






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
@flax.struct.dataclass
class 	a_:



     """simple docstring"""



     __snake_case		:				Optional[jnp.ndarray]  					=None
     __snake_case		:				Optional[jnp.ndarray]  					=None
     __snake_case		:				Optional[jnp.ndarray]  					=None





     @classmethod
     def     __UpperCamelCase						(		cls					:	Optional[Any])							->	int:

      """simple docstring"""
      return cls()

@dataclass
class 	a_(		lowercase__	):



     """simple docstring"""



     __snake_case		:				ScoreSdeVeSchedulerState
     __snake_case		:				jnp.ndarray
     __snake_case		:				Optional[jnp.ndarray]  					=None

class 	a_(		lowercase__			,							lowercase__	):



     """simple docstring"""



     @property
     def     __UpperCamelCase						(		self					:	Tuple)							->	str:

      """simple docstring"""
      return True





     @register_to_config
     def __init__(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	int = 2_0_0_0  ,  lowerCAmelCase__					:	float = 0.15  ,  lowerCAmelCase__					:	float = 0.01  ,  lowerCAmelCase__					:	float = 13_48.0  ,  lowerCAmelCase__					:	float = 1e-5  ,  lowerCAmelCase__					:	int = 1  ,  )							->	str:

      """simple docstring"""
      pass





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ScoreSdeVeSchedulerState.create()
      return self.set_sigmas(
          lowerCAmelCase__  ,  self.config.num_train_timesteps  ,  self.config.sigma_min  ,  self.config.sigma_max  ,  self.config.sampling_eps  ,  )





     def     __UpperCamelCase						(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	ScoreSdeVeSchedulerState  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	Tuple = ()  ,  lowerCAmelCase__					:	float = None)							->	ScoreSdeVeSchedulerState:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		sampling_eps if sampling_eps is not None else self.config.sampling_eps

      SCREAMING_SNAKE_CASE				    =		jnp.linspace(1  ,  lowerCAmelCase__  ,  lowerCAmelCase__)
      return state.replace(timesteps=lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Any  ,  lowerCAmelCase__					:	ScoreSdeVeSchedulerState  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	float = None  ,  lowerCAmelCase__					:	float = None  ,  lowerCAmelCase__					:	float = None  ,  )							->	ScoreSdeVeSchedulerState:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		sigma_min if sigma_min is not None else self.config.sigma_min
      SCREAMING_SNAKE_CASE				    =		sigma_max if sigma_max is not None else self.config.sigma_max
      SCREAMING_SNAKE_CASE				    =		sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if state.timesteps is None:
       SCREAMING_SNAKE_CASE				    =		self.set_timesteps(lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		jnp.exp(jnp.linspace(jnp.log(lowerCAmelCase__)  ,  jnp.log(lowerCAmelCase__)  ,  lowerCAmelCase__))
      SCREAMING_SNAKE_CASE				    =		jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps])

      return state.replace(discrete_sigmas=lowerCAmelCase__  ,  sigmas=lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	List[Any]  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	Dict)							->	Dict:

      """simple docstring"""
      return jnp.where(timesteps == 0  ,  jnp.zeros_like(lowerCAmelCase__)  ,  state.discrete_sigmas[timesteps - 1])





     def     __UpperCamelCase						(		self					:	Any  ,  lowerCAmelCase__					:	ScoreSdeVeSchedulerState  ,  lowerCAmelCase__					:	jnp.ndarray  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	jnp.ndarray  ,  lowerCAmelCase__					:	random.KeyArray  ,  lowerCAmelCase__					:	bool = True  ,  )							->	Union[FlaxSdeVeOutput, Tuple]:

      """simple docstring"""
      if state.timesteps is None:
       raise ValueError(
           '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler')

      SCREAMING_SNAKE_CASE				    =		timestep * jnp.ones(
          sample.shape[0]  ,  )
      SCREAMING_SNAKE_CASE				    =		(timestep * (len(state.timesteps) - 1)).long()

      SCREAMING_SNAKE_CASE				    =		state.discrete_sigmas[timesteps]
      SCREAMING_SNAKE_CASE				    =		self.get_adjacent_sigma(lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		jnp.zeros_like(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		(sigma**2 - adjacent_sigma**2) ** 0.5

      # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
      # also equation 47 shows the analog from SDE models to ancestral sampling methods
      SCREAMING_SNAKE_CASE				    =		diffusion.flatten()
      SCREAMING_SNAKE_CASE				    =		broadcast_to_shape_from_left(lowerCAmelCase__  ,  sample.shape)
      SCREAMING_SNAKE_CASE				    =		drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      SCREAMING_SNAKE_CASE				    =		random.split(lowerCAmelCase__  ,  num=1)
      SCREAMING_SNAKE_CASE				    =		random.normal(key=lowerCAmelCase__  ,  shape=sample.shape)
      SCREAMING_SNAKE_CASE				    =		sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      SCREAMING_SNAKE_CASE				    =		prev_sample_mean + diffusion * noise  # add impact of diffusion field g

      if not return_dict:
       return (prev_sample, prev_sample_mean, state)

      return FlaxSdeVeOutput(prev_sample=lowerCAmelCase__  ,  prev_sample_mean=lowerCAmelCase__  ,  state=lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Dict  ,  lowerCAmelCase__					:	ScoreSdeVeSchedulerState  ,  lowerCAmelCase__					:	jnp.ndarray  ,  lowerCAmelCase__					:	jnp.ndarray  ,  lowerCAmelCase__					:	random.KeyArray  ,  lowerCAmelCase__					:	bool = True  ,  )							->	Union[FlaxSdeVeOutput, Tuple]:

      """simple docstring"""
      if state.timesteps is None:
       raise ValueError(
           '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler')

      # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
      # sample noise for correction
      SCREAMING_SNAKE_CASE				    =		random.split(lowerCAmelCase__  ,  num=1)
      SCREAMING_SNAKE_CASE				    =		random.normal(key=lowerCAmelCase__  ,  shape=sample.shape)

      # compute step size from the model_output, the noise, and the snr
      SCREAMING_SNAKE_CASE				    =		jnp.linalg.norm(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		jnp.linalg.norm(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		(self.config.snr * noise_norm / grad_norm) ** 2 * 2
      SCREAMING_SNAKE_CASE				    =		step_size * jnp.ones(sample.shape[0])

      # compute corrected sample: model_output term and noise term
      SCREAMING_SNAKE_CASE				    =		step_size.flatten()
      SCREAMING_SNAKE_CASE				    =		broadcast_to_shape_from_left(lowerCAmelCase__  ,  sample.shape)
      SCREAMING_SNAKE_CASE				    =		sample + step_size * model_output
      SCREAMING_SNAKE_CASE				    =		prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
       return (prev_sample, state)

      return FlaxSdeVeOutput(prev_sample=lowerCAmelCase__  ,  state=lowerCAmelCase__)





     def __len__(		self					:	Any)							->	List[Any]:

      """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


lowercase__						:Optional[Any]					    =       get_logger(__name__)



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



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=None					,     lowerCAmelCase__=None					,     **lowerCAmelCase__					,     ):
					'''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:
										lowercase		      =		'''cpu'''
					elif map_location == "on_device":
										lowercase		      =		PartialState().device
					# Model states
					for i, model in enumerate(lowerCAmelCase__						):
										lowercase		      =		f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										models[i].load_state_dict(torch.load(lowerCAmelCase__					,     map_location=lowerCAmelCase__						)					,     **lowerCAmelCase__						)
					logger.info('''All model weights loaded successfully'''						)

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

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

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

					# Random states
					try:
										lowercase		      =		torch.load(os.path.join(lowerCAmelCase__					,     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       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = 0						):
					'''simple docstring'''
					lowercase		      =		Path(lowerCAmelCase__						) / f'custom_checkpoint_{index}.pkl'
					logger.info(f'Saving the state of {get_pretty_name(lowerCAmelCase__						)} to {save_location}'						)
					torch.save(obj.state_dict()					,     lowerCAmelCase__						)



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = 0						):
					'''simple docstring'''
					lowercase		      =		f'{path}/custom_checkpoint_{index}.pkl'
					logger.info(f'Loading the state of {get_pretty_name(lowerCAmelCase__						)} from {load_location}'						)
					obj.load_state_dict(torch.load(lowerCAmelCase__					,     map_location='''cpu'''						)						)




