



"""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        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			torch.FloatTensor
 _lowerCAmelCase   :			Optional[torch.FloatTensor]							=		None





def __lowercase			(							_a		,			_a=0.999		,			_a="cosine"		,			):
    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

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

    snake_case_ :     Any				=	[]
    for i in range(_a    ):
        snake_case_ :     Optional[int]				=	i / num_diffusion_timesteps
        snake_case_ :     List[Any]				=	(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        _UpperCAmelCase       (			lowerCAmelCase__    ,     lowerCAmelCase__):

 @register_to_config
 def __init__(     self		:       List[str]  ,     lowercase_		:       int = 1000  ,     lowercase_		:       str = "fixed_small_log"  ,     lowercase_		:       bool = True  ,     lowercase_		:       Optional[float] = 1.0  ,     lowercase_		:       str = "epsilon"  ,     lowercase_		:       str = "squaredcos_cap_v2"  ,     ):
     if beta_schedule != "squaredcos_cap_v2":
         raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''				)

     snake_case_ :     List[str]				=	betas_for_alpha_bar(lowercase_				)

     snake_case_ :     Optional[Any]				=	1.0 - self.betas
     snake_case_ :     Tuple				=	torch.cumprod(self.alphas  ,     dim=0				)
     snake_case_ :     Optional[int]				=	torch.tensor(1.0				)

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

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

     snake_case_ :     Optional[Any]				=	variance_type

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

 def 						_snake_case					(     self		:       Tuple  ,     lowercase_		:       int  ,     lowercase_		:       Union[str, torch.device] = None				):
     snake_case_ :     Any				=	num_inference_steps
     snake_case_ :     str				=	(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
     snake_case_ :     Optional[Any]				=	(np.arange(0  ,     lowercase_				) * step_ratio).round()[::-1].copy().astype(np.intaa				)
     snake_case_ :     int				=	torch.from_numpy(lowercase_				).to(lowercase_				)

 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       List[Any]  ,     lowercase_		:       Dict=None  ,     lowercase_		:       Tuple=None  ,     lowercase_		:       List[Any]=None				):
     if prev_timestep is None:
         snake_case_ :     Dict				=	t - 1

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

     if prev_timestep == t - 1:
         snake_case_ :     List[str]				=	self.betas[t]
     else:
         snake_case_ :     Union[str, 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_ :     List[str]				=	beta_prod_t_prev / beta_prod_t * beta

     if variance_type is None:
         snake_case_ :     List[str]				=	self.config.variance_type

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

         snake_case_ :     int				=	(predicted_variance + 1) / 2
         snake_case_ :     List[str]				=	frac * max_log + (1 - frac) * min_log

     return variance

 def 						_snake_case					(     self		:       str  ,     lowercase_		:       torch.FloatTensor  ,     lowercase_		:       int  ,     lowercase_		:       torch.FloatTensor  ,     lowercase_		:       Optional[int] = None  ,     lowercase_		:       List[Any]=None  ,     lowercase_		:       bool = True  ,     ):
     snake_case_ :     Any				=	timestep

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

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

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

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

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

         snake_case_ :     Dict				=	self._get_variance(
             lowercase_  ,     predicted_variance=lowercase_  ,     prev_timestep=lowercase_  ,     )

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

         snake_case_ :     Dict				=	variance * variance_noise

     snake_case_ :     Optional[Any]				=	pred_prev_sample + variance

     if not return_dict:
         return (pred_prev_sample,)

     return UnCLIPSchedulerOutput(prev_sample=lowercase_  ,     pred_original_sample=lowercase_				)






 def 						_snake_case					(     self		:       Any  ,     lowercase_		:       torch.FloatTensor  ,     lowercase_		:       torch.FloatTensor  ,     lowercase_		:       torch.IntTensor  ,     ):
     # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
     snake_case_ :     Optional[int]				=	self.alphas_cumprod.to(device=original_samples.device  ,     dtype=original_samples.dtype				)
     snake_case_ :     Optional[int]				=	timesteps.to(original_samples.device				)

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

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

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










"""simple docstring"""

def __lowercase			(							_a		,			_a		,			_a=False    ):

    if isinstance(_a		,			_a    ) and isinstance(_a		,			_a    ):
        snake_case_ :     Union[str, Any]				=	len(set_a.intersection(_a    )    )

        if alternative_union:
            snake_case_ :     Any				=	len(_a    ) + len(_a    )
        else:
            snake_case_ :     str				=	len(set_a.union(_a    )    )

        return intersection / union

    if isinstance(_a		,			(list, tuple)    ) and isinstance(_a		,			(list, tuple)    ):
        snake_case_ :     str				=	[element for element in set_a if element in set_b]

        if alternative_union:
            snake_case_ :     Tuple				=	len(_a    ) + len(_a    )
            return len(_a    ) / union
        else:
            snake_case_ :     List[Any]				=	set_a + [element for element in set_b if element not in set_a]
            return len(_a    ) / len(_a    )

        return len(_a    ) / len(_a    )
    return None


if __name__ == "__main__":
     lowercase__    :  Any							    =	{'''a''', '''b''', '''c''', '''d''', '''e'''}
     lowercase__    :  Optional[Any]							    =	{'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
     print(jaccard_similarity(set_a, set_b))





