





'''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 __a			( _snake_case      ):
   __UpperCamelCase :			torch.FloatTensor
   __UpperCamelCase :			Optional[torch.FloatTensor]						   =						None





def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=0.9_9_9				,						__UpperCAmelCase="cosine"				,						)    ->       Union[str, Any]:

   '''simple docstring'''







   if alpha_transform_type == "cosine":

      def alpha_bar_fn(__UpperCAmelCase   ):
         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(__UpperCAmelCase   ):
         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(__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							i / num_diffusion_timesteps
      __SCREAMING_SNAKE_CASE   				=							(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase   ) / alpha_bar_fn(__UpperCAmelCase   )				,						__UpperCAmelCase   )   )
   return torch.tensor(__UpperCAmelCase				,						dtype=torch.floataa   )







class __a			( _snake_case, _snake_case      ):





   @register_to_config
   def __init__(       self					:      Tuple					,lowerCamelCase					:      int = 1000					,lowerCamelCase					:      str = "fixed_small_log"					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      Optional[float] = 1.0					,lowerCamelCase					:      str = "epsilon"					,lowerCamelCase					:      str = "squaredcos_cap_v2"					,):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE   				=							betas_for_alpha_bar(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							1.0 - self.betas
      __SCREAMING_SNAKE_CASE   				=							torch.cumprod(self.alphas					,dim=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					,lowerCamelCase     )[::-1].copy()     )

      __SCREAMING_SNAKE_CASE   				=							variance_type





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      torch.FloatTensor					,lowerCamelCase					:      Optional[int] = None     ):

      '''simple docstring'''

      return sample





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      int					,lowerCamelCase					:      Union[str, torch.device] = None     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Optional[Any]=None					,lowerCamelCase					:      List[str]=None					,lowerCamelCase					:      Any=None     ):

      '''simple docstring'''

      if prev_timestep is None:
         __SCREAMING_SNAKE_CASE   				=							t - 1

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

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

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

      # hacks - were probably added for training stability
      if variance_type == "fixed_small_log":
         __SCREAMING_SNAKE_CASE   				=							torch.log(torch.clamp(lowerCamelCase					,min=1E-2_0     )     )
         __SCREAMING_SNAKE_CASE   				=							torch.exp(0.5 * variance     )
      elif variance_type == "learned_range":
         # NOTE difference with DDPM scheduler
         __SCREAMING_SNAKE_CASE   				=							variance.log()
         __SCREAMING_SNAKE_CASE   				=							beta.log()

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

      return variance





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      torch.FloatTensor					,lowerCamelCase					:      int					,lowerCamelCase					:      torch.FloatTensor					,lowerCamelCase					:      Optional[int] = None					,lowerCamelCase					:      Tuple=None					,lowerCamelCase					:      bool = True					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							timestep

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

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

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

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

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

         __SCREAMING_SNAKE_CASE   				=							self._get_variance(
             lowerCamelCase					,predicted_variance=lowerCamelCase					,prev_timestep=lowerCamelCase					,)

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

         __SCREAMING_SNAKE_CASE   				=							variance * variance_noise

      __SCREAMING_SNAKE_CASE   				=							pred_prev_sample + variance

      if not return_dict:
         return (pred_prev_sample,)

      return UnCLIPSchedulerOutput(prev_sample=lowerCamelCase					,pred_original_sample=lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Any					,lowerCamelCase					:      torch.FloatTensor					,lowerCamelCase					:      torch.FloatTensor					,lowerCamelCase					:      torch.IntTensor					,):

      '''simple docstring'''

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

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

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

      __SCREAMING_SNAKE_CASE   				=							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples












import gc
import threading
import time

import psutil
import torch

class   lowerCAmelCase				:
 def __init__( self    : str      )     ->      Union[str, Any]:
     lowerCamelCase__       : Optional[Any]   					=						psutil.Process()
     lowerCamelCase__       : Union[str, Any]   					=						False
 def 	A_      ( self    : Optional[int]      )     ->      int:
     lowerCamelCase__       : Optional[Any]   					=						-1

     while True:
         lowerCamelCase__       : Dict   					=						max(self.process.memory_info().rss     ,	self.cpu_memory_peak      )

         # can't sleep or will not catch the peak right (this comment is here on purpose)
         if not self.peak_monitoring:
             break
 def 	A_      ( self    : Tuple      )     ->      Dict:
     lowerCamelCase__       : List[Any]   					=						True
     lowerCamelCase__       : List[str]   					=						threading.Thread(target=self.peak_monitor      )
     lowerCamelCase__       : Union[str, Any]   					=						True
     self.thread.start()







 def 	A_      ( self    : str      )     ->      Dict:
     lowerCamelCase__       : int   					=						False
     self.thread.join()
     return self.cpu_memory_peak


_UpperCAmelCase							:			Dict        =							PeakCPUMemory()


def 			SCREAMING_SNAKE_CASE (			)     ->	Union[str, Any]:
    # Time
    lowerCamelCase__       : List[Any]   					=						{'time': time.time()}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    lowerCamelCase__       : List[str]   					=						psutil.Process().memory_info().rss
    cpu_peak_tracker.start()

    # GPU mem
    for i in range(torch.cuda.device_count()					):
        lowerCamelCase__       : Union[str, Any]   					=						torch.cuda.memory_allocated(_UpperCAmelCase					)
    torch.cuda.reset_peak_memory_stats()

    return measures


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Tuple:
    # Time
    lowerCamelCase__       : Optional[int]   					=						{'time': time.time() - start_measures['time']}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    lowerCamelCase__       : Dict   					=						(psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
    lowerCamelCase__       : int   					=						(cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20

    # GPU mem
    for i in range(torch.cuda.device_count()					):
        lowerCamelCase__       : List[str]   					=						(torch.cuda.memory_allocated(_UpperCAmelCase					) - start_measures[str(_UpperCAmelCase					)]) / 2**20
        lowerCamelCase__       : Optional[Any]   					=						(torch.cuda.max_memory_allocated(_UpperCAmelCase					) - start_measures[str(_UpperCAmelCase					)]) / 2**20

    return measures


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	Dict:
    print(F"""{description}:"""					)
    print(F"""- Time: {measures["time"]:.2f}s"""					)
    for i in range(torch.cuda.device_count()					):
        print(F"""- GPU {i} allocated: {measures[str(_UpperCAmelCase					)]:.2f}MiB"""					)
        lowerCamelCase__       : List[str]   					=						measures[F"""{i}-peak"""]
        print(F"""- GPU {i} peak: {peak:.2f}MiB"""					)
    print(F"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB"""					)
    print(F"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB"""					)





