
'''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 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						42
		lowerCamelCase     							=						None







def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=0.999 ,       SCREAMING_SNAKE_CASE__="cosine" ,       )  ->							str:


		'''simple docstring'''


		if alpha_transform_type == "cosine":

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

		elif alpha_transform_type == "exp":

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

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

		snake_case  :       int     			=						[]
		for i in range(SCREAMING_SNAKE_CASE__			):
				snake_case  :       Union[str, Any]     			=						i / num_diffusion_timesteps
				snake_case  :       Optional[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 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

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






				"""simple docstring"""







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

				snake_case  :       List[Any]     			=						betas_for_alpha_bar(UpperCamelCase__      )

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

				# standard deviation of the initial noise distribution
				snake_case  :       Optional[int]     			=						1.0

				# setable values
				snake_case  :       Any     			=						None
				snake_case  :       Union[str, Any]     			=						torch.from_numpy(np.arange(0  ,	UpperCamelCase__      )[::-1].copy()      )

				snake_case  :       Union[str, Any]     			=						variance_type



		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			torch.FloatTensor  ,	UpperCamelCase__				:			Optional[int] = None      )						->			torch.FloatTensor:






				"""simple docstring"""







				return sample



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






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			Any=None  ,	UpperCamelCase__				:			str=None  ,	UpperCamelCase__				:			List[str]=None      )						->			Union[str, Any]:






				"""simple docstring"""







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

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

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

				if variance_type is None:
						snake_case  :       Union[str, Any]     			=						self.config.variance_type

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

						snake_case  :       Optional[int]     			=						(predicted_variance + 1) / 2
						snake_case  :       Tuple     			=						frac * max_log + (1 - frac) * min_log

				return variance



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			torch.FloatTensor  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			torch.FloatTensor  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			int=None  ,	UpperCamelCase__				:			bool = True  ,	)						->			Union[UnCLIPSchedulerOutput, Tuple]:






				"""simple docstring"""







				snake_case  :       int     			=						timestep

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

				# 1. compute alphas, betas
				if prev_timestep is None:
						snake_case  :       Dict     			=						t - 1

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

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

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

						snake_case  :       List[Any]     			=						self._get_variance(
						    UpperCamelCase__  ,	predicted_variance=UpperCamelCase__  ,	prev_timestep=UpperCamelCase__  ,	)

						if self.variance_type == "fixed_small_log":
								snake_case  :       Optional[Any]     			=						variance
						elif self.variance_type == "learned_range":
								snake_case  :       Tuple     			=						(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.'''      )

						snake_case  :       List[Any]     			=						variance * variance_noise

				snake_case  :       Optional[Any]     			=						pred_prev_sample + variance

				if not return_dict:
						return (pred_prev_sample,)

				return UnCLIPSchedulerOutput(prev_sample=UpperCamelCase__  ,	pred_original_sample=UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			torch.FloatTensor  ,	UpperCamelCase__				:			torch.FloatTensor  ,	UpperCamelCase__				:			torch.IntTensor  ,	)						->			torch.FloatTensor:






				"""simple docstring"""







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

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

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

				snake_case  :       Any     			=						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, is_vision_available


lowercase__						       =      {
    "configuration_poolformer": [
        "POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "PoolFormerConfig",
        "PoolFormerOnnxConfig",
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["PoolFormerFeatureExtractor"]
						lowercase__						       =      ["PoolFormerImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      [
						    "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "PoolFormerForImageClassification",
						    "PoolFormerModel",
						    "PoolFormerPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_poolformer import (
						    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    PoolFormerConfig,
						    PoolFormerOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_poolformer import PoolFormerFeatureExtractor
												from .image_processing_poolformer import PoolFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_poolformer import (
												    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PoolFormerForImageClassification,
												    PoolFormerModel,
												    PoolFormerPreTrainedModel,
												)


else:
						import sys

						lowercase__						       =      _LazyModule(__name__, globals()["__file__"], _import_structure)


