

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     __lowercase							(					UpperCAmelCase__      ):




			"""simple docstring"""







			_UpperCAmelCase  :				Optional[Any]     				=	42
			_UpperCAmelCase  :				Any     				=	None







def      A_ (       _UpperCAmelCase							,      _UpperCAmelCase=0.9_9_9							,      _UpperCAmelCase="cosine"							,      ):





						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_UpperCAmelCase       ):
																		return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2       ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(_UpperCAmelCase       ):
																		return math.exp(t * -1_2.0       )

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

						SCREAMING_SNAKE_CASE_:					Dict    						=			[]
						for i in range(_A       ):
												SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			i / num_diffusion_timesteps
												SCREAMING_SNAKE_CASE_:					str    						=			(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(_A       ) / alpha_bar_fn(_A       )							,      _A       )       )
						return torch.tensor(_A							,      dtype=torch.floataa       )



class     __lowercase							(					UpperCAmelCase__						,			UpperCAmelCase__      ):




			"""simple docstring"""







			@register_to_config
			def __init__(					self    :	str  ,		lowerCAmelCase__    :	int = 1000  ,		lowerCAmelCase__    :	str = "fixed_small_log"  ,		lowerCAmelCase__    :	bool = True  ,		lowerCAmelCase__    :	Optional[float] = 1.0  ,		lowerCAmelCase__    :	str = "epsilon"  ,		lowerCAmelCase__    :	str = "squaredcos_cap_v2"  ,		):






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

									SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			betas_for_alpha_bar(__lowercase)

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

									# standard deviation of the initial noise distribution
									SCREAMING_SNAKE_CASE_:					str    						=			1.0

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

									SCREAMING_SNAKE_CASE_:					Optional[int]    						=			variance_type



			def 			_SCREAMING_SNAKE_CASE     (					self    :	Optional[Any]  ,		lowerCAmelCase__    :	torch.FloatTensor  ,		lowerCAmelCase__    :	Optional[int] = None):
									return sample



			def 			_SCREAMING_SNAKE_CASE     (					self    :	Dict  ,		lowerCAmelCase__    :	int  ,		lowerCAmelCase__    :	Union[str, torch.device] = None):
									SCREAMING_SNAKE_CASE_:					List[Any]    						=			num_inference_steps
									SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
									SCREAMING_SNAKE_CASE_:					int    						=			(np.arange(0  ,		__lowercase) * step_ratio).round()[::-1].copy().astype(np.intaa)
									SCREAMING_SNAKE_CASE_:					Tuple    						=			torch.from_numpy(__lowercase).to(__lowercase)



			def 			_SCREAMING_SNAKE_CASE     (					self    :	Union[str, Any]  ,		lowerCAmelCase__    :	str  ,		lowerCAmelCase__    :	Optional[int]=None  ,		lowerCAmelCase__    :	Tuple=None  ,		lowerCAmelCase__    :	str=None):






									if prev_timestep is None:
															SCREAMING_SNAKE_CASE_:					str    						=			t - 1

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

									if prev_timestep == t - 1:
															SCREAMING_SNAKE_CASE_:					Optional[int]    						=			self.betas[t]
									else:
															SCREAMING_SNAKE_CASE_:					int    						=			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
									SCREAMING_SNAKE_CASE_:					str    						=			beta_prod_t_prev / beta_prod_t * beta

									if variance_type is None:
															SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			self.config.variance_type

									# hacks - were probably added for training stability
									if variance_type == "fixed_small_log":
															SCREAMING_SNAKE_CASE_:					Tuple    						=			torch.log(torch.clamp(__lowercase  ,		min=1E-20))
															SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			torch.exp(0.5 * variance)
									elif variance_type == "learned_range":
															# NOTE difference with DDPM scheduler
															SCREAMING_SNAKE_CASE_:					Optional[int]    						=			variance.log()
															SCREAMING_SNAKE_CASE_:					Optional[int]    						=			beta.log()

															SCREAMING_SNAKE_CASE_:					Any    						=			(predicted_variance + 1) / 2
															SCREAMING_SNAKE_CASE_:					str    						=			frac * max_log + (1 - frac) * min_log

									return variance



			def 			_SCREAMING_SNAKE_CASE     (					self    :	Union[str, Any]  ,		lowerCAmelCase__    :	torch.FloatTensor  ,		lowerCAmelCase__    :	int  ,		lowerCAmelCase__    :	torch.FloatTensor  ,		lowerCAmelCase__    :	Optional[int] = None  ,		lowerCAmelCase__    :	Tuple=None  ,		lowerCAmelCase__    :	bool = True  ,		):
									SCREAMING_SNAKE_CASE_:					Tuple    						=			timestep

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

									# 1. compute alphas, betas
									if prev_timestep is None:
															SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			t - 1

									SCREAMING_SNAKE_CASE_:					List[Any]    						=			self.alphas_cumprod[t]
									SCREAMING_SNAKE_CASE_:					Tuple    						=			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
									SCREAMING_SNAKE_CASE_:					List[Any]    						=			1 - alpha_prod_t
									SCREAMING_SNAKE_CASE_:					Dict    						=			1 - alpha_prod_t_prev

									if prev_timestep == t - 1:
															SCREAMING_SNAKE_CASE_:					str    						=			self.betas[t]
															SCREAMING_SNAKE_CASE_:					Dict    						=			self.alphas[t]
									else:
															SCREAMING_SNAKE_CASE_:					str    						=			1 - alpha_prod_t / alpha_prod_t_prev
															SCREAMING_SNAKE_CASE_:					str    						=			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":
															SCREAMING_SNAKE_CASE_:					Any    						=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									elif self.config.prediction_type == "sample":
															SCREAMING_SNAKE_CASE_:					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:
															SCREAMING_SNAKE_CASE_:					int    						=			torch.clamp(
															    __lowercase  ,		-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
									SCREAMING_SNAKE_CASE_:					int    						=			(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
									SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			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
									SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

									# 6. Add noise
									SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			0
									if t > 0:
															SCREAMING_SNAKE_CASE_:					Optional[int]    						=			randn_tensor(
															    model_output.shape  ,		dtype=model_output.dtype  ,		generator=__lowercase  ,		device=model_output.device)

															SCREAMING_SNAKE_CASE_:					Tuple    						=			self._get_variance(
															    __lowercase  ,		predicted_variance=__lowercase  ,		prev_timestep=__lowercase  ,		)

															if self.variance_type == "fixed_small_log":
																					SCREAMING_SNAKE_CASE_:					List[str]    						=			variance
															elif self.variance_type == "learned_range":
																					SCREAMING_SNAKE_CASE_:					List[str]    						=			(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.")

															SCREAMING_SNAKE_CASE_:					Dict    						=			variance * variance_noise

									SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			pred_prev_sample + variance

									if not return_dict:
															return (pred_prev_sample,)

									return UnCLIPSchedulerOutput(prev_sample=__lowercase  ,		pred_original_sample=__lowercase)




			def 			_SCREAMING_SNAKE_CASE     (					self    :	List[Any]  ,		lowerCAmelCase__    :	torch.FloatTensor  ,		lowerCAmelCase__    :	torch.FloatTensor  ,		lowerCAmelCase__    :	torch.IntTensor  ,		):
									SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			self.alphas_cumprod.to(device=original_samples.device  ,		dtype=original_samples.dtype)
									SCREAMING_SNAKE_CASE_:					str    						=			timesteps.to(original_samples.device)

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

									SCREAMING_SNAKE_CASE_:					List[str]    						=			(1 - alphas_cumprod[timesteps]) ** 0.5
									SCREAMING_SNAKE_CASE_:					Tuple    						=			sqrt_one_minus_alpha_prod.flatten()
									while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
															SCREAMING_SNAKE_CASE_:					Dict    						=			sqrt_one_minus_alpha_prod.unsqueeze(-1)

									SCREAMING_SNAKE_CASE_:					str    						=			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
									return noisy_samples












from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowercase__							:	Union[str, Any]											=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Tuple											=		["DPTFeatureExtractor"]
		lowercase__							:	Optional[Any]											=		["DPTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Dict											=		[
		    "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "DPTForDepthEstimation",
		    "DPTForSemanticSegmentation",
		    "DPTModel",
		    "DPTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_dpt import DPTFeatureExtractor
				from .image_processing_dpt import DPTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dpt import (
				    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DPTForDepthEstimation,
				    DPTForSemanticSegmentation,
				    DPTModel,
				    DPTPreTrainedModel,
				)


else:
		import sys

		lowercase__							:	List[Any]											=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
