
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     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			42
     lowerCAmelCase_							   =			None






def 							a__					(							snake_case       ,			snake_case=0.999       ,			snake_case="cosine"       ,			):





  """simple docstring"""
  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}'''							)

  __SCREAMING_SNAKE_CASE      :	Any					=		[]
  for i in range(snake_case							):
    __SCREAMING_SNAKE_CASE      :	int					=		i / num_diffusion_timesteps
    __SCREAMING_SNAKE_CASE      :	List[str]					=		(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     __UpperCamelCase (							lowerCAmelCase__ ,						lowerCAmelCase__       ):






     """simple docstring"""







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







       """simple docstring"""




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

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		betas_for_alpha_bar(_A )

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

       # standard deviation of the initial noise distribution
       __SCREAMING_SNAKE_CASE      :	Tuple					=		1.0

       # setable values
       __SCREAMING_SNAKE_CASE      :	Any					=		None
       __SCREAMING_SNAKE_CASE      :	Any					=		torch.from_numpy(np.arange(0     ,						_A )[::-1].copy() )

       __SCREAMING_SNAKE_CASE      :	Tuple					=		variance_type



     def  UpperCAmelCase__							(				self							:							int     ,						_A							:							torch.FloatTensor     ,						_A							:							Optional[int] = None ):







       """simple docstring"""




       return sample



     def  UpperCAmelCase__							(				self							:							int     ,						_A							:							int     ,						_A							:							Union[str, torch.device] = None ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							List[str]     ,						_A							:							List[Any]     ,						_A							:							List[str]=None     ,						_A							:							List[Any]=None     ,						_A							:							List[str]=None ):







       """simple docstring"""




       if prev_timestep is None:
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		t - 1

       __SCREAMING_SNAKE_CASE      :	str					=		self.alphas_cumprod[t]
       __SCREAMING_SNAKE_CASE      :	Tuple					=		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      :	List[Any]					=		self.betas[t]
       else:
         __SCREAMING_SNAKE_CASE      :	List[str]					=		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      :	Dict					=		beta_prod_t_prev / beta_prod_t * beta

       if variance_type is None:
         __SCREAMING_SNAKE_CASE      :	Any					=		self.config.variance_type

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

         __SCREAMING_SNAKE_CASE      :	Dict					=		(predicted_variance + 1) / 2
         __SCREAMING_SNAKE_CASE      :	str					=		frac * max_log + (1 - frac) * min_log

       return variance



     def  UpperCAmelCase__							(				self							:							Dict     ,						_A							:							torch.FloatTensor     ,						_A							:							int     ,						_A							:							torch.FloatTensor     ,						_A							:							Optional[int] = None     ,						_A							:							List[str]=None     ,						_A							:							bool = True     ,						):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	int					=		timestep

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

       # 1. compute alphas, betas
       if prev_timestep is None:
         __SCREAMING_SNAKE_CASE      :	List[str]					=		t - 1

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

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

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

         __SCREAMING_SNAKE_CASE      :	Any					=		self._get_variance(
             _A     ,						predicted_variance=_A     ,						prev_timestep=_A     ,						)

         if self.variance_type == "fixed_small_log":
           __SCREAMING_SNAKE_CASE      :	List[Any]					=		variance
         elif self.variance_type == "learned_range":
           __SCREAMING_SNAKE_CASE      :	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      :	List[Any]					=		variance * variance_noise

       __SCREAMING_SNAKE_CASE      :	List[str]					=		pred_prev_sample + variance

       if not return_dict:
         return (pred_prev_sample,)

       return UnCLIPSchedulerOutput(prev_sample=_A     ,						pred_original_sample=_A )




     def  UpperCAmelCase__							(				self							:							str     ,						_A							:							torch.FloatTensor     ,						_A							:							torch.FloatTensor     ,						_A							:							torch.IntTensor     ,						):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		self.alphas_cumprod.to(device=original_samples.device     ,						dtype=original_samples.dtype )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		timesteps.to(original_samples.device )

       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		alphas_cumprod[timesteps] ** 0.5
       __SCREAMING_SNAKE_CASE      :	Dict					=		sqrt_alpha_prod.flatten()
       while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
         __SCREAMING_SNAKE_CASE      :	Any					=		sqrt_alpha_prod.unsqueeze(-1 )

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		(1 - alphas_cumprod[timesteps]) ** 0.5
       __SCREAMING_SNAKE_CASE      :	Any					=		sqrt_one_minus_alpha_prod.flatten()
       while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		sqrt_one_minus_alpha_prod.unsqueeze(-1 )

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__			:      str								={
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__			:      Optional[Any]								=[
				    'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'CLIPSegModel',
				    'CLIPSegPreTrainedModel',
				    'CLIPSegTextModel',
				    'CLIPSegVisionModel',
				    'CLIPSegForImageSegmentation',
				]

if TYPE_CHECKING:
				from .configuration_clipseg import (
				    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    CLIPSegConfig,
				    CLIPSegTextConfig,
				    CLIPSegVisionConfig,
				)
				from .processing_clipseg import CLIPSegProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_clipseg import (
								    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CLIPSegForImageSegmentation,
								    CLIPSegModel,
								    CLIPSegPreTrainedModel,
								    CLIPSegTextModel,
								    CLIPSegVisionModel,
								)

else:
				import sys

				lowerCAmelCase__			:      str								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





