



"""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       SCREAMING_SNAKE_CASE__       (							lowercase__      ):
    lowercase__          =      42
    lowercase__          =      None







def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase       ,     __lowerCAmelCase=0.999       ,     __lowerCAmelCase="cosine"       ,     )       ->  Optional[Any]:







  '''simple docstring'''


  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

  lowercase_	    =       []
  for i in range(__lowerCamelCase							):
    lowercase_	    =       i / num_diffusion_timesteps
    lowercase_	    =       (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(__lowerCamelCase							) / alpha_bar_fn(__lowerCamelCase							)       ,     __lowerCamelCase							)							)
  return torch.tensor(__lowerCamelCase       ,     dtype=torch.floataa							)


class       SCREAMING_SNAKE_CASE__       (							lowercase__					,     lowercase__      ):

    @register_to_config
    def __init__( self	: Any       ,     lowerCAmelCase_	: Any = 1_0_0_0       ,     lowerCAmelCase_	: Dict = "fixed_small_log"       ,     lowerCAmelCase_	: int = True       ,     lowerCAmelCase_	: Tuple = 1.0       ,     lowerCAmelCase_	: Union[str, Any] = "epsilon"       ,     lowerCAmelCase_	: Dict = "squaredcos_cap_v2"       ,     ):







      """simple docstring"""







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

      lowercase_	    =       betas_for_alpha_bar(lowercase_)

      lowercase_	    =       1.0 - self.betas
      lowercase_	    =       torch.cumprod(self.alphas       ,     dim=0)
      lowercase_	    =       torch.tensor(1.0)

      # standard deviation of the initial noise distribution
      lowercase_	    =       1.0

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

      lowercase_	    =       variance_type

    def     _UpperCAmelCase		( self	: Tuple       ,     lowerCAmelCase_	: str       ,     lowerCAmelCase_	: Tuple = None):







      """simple docstring"""







      return sample

    def     _UpperCAmelCase		( self	: Optional[int]       ,     lowerCAmelCase_	: Dict       ,     lowerCAmelCase_	: List[Any] = None):







      """simple docstring"""







      lowercase_	    =       num_inference_steps
      lowercase_	    =       (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
      lowercase_	    =       (np.arange(0       ,     lowercase_) * step_ratio).round()[::-1].copy().astype(np.intaa)
      lowercase_	    =       torch.from_numpy(lowercase_).to(lowercase_)

    def     _UpperCAmelCase		( self	: Optional[int]       ,     lowerCAmelCase_	: int       ,     lowerCAmelCase_	: int=None       ,     lowerCAmelCase_	: Optional[int]=None       ,     lowerCAmelCase_	: Optional[Any]=None):







      """simple docstring"""







      if prev_timestep is None:
        lowercase_	    =       t - 1

      lowercase_	    =       self.alphas_cumprod[t]
      lowercase_	    =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      lowercase_	    =       1 - alpha_prod_t
      lowercase_	    =       1 - alpha_prod_t_prev

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

      if variance_type is None:
        lowercase_	    =       self.config.variance_type

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

        lowercase_	    =       (predicted_variance + 1) / 2
        lowercase_	    =       frac * max_log + (1 - frac) * min_log

      return variance

    def     _UpperCAmelCase		( self	: List[str]       ,     lowerCAmelCase_	: Dict       ,     lowerCAmelCase_	: str       ,     lowerCAmelCase_	: Union[str, Any]       ,     lowerCAmelCase_	: str = None       ,     lowerCAmelCase_	: Tuple=None       ,     lowerCAmelCase_	: Dict = True       ,     ):







      """simple docstring"""







      lowercase_	    =       timestep

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

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

      lowercase_	    =       self.alphas_cumprod[t]
      lowercase_	    =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      lowercase_	    =       1 - alpha_prod_t
      lowercase_	    =       1 - alpha_prod_t_prev

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

      # 6. Add noise
      lowercase_	    =       0
      if t > 0:
        lowercase_	    =       randn_tensor(
            model_output.shape       ,     dtype=model_output.dtype       ,     generator=lowercase_       ,     device=model_output.device)

        lowercase_	    =       self._get_variance(
            lowercase_       ,     predicted_variance=lowercase_       ,     prev_timestep=lowercase_       ,     )

        if self.variance_type == "fixed_small_log":
          lowercase_	    =       variance
        elif self.variance_type == "learned_range":
          lowercase_	    =       (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.""")

        lowercase_	    =       variance * variance_noise

      lowercase_	    =       pred_prev_sample + variance

      if not return_dict:
        return (pred_prev_sample,)

      return UnCLIPSchedulerOutput(prev_sample=lowercase_       ,     pred_original_sample=lowercase_)




    def     _UpperCAmelCase		( self	: List[str]       ,     lowerCAmelCase_	: Union[str, Any]       ,     lowerCAmelCase_	: Dict       ,     lowerCAmelCase_	: Optional[int]       ,     ):







      """simple docstring"""







      lowercase_	    =       self.alphas_cumprod.to(device=original_samples.device       ,     dtype=original_samples.dtype)
      lowercase_	    =       timesteps.to(original_samples.device)

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

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

      lowercase_	    =       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples



"""simple docstring"""





import argparse
from collections import defaultdict







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					int			     =    f"""{file}_{class_name}_{test_name}"""
   done_test[_id] += 1

   with open(__lowerCamelCase,		"r"  ) as f:
      UpperCAmelCase_					:					List[Any]			     =    f.readlines()

   UpperCAmelCase_					:					int			     =    f"""class {class_name}("""
   UpperCAmelCase_					:					Optional[Any]			     =    f"""{4 * " "}def {test_name}("""
   UpperCAmelCase_					:					Optional[Any]			     =    f"""{8 * " "}{correct_line.split()[0]}"""
   UpperCAmelCase_					:					Tuple			     =    f"""{16 * " "}{correct_line.split()[0]}"""
   UpperCAmelCase_					:					int			     =    False
   UpperCAmelCase_					:					Union[str, Any]			     =    False
   UpperCAmelCase_					:					str			     =    False
   UpperCAmelCase_					:					Optional[Any]			     =    False
   UpperCAmelCase_					:					List[str]			     =    0
   UpperCAmelCase_					:					Optional[int]			     =    0

   UpperCAmelCase_					:					int			     =    []
   for line in lines:
      if line.startswith(__lowerCamelCase  ):
         UpperCAmelCase_					:					Tuple			     =    True
      elif in_class and line.startswith(__lowerCamelCase  ):
         UpperCAmelCase_					:					Optional[int]			     =    True
      elif in_class and in_func and (line.startswith(__lowerCamelCase  ) or line.startswith(__lowerCamelCase  )):
         UpperCAmelCase_					:					Any			     =    len(line.split(correct_line.split()[0]  )[0]  )
         count += 1

         if count == done_test[_id]:
            UpperCAmelCase_					:					Union[str, Any]			     =    True

      if in_class and in_func and in_line:
         if ")" not in line:
            continue
         else:
            UpperCAmelCase_					:					Any			     =    True

      if in_class and in_func and in_line and insert_line:
         new_lines.append(f"""{spaces * " "}{correct_line}"""  )
         UpperCAmelCase_					:					int			     =    False
      else:
         new_lines.append(__lowerCamelCase  )

   with open(__lowerCamelCase,		"w"  ) as f:
      for line in new_lines:
         f.write(__lowerCamelCase  )







def 	__a     (		__lowerCamelCase,		__lowerCamelCase=None  ):
   if fail is not None:
      with open(__lowerCamelCase,		"r"  ) as f:
         UpperCAmelCase_					:					Tuple			     =    {l.strip() for l in f.readlines()}
   else:
      UpperCAmelCase_					:					str			     =    None

   with open(__lowerCamelCase,		"r"  ) as f:
      UpperCAmelCase_					:					Optional[int]			     =    f.readlines()

   UpperCAmelCase_					:					Any			     =    defaultdict(__lowerCamelCase  )
   for line in correct_lines:
      UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Any			     =    line.split(";"  )
      if test_failures is None or "::".join([file, class_name, test_name]  ) in test_failures:
         overwrite_file(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )


if __name__ == "__main__":
    _a		       =							argparse.ArgumentParser()
    parser.add_argument('--correct_filename', help='filename of tests with expected result')
    parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
    _a		       =							parser.parse_args()

    main(args.correct_filename, args.fail_filename)


