





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 (		lowerCamelCase_					):





 """simple docstring"""


 _lowerCamelCase				=					42
 _lowerCamelCase				=					None







def 		__lowercase					(       snake_case_  :  Optional[int]    ,snake_case_  :  Tuple=0.999    ,snake_case_  :  List[str]="cosine"    ,)       ->List[Any]:



    '''simple docstring'''







    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

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

    __A   :						int									=					[]
    for i in range(SCREAMING_SNAKE_CASE__						):
        __A   :						Optional[int]									=					i / num_diffusion_timesteps
        __A   :						Tuple									=					(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 (		lowerCamelCase_							,							lowerCamelCase_					):





 """simple docstring"""


 @register_to_config
 def __init__(   self ,						__lowerCamelCase = 1000 ,						__lowerCamelCase = "fixed_small_log" ,						__lowerCamelCase = True ,						__lowerCamelCase = 1.0 ,						__lowerCamelCase = "epsilon" ,						__lowerCamelCase = "squaredcos_cap_v2" ,						):



     '''simple docstring'''




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

     __A   :						Optional[int]									=					betas_for_alpha_bar(_UpperCAmelCase  )

     __A   :						Any									=					1.0 - self.betas
     __A   :						Tuple									=					torch.cumprod(self.alphas ,						dim=0  )
     __A   :						Any									=					torch.tensor(1.0  )

     # standard deviation of the initial noise distribution
     __A   :						List[Any]									=					1.0

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

     __A   :						Union[str, Any]									=					variance_type

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None  ):



     '''simple docstring'''




     return sample

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None  ):



     '''simple docstring'''




     if prev_timestep is None:
         __A   :						Any									=					t - 1

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

     if prev_timestep == t - 1:
         __A   :						List[Any]									=					self.betas[t]
     else:
         __A   :						List[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
     __A   :						Any									=					beta_prod_t_prev / beta_prod_t * beta

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

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

         __A   :						List[Any]									=					(predicted_variance + 1) / 2
         __A   :						Optional[Any]									=					frac * max_log + (1 - frac) * min_log

     return variance

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase=None ,						__lowerCamelCase = True ,						):



     '''simple docstring'''




     __A   :						Tuple									=					timestep

     if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
         __A			,	__A   :						Tuple									=					torch.split(_UpperCAmelCase ,						sample.shape[1] ,						dim=1  )
     else:
         __A   :						List[Any]									=					None

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

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

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

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

         __A   :						Optional[int]									=					self._get_variance(
             _UpperCAmelCase ,						predicted_variance=_UpperCAmelCase ,						prev_timestep=_UpperCAmelCase ,						)

         if self.variance_type == "fixed_small_log":
             __A   :						Union[str, Any]									=					variance
         elif self.variance_type == "learned_range":
             __A   :						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.'''  )

         __A   :						Any									=					variance * variance_noise

     __A   :						List[Any]									=					pred_prev_sample + variance

     if not return_dict:
         return (pred_prev_sample,)

     return UnCLIPSchedulerOutput(prev_sample=_UpperCAmelCase ,						pred_original_sample=_UpperCAmelCase  )







 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						):



     '''simple docstring'''




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

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

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

     __A   :						List[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 _LazyModule


a_			=      {
    """config""": [
        """EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
        """OnnxConfig""",
        """OnnxConfigWithPast""",
        """OnnxSeq2SeqConfigWithPast""",
        """PatchingSpec""",
    ],
    """convert""": ["""export""", """validate_model_outputs"""],
    """features""": ["""FeaturesManager"""],
    """utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""],
}


if TYPE_CHECKING:
       from .config import (
           EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
           OnnxConfig,
           OnnxConfigWithPast,
           OnnxSeqaSeqConfigWithPast,
           PatchingSpec,
       )
       from .convert import export, validate_model_outputs
       from .features import FeaturesManager
       from .utils import ParameterFormat, compute_serialized_parameters_size

else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




