




'''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
class 		lowercase_    ( UpperCAmelCase_   ):


   """simple docstring"""

   lowerCamelCase_				  =						42
   lowerCamelCase_				  =						42


def  SCREAMING_SNAKE_CASE_ (							__A				: List[Any]       ,     __A				: str=0.9_9_9       ,     __A				: Dict="cosine"       ,     ) -> List[Any]:







   if alpha_transform_type == "cosine":

      def alpha_bar_fn(__A				: Optional[Any]  ):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2  ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(__A				: Optional[int]  ):
         return math.exp(t * -1_2.0  )

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

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







class 		lowercase_    ( UpperCAmelCase_     ,  UpperCAmelCase_   ):


   """simple docstring"""

   lowerCamelCase_				  =						1



   @register_to_config
   def __init__(    self					:	int  ,      __lowerCamelCase					:	Optional[int] = 1_0_0_0  ,      __lowerCamelCase					:	Optional[Any] = 0.0_0_0_1  ,      __lowerCamelCase					:	Any = 0.0_2  ,      __lowerCamelCase					:	List[Any] = "linear"  ,      __lowerCamelCase					:	str = 0.0  ,      __lowerCamelCase					:	Optional[int] = None  ,      __lowerCamelCase					:	List[str] = True  ,      ):

      """simple docstring"""
      if trained_betas is not None:
         _SCREAMING_SNAKE_CASE       =      torch.from_numpy(__UpperCamelCase    )
      elif beta_schedule == "linear":
         _SCREAMING_SNAKE_CASE       =      torch.linspace(__UpperCamelCase  ,      __UpperCamelCase  ,      __UpperCamelCase  ,      dtype=torch.floataa    )
      elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         _SCREAMING_SNAKE_CASE       =      (
             torch.linspace(beta_start**0.5  ,      beta_end**0.5  ,      __UpperCamelCase  ,      dtype=torch.floataa    ) ** 2
         )
      elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         _SCREAMING_SNAKE_CASE       =      betas_for_alpha_bar(__UpperCamelCase    )
      elif beta_schedule == "sigmoid":
         # GeoDiff sigmoid schedule
         _SCREAMING_SNAKE_CASE       =      torch.linspace(-6  ,      6  ,      __UpperCamelCase    )
         _SCREAMING_SNAKE_CASE       =      torch.sigmoid(__UpperCamelCase    ) * (beta_end - beta_start) + beta_start
      else:
         raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""    )

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

      _SCREAMING_SNAKE_CASE       =      torch.tensor(1.0    )

      # standard deviation of the initial noise distribution
      _SCREAMING_SNAKE_CASE       =      1.0

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

      _SCREAMING_SNAKE_CASE       =      eta



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

      """simple docstring"""
      return sample



   def 							lowerCAmelCase_			(    self					:	List[Any]  ,      __lowerCamelCase					:	int  ,      __lowerCamelCase					:	Dict = 1_0  ,      __lowerCamelCase					:	List[str] = 1_0  ,      __lowerCamelCase					:	Optional[Any] = None  ,      ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE       =      min(self.config.num_train_timesteps  ,      __UpperCamelCase    )
      _SCREAMING_SNAKE_CASE       =      num_inference_steps

      _SCREAMING_SNAKE_CASE       =      []

      _SCREAMING_SNAKE_CASE       =      {}
      for j in range(0  ,      num_inference_steps - jump_length  ,      __UpperCamelCase    ):
         _SCREAMING_SNAKE_CASE       =      jump_n_sample - 1

      _SCREAMING_SNAKE_CASE       =      num_inference_steps
      while t >= 1:
         _SCREAMING_SNAKE_CASE       =      t - 1
         timesteps.append(__UpperCamelCase    )

         if jumps.get(__UpperCamelCase  ,      0    ) > 0:
            _SCREAMING_SNAKE_CASE       =      jumps[t] - 1
            for _ in range(__UpperCamelCase    ):
               _SCREAMING_SNAKE_CASE       =      t + 1
               timesteps.append(__UpperCamelCase    )

      _SCREAMING_SNAKE_CASE       =      np.array(__UpperCamelCase    ) * (self.config.num_train_timesteps // self.num_inference_steps)
      _SCREAMING_SNAKE_CASE       =      torch.from_numpy(__UpperCamelCase    ).to(__UpperCamelCase    )



   def 							lowerCAmelCase_			(    self					:	List[str]  ,      __lowerCamelCase					:	List[str]    ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE       =      t - self.config.num_train_timesteps // self.num_inference_steps

      _SCREAMING_SNAKE_CASE       =      self.alphas_cumprod[t]
      _SCREAMING_SNAKE_CASE       =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
      _SCREAMING_SNAKE_CASE       =      1 - alpha_prod_t
      _SCREAMING_SNAKE_CASE       =      1 - 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
      # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
      # without eta.
      # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
      _SCREAMING_SNAKE_CASE       =      (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

      return variance



   def 							lowerCAmelCase_			(    self					:	str  ,      __lowerCamelCase					:	str  ,      __lowerCamelCase					:	Any  ,      __lowerCamelCase					:	int  ,      __lowerCamelCase					:	Optional[Any]  ,      __lowerCamelCase					:	str  ,      __lowerCamelCase					:	Optional[Any] = None  ,      __lowerCamelCase					:	Tuple = True  ,      ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE       =      timestep
      _SCREAMING_SNAKE_CASE       =      timestep - self.config.num_train_timesteps // self.num_inference_steps

      # 1. compute alphas, betas
      _SCREAMING_SNAKE_CASE       =      self.alphas_cumprod[t]
      _SCREAMING_SNAKE_CASE       =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
      _SCREAMING_SNAKE_CASE       =      1 - alpha_prod_t

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      _SCREAMING_SNAKE_CASE       =      (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
         _SCREAMING_SNAKE_CASE       =      torch.clamp(__UpperCamelCase  ,      -1  ,      1    )

      # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
      # substitute formula (7) in the algorithm coming from DDPM paper
      # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
      # DDIM schedule gives the same results as DDPM with eta = 1.0
      # Noise is being reused in 7. and 8., but no impact on quality has
      # been observed.

      # 5. Add noise
      _SCREAMING_SNAKE_CASE       =      model_output.device
      _SCREAMING_SNAKE_CASE       =      randn_tensor(model_output.shape  ,      generator=__UpperCamelCase  ,      device=__UpperCamelCase  ,      dtype=model_output.dtype    )
      _SCREAMING_SNAKE_CASE       =      self.eta * self._get_variance(__UpperCamelCase    ) ** 0.5

      _SCREAMING_SNAKE_CASE       =      0
      if t > 0 and self.eta > 0:
         _SCREAMING_SNAKE_CASE       =      std_dev_t * noise

      # 6. compute "direction pointing to x_t" of formula (12)
      # from https://arxiv.org/pdf/2010.02502.pdf
      _SCREAMING_SNAKE_CASE       =      (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

      # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      _SCREAMING_SNAKE_CASE       =      alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

      # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
      _SCREAMING_SNAKE_CASE       =      (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

      # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
      _SCREAMING_SNAKE_CASE       =      mask * prev_known_part + (1.0 - mask) * prev_unknown_part

      if not return_dict:
         return (
             pred_prev_sample,
             pred_original_sample,
         )

      return RePaintSchedulerOutput(prev_sample=__UpperCamelCase  ,      pred_original_sample=__UpperCamelCase    )



   def 							lowerCAmelCase_			(    self					:	Union[str, Any]  ,      __lowerCamelCase					:	Optional[int]  ,      __lowerCamelCase					:	List[str]  ,      __lowerCamelCase					:	List[str]=None    ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE       =      self.config.num_train_timesteps // self.num_inference_steps

      for i in range(__UpperCamelCase    ):
         _SCREAMING_SNAKE_CASE       =      self.betas[timestep + i]
         if sample.device.type == "mps":
            # randn does not work reproducibly on mps
            _SCREAMING_SNAKE_CASE       =      randn_tensor(sample.shape  ,      dtype=sample.dtype  ,      generator=__UpperCamelCase    )
            _SCREAMING_SNAKE_CASE       =      noise.to(sample.device    )
         else:
            _SCREAMING_SNAKE_CASE       =      randn_tensor(sample.shape  ,      generator=__UpperCamelCase  ,      device=sample.device  ,      dtype=sample.dtype    )

         # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
         _SCREAMING_SNAKE_CASE       =      (1 - beta) ** 0.5 * sample + beta**0.5 * noise

      return sample



   def 							lowerCAmelCase_			(    self					:	int  ,      __lowerCamelCase					:	Union[str, Any]  ,      __lowerCamelCase					:	Union[str, Any]  ,      __lowerCamelCase					:	Optional[Any]  ,      ):

      """simple docstring"""
      raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."    )



   def __len__(    self					:	Dict    ):

      """simple docstring"""
      return self.config.num_train_timesteps







'''simple docstring'''







import unittest
from timeit import timeit






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       first_num if first_num >= second_num else second_num
		_snake_case	       =       max_num
		while (common_mult % first_num > 0) or (common_mult % second_num > 0):
				common_mult += max_num
		return common_mult






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		return b if a == 0 else greatest_common_divisor(b % a						,							lowercase_  )






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		return first_num // greatest_common_divisor(lowercase_						,							lowercase_  ) * second_num






def 		_lowercase		(	):



		'''simple docstring'''







		_snake_case	       =       (
		    "from __main__ import least_common_multiple_slow, least_common_multiple_fast"
		)
		print(
		    "least_common_multiple_slow():"						,							timeit("least_common_multiple_slow(1000, 999)"						,							setup=lowercase_  )						,							)
		print(
		    "least_common_multiple_fast():"						,							timeit("least_common_multiple_fast(1000, 999)"						,							setup=lowercase_  )						,							)





class      lowerCamelCase_							(       unittest.TestCase							):




					'''simple docstring'''






					lowercase__    = (
					    (1_0, 2_0),
					    (1_3, 1_5),
					    (4, 3_1),
					    (1_0, 4_2),
					    (4_3, 3_4),
					    (5, 1_2),
					    (1_2, 2_5),
					    (1_0, 2_5),
					    (6, 9),
					)
					lowercase__    = (2_0, 1_9_5, 1_2_4, 2_1_0, 1_4_6_2, 6_0, 3_0_0, 5_0, 1_8)






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for i, (first_num, second_num) in enumerate(self.test_inputs ):
									_snake_case	       =       least_common_multiple_slow(__UpperCamelCase,  __UpperCamelCase )
									_snake_case	       =       least_common_multiple_fast(__UpperCamelCase,  __UpperCamelCase )
									with self.subTest(i=__UpperCamelCase ):
											self.assertEqual(__UpperCamelCase,  self.expected_results[i] )
											self.assertEqual(__UpperCamelCase,  self.expected_results[i] )


if __name__ == "__main__":
						benchmark()
						unittest.main()





