :					Dict													=	self.alphas_cumprod.to(device=original_samples.device    ,							dtype=original_samples.dtype		)
											lowercase_     :					Any													=	timesteps.to(original_samples.device		)

											lowercase_     :					str													=	alphas_cumprod[timesteps] ** 0.5
											lowercase_     :					Optional[int]													=	sqrt_alpha_prod.flatten()
											while len(sqrt_alpha_prod.shape		) < len(original_samples.shape		):
																	lowercase_     :					str													=	sqrt_alpha_prod.unsqueeze(-1		)

											lowercase_     :					str													=	(1 - alphas_cumprod[timesteps]) ** 0.5
											lowercase_     :					Optional[int]													=	sqrt_one_minus_alpha_prod.flatten()
											while len(sqrt_one_minus_alpha_prod.shape		) < len(original_samples.shape		):
																	lowercase_     :					Dict													=	sqrt_one_minus_alpha_prod.unsqueeze(-1		)

											lowercase_     :					Tuple													=	sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
											return noisy_samples






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase			:  Tuple   							=	{"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase			:  str   							=	[
							    "VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "ViTMSNModel",
							    "ViTMSNForImageClassification",
							    "ViTMSNPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_msn import (
														    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTMSNForImageClassification,
														    ViTMSNModel,
														    ViTMSNPreTrainedModel,
														)

else:
							import sys

							_lowercase			:  int   							=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



