

'''simple docstring'''





from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


lowerCamelCase     =      logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class 		__magic_name__    (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__				:			torch.FloatTensor







class 		__magic_name__    (							lowerCamelCase__	, lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__				:			Optional[int]												=     1



   @register_to_config
   def __init__(       self,					lowercase_ = 40,					lowercase_ = 0.002,					lowercase_ = 80.0,					lowercase_ = 0.5,					lowercase_ = 1.0,					lowercase_ = 7.0,					lowercase_ = True,					)							->					Optional[Any]:






     """simple docstring"""






     a__						=sigma_max

     a__						=np.linspace(0,					1,					lowercase_ )
     a__						=self._convert_to_karras(lowercase_ )
     a__						=self.sigma_to_t(lowercase_ )

     # setable values
     a__						=None
     a__						=torch.from_numpy(lowercase_ )
     a__						=torch.from_numpy(lowercase_ )
     a__						=False
     a__						=False



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_=None )							->					str:






     """simple docstring"""






     if schedule_timesteps is None:
       a__						=self.timesteps

     a__						=(schedule_timesteps == timestep).nonzero()
     return indices.item()



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ )							->					torch.FloatTensor:






     """simple docstring"""






     if isinstance(lowercase_,					torch.Tensor ):
       a__						=timestep.to(self.timesteps.device )
     a__						=self.index_for_timestep(lowercase_ )
     a__						=self.sigmas[step_idx]

     a__						=sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

     a__						=True
     return sample



   def 				_UpperCAmelCase				(       self,					lowercase_ )							->					Optional[int]:






     """simple docstring"""






     if not isinstance(lowercase_,					np.ndarray ):
       a__						=np.array(lowercase_,					dtype=np.floataa )

     a__						=1000 * 0.25 * np.log(sigmas + 1E-44 )

     return timesteps



   def 				_UpperCAmelCase				(       self,					lowercase_ = None,					lowercase_ = None,					lowercase_ = None,					)							->					int:






     """simple docstring"""






     if num_inference_steps is None and timesteps is None:
       raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.''' )

     if num_inference_steps is not None and timesteps is not None:
       raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.''' )

     # Follow DDPMScheduler custom timesteps logic
     if timesteps is not None:
       for i in range(1,					len(lowercase_ ) ):
         if timesteps[i] >= timesteps[i - 1]:
           raise ValueError('''`timesteps` must be in descending order.''' )

       if timesteps[0] >= self.config.num_train_timesteps:
         raise ValueError(
             F"""`timesteps` must start before `self.config.train_timesteps`:"""
             F""" {self.config.num_train_timesteps}.""" )

       a__						=np.array(lowercase_,					dtype=np.intaa )
       a__						=True
     else:
       if num_inference_steps > self.config.num_train_timesteps:
         raise ValueError(
             F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
             F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
             F""" maximal {self.config.num_train_timesteps} timesteps.""" )

       a__						=num_inference_steps

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

     # Map timesteps to Karras sigmas directly for multistep sampling
     # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
     a__						=self.config.num_train_timesteps
     a__						=timesteps[::-1].copy()
     a__						=ramp / (num_train_timesteps - 1)
     a__						=self._convert_to_karras(lowercase_ )
     a__						=self.sigma_to_t(lowercase_ )

     a__						=np.concatenate([sigmas, [self.sigma_min]] ).astype(np.floataa )
     a__						=torch.from_numpy(lowercase_ ).to(device=lowercase_ )

     if str(lowercase_ ).startswith('''mps''' ):
       # mps does not support float64
       a__						=torch.from_numpy(lowercase_ ).to(lowercase_,					dtype=torch.floataa )
     else:
       a__						=torch.from_numpy(lowercase_ ).to(device=lowercase_ )



   def 				_UpperCAmelCase				(       self,					lowercase_ )							->					int:






     """simple docstring"""






     a__						=self.config.sigma_min
     a__						=self.config.sigma_max

     a__						=self.config.rho
     a__						=sigma_min ** (1 / rho)
     a__						=sigma_max ** (1 / rho)
     a__						=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
     return sigmas



   def 				_UpperCAmelCase				(       self,					lowercase_ )							->					Optional[Any]:






     """simple docstring"""






     a__						=self.config.sigma_data

     a__						=sigma_data**2 / (sigma**2 + sigma_data**2)
     a__						=sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
     return c_skip, c_out



   def 				_UpperCAmelCase				(       self,					lowercase_ )							->					List[Any]:






     """simple docstring"""






     a__						=self.config.sigma_min
     a__						=self.config.sigma_data

     a__						=sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
     a__						=(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
     return c_skip, c_out



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_ = None,					lowercase_ = True,					)							->					Union[CMStochasticIterativeSchedulerOutput, Tuple]:






     """simple docstring"""






     if (
         isinstance(lowercase_,					lowercase_ )
         or isinstance(lowercase_,					torch.IntTensor )
         or isinstance(lowercase_,					torch.LongTensor )
     ):
       raise ValueError(
           (
               '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
               F""" `{self.__class__}.step()` is not supported. Make sure to pass"""
               ''' one of the `scheduler.timesteps` as a timestep.'''
           ),					)

     if not self.is_scale_input_called:
       logger.warning(
           '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
           '''See `StableDiffusionPipeline` for a usage example.''' )

     if isinstance(lowercase_,					torch.Tensor ):
       a__						=timestep.to(self.timesteps.device )

     a__						=self.config.sigma_min
     a__						=self.config.sigma_max

     a__						=self.index_for_timestep(lowercase_ )

     # sigma_next corresponds to next_t in original implementation
     a__						=self.sigmas[step_index]
     if step_index + 1 < self.config.num_train_timesteps:
       a__						=self.sigmas[step_index + 1]
     else:
       # Set sigma_next to sigma_min
       a__						=self.sigmas[-1]

     # Get scalings for boundary conditions
     a__,   a__						=self.get_scalings_for_boundary_condition(lowercase_ )

     # 1. Denoise model output using boundary conditions
     a__						=c_out * model_output + c_skip * sample
     if self.config.clip_denoised:
       a__						=denoised.clamp(-1,					1 )

     # 2. Sample z ~ N(0, s_noise^2 * I)
     # Noise is not used for onestep sampling.
     if len(self.timesteps ) > 1:
       a__						=randn_tensor(
           model_output.shape,					dtype=model_output.dtype,					device=model_output.device,					generator=lowercase_ )
     else:
       a__						=torch.zeros_like(lowercase_ )
     a__						=noise * self.config.s_noise

     a__						=sigma_next.clamp(min=lowercase_,					max=lowercase_ )

     # 3. Return noisy sample
     # tau = sigma_hat, eps = sigma_min
     a__						=denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

     if not return_dict:
       return (prev_sample,)

     return CMStochasticIterativeSchedulerOutput(prev_sample=lowercase_ )



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					)							->					torch.FloatTensor:






     """simple docstring"""






     a__						=self.sigmas.to(device=original_samples.device,					dtype=original_samples.dtype )
     if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_ ):
       # mps does not support float64
       a__						=self.timesteps.to(original_samples.device,					dtype=torch.floataa )
       a__						=timesteps.to(original_samples.device,					dtype=torch.floataa )
     else:
       a__						=self.timesteps.to(original_samples.device )
       a__						=timesteps.to(original_samples.device )

     a__						=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

     a__						=sigmas[step_indices].flatten()
     while len(sigma.shape ) < len(original_samples.shape ):
       a__						=sigma.unsqueeze(-1 )

     a__						=original_samples + noise * sigma
     return noisy_samples



   def __len__(       self )							->					Tuple:






     """simple docstring"""






     return self.config.num_train_timesteps



import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


lowerCamelCase     =      get_logger(__name__)




def     UpperCAmelCase__       (    _A   :				str			,   _A   :				List[dict]			,   _A   :				list			,   _A   :				list			,   _A   :				int			,   _A   :				GradScaler = None			,   ):





  '''simple docstring'''







  for i, state in enumerate(_A							):
    a__						=F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    save(_A			,   _A							)
    logger.info(F"""Model weights saved in {output_model_file}"""							)
  # Optimizer states
  for i, opt in enumerate(_A							):
    a__						=opt.state_dict()
    a__						=F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    save(_A			,   _A							)
    logger.info(F"""Optimizer state saved in {output_optimizer_file}"""							)
  # Scheduler states
  for i, scheduler in enumerate(_A							):
    a__						=scheduler.state_dict()
    a__						=F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    save(_A			,   _A							)
    logger.info(F"""Scheduler state saved in {output_scheduler_file}"""							)
  # GradScaler state
  if scaler is not None:
    a__						=scaler.state_dict()
    a__						=os.path.join(_A			,   _A							)
    torch.save(_A			,   _A							)
    logger.info(F"""Gradient scaler state saved in {output_scaler_file}"""							)
  # Random number generator states
  a__						={}
  a__						=F"""{RNG_STATE_NAME}_{process_index}.pkl"""
  a__						=random.getstate()
  a__						=np.random.get_state()
  a__						=torch.get_rng_state()
  if is_xpu_available():
    a__						=torch.xpu.get_rng_state_all()
  else:
    a__						=torch.cuda.get_rng_state_all()
  if is_tpu_available():
    a__						=xm.get_rng_state()
  a__						=os.path.join(_A			,   _A							)
  torch.save(_A			,   _A							)
  logger.info(F"""Random states saved in {output_states_file}"""							)
  return output_dir




def     UpperCAmelCase__       (    _A   :				Union[str, Any]			,   _A   :				int			,   _A   :				int			,   _A   :				Union[str, Any]			,   _A   :				Tuple			,   _A   :				Optional[int]=None			,   _A   :				List[Any]=None			,   **_A   :				Dict			,   ):





  '''simple docstring'''







  if map_location not in [None, "cpu", "on_device"]:
    raise TypeError(
        '''Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'''							)
  if map_location is None:
    a__						='''cpu'''
  elif map_location == "on_device":
    a__						=PartialState().device
  # Model states
  for i, model in enumerate(_A							):
    a__						=F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    models[i].load_state_dict(torch.load(_A			,   map_location=_A							)			,   **_A							)
  logger.info('''All model weights loaded successfully'''							)

  # Optimizer states
  for i, opt in enumerate(_A							):
    a__						=F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    a__						=torch.load(_A			,   map_location=_A							)
    optimizers[i].load_state_dict(_A							)
  logger.info('''All optimizer states loaded successfully'''							)

  # Scheduler states
  for i, scheduler in enumerate(_A							):
    a__						=F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    scheduler.load_state_dict(torch.load(_A							)							)
  logger.info('''All scheduler states loaded successfully'''							)

  # GradScaler state
  if scaler is not None:
    a__						=os.path.join(_A			,   _A							)
    scaler.load_state_dict(torch.load(_A							)							)
    logger.info('''GradScaler state loaded successfully'''							)

  # Random states
  try:
    a__						=torch.load(os.path.join(_A			,   F"""{RNG_STATE_NAME}_{process_index}.pkl"""							)							)
    random.setstate(states['''random_state''']							)
    np.random.set_state(states['''numpy_random_seed''']							)
    torch.set_rng_state(states['''torch_manual_seed''']							)
    if is_xpu_available():
      torch.xpu.set_rng_state_all(states['''torch_xpu_manual_seed''']							)
    else:
      torch.cuda.set_rng_state_all(states['''torch_cuda_manual_seed''']							)
    if is_tpu_available():
      xm.set_rng_state(states['''xm_seed''']							)
    logger.info('''All random states loaded successfully'''							)
  except Exception:
    logger.info('''Could not load random states'''							)




def     UpperCAmelCase__       (    _A   :				Optional[Any]			,   _A   :				Any			,   _A   :				int = 0							):





  '''simple docstring'''







  a__						=Path(_A							) / F"""custom_checkpoint_{index}.pkl"""
  logger.info(F"""Saving the state of {get_pretty_name(_A							)} to {save_location}"""							)
  torch.save(obj.state_dict()			,   _A							)




def     UpperCAmelCase__       (    _A   :				int			,   _A   :				Any			,   _A   :				int = 0							):





  '''simple docstring'''







  a__						=F"""{path}/custom_checkpoint_{index}.pkl"""
  logger.info(F"""Loading the state of {get_pretty_name(_A							)} from {load_location}"""							)
  obj.load_state_dict(torch.load(_A			,   map_location='''cpu'''							)							)
