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       __snake_case :
 # setable values
 __lowerCamelCase     : Optional[jnp.ndarray] 	=   None
 __lowerCamelCase     : Optional[jnp.ndarray] 	=   None
 __lowerCamelCase     : Optional[jnp.ndarray] 	=   None
 @classmethod
 def 						UpperCAmelCase__							(	cls    )						->				List[str]:
       '''simple docstring'''





       return cls()







@dataclass
class       __snake_case (  lowerCamelCase__ ):
 __lowerCamelCase     : ScoreSdeVeSchedulerState
 __lowerCamelCase     : jnp.ndarray
 __lowerCamelCase     : Optional[jnp.ndarray] 	=   None







class       __snake_case (  lowerCamelCase__       , lowerCamelCase__ ):
 @property
 def 						UpperCAmelCase__							(	self    )						->				Any:
       '''simple docstring'''





       return True
 @register_to_config
 def __init__(	self		, snake_case__ = 2000		, snake_case__ = 0.15		, snake_case__ = 0.01		, snake_case__ = 1348.0		, snake_case__ = 1e-5		, snake_case__ = 1		, )						->				Union[str, Any]:
       '''simple docstring'''





       pass
 def 						UpperCAmelCase__							(	self    )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=ScoreSdeVeSchedulerState.create()
       return self.set_sigmas(
           snake_case__		, self.config.num_train_timesteps		, self.config.sigma_min		, self.config.sigma_max		, self.config.sampling_eps		, )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__ = ()		, snake_case__ = None    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			List[str]     	=sampling_eps if sampling_eps is not None else self.config.sampling_eps

       UpperCAmelCase     :			int     	=jnp.linspace(1		, snake_case__		, snake_case__    )
       return state.replace(timesteps=snake_case__    )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__ = None		, snake_case__ = None		, snake_case__ = None		, )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase     :			List[str]     	=sigma_min if sigma_min is not None else self.config.sigma_min
       UpperCAmelCase     :			List[str]     	=sigma_max if sigma_max is not None else self.config.sigma_max
       UpperCAmelCase     :			List[Any]     	=sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if state.timesteps is None:
             UpperCAmelCase     :			Dict     	=self.set_timesteps(snake_case__		, snake_case__		, snake_case__    )

       UpperCAmelCase     :			Optional[Any]     	=jnp.exp(jnp.linspace(jnp.log(snake_case__    )		, jnp.log(snake_case__    )		, snake_case__    )    )
       UpperCAmelCase     :			Optional[Any]     	=jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]    )

       return state.replace(discrete_sigmas=snake_case__		, sigmas=snake_case__    )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__    )						->				Optional[Any]:
       '''simple docstring'''





       return jnp.where(timesteps == 0		, jnp.zeros_like(snake_case__    )		, state.discrete_sigmas[timesteps - 1]    )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__ = True		, )						->				Dict:
       '''simple docstring'''





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

       UpperCAmelCase     :			Optional[Any]     	=timestep * jnp.ones(
           sample.shape[0]		, )
       UpperCAmelCase     :			str     	=(timestep * (len(state.timesteps    ) - 1)).long()

       UpperCAmelCase     :			List[Any]     	=state.discrete_sigmas[timesteps]
       UpperCAmelCase     :			Dict     	=self.get_adjacent_sigma(snake_case__		, snake_case__		, snake_case__    )
       UpperCAmelCase     :			List[Any]     	=jnp.zeros_like(snake_case__    )
       UpperCAmelCase     :			str     	=(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
       UpperCAmelCase     :			Optional[Any]     	=diffusion.flatten()
       UpperCAmelCase     :			List[Any]     	=broadcast_to_shape_from_left(snake_case__		, sample.shape    )
       UpperCAmelCase     :			str     	=drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       UpperCAmelCase     :			int     	=random.split(snake_case__		, num=1    )
       UpperCAmelCase     :			List[Any]     	=random.normal(key=snake_case__		, shape=sample.shape    )
       UpperCAmelCase     :			Dict     	=sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       UpperCAmelCase     :			List[str]     	=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=snake_case__		, prev_sample_mean=snake_case__		, state=snake_case__    )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__ = True		, )						->				Union[str, Any]:
       '''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
       UpperCAmelCase     :			Union[str, Any]     	=random.split(snake_case__		, num=1    )
       UpperCAmelCase     :			List[Any]     	=random.normal(key=snake_case__		, shape=sample.shape    )

       # compute step size from the model_output, the noise, and the snr
       UpperCAmelCase     :			Union[str, Any]     	=jnp.linalg.norm(snake_case__    )
       UpperCAmelCase     :			Union[str, Any]     	=jnp.linalg.norm(snake_case__    )
       UpperCAmelCase     :			List[Any]     	=(self.config.snr * noise_norm / grad_norm) ** 2 * 2
       UpperCAmelCase     :			List[Any]     	=step_size * jnp.ones(sample.shape[0]    )

       # compute corrected sample: model_output term and noise term
       UpperCAmelCase     :			Union[str, Any]     	=step_size.flatten()
       UpperCAmelCase     :			str     	=broadcast_to_shape_from_left(snake_case__		, sample.shape    )
       UpperCAmelCase     :			List[Any]     	=sample + step_size * model_output
       UpperCAmelCase     :			int     	=prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
             return (prev_sample, state)

       return FlaxSdeVeOutput(prev_sample=snake_case__		, state=snake_case__    )






 def __len__(	self    )						->				Tuple:
       '''simple docstring'''





       return self.config.num_train_timesteps

'''simple docstring'''
import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin







def 				__UpperCamelCase (					__UpperCAmelCase  :					List[Any]=2 ,   __UpperCAmelCase  :					Dict=True ,   __UpperCAmelCase  :					str=False			)							->	List[Any]:
  lowercase				:							Tuple									=   Accelerator(step_scheduler_with_optimizer=__UpperCAmelCase ,   split_batches=__UpperCAmelCase			)
  lowercase				:							Optional[int]									=   torch.nn.Linear(2 ,   4			)
  lowercase				:							Dict									=   torch.optim.AdamW(model.parameters() ,   lr=1.0			)
  lowercase				:							List[Any]									=   torch.optim.lr_scheduler.OneCycleLR(__UpperCAmelCase ,   max_lr=0.01 ,   steps_per_epoch=2 ,   epochs=1			)
  lowercase				,      lowercase				,      lowercase				:							Optional[Any]									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

  # Optimizer has stepped
  scheduler.step()
  if step_scheduler_with_optimizer or (num_processes == 1):
    assert (
        scheduler.scheduler.last_epoch == num_processes
    ), f"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
  else:
    assert (
        scheduler.scheduler.last_epoch != num_processes
    ), f"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""







def 				__UpperCamelCase (					__UpperCAmelCase  :					str=2 ,   __UpperCAmelCase  :					str=True ,   __UpperCAmelCase  :					Tuple=False			)							->	str:
  lowercase				:							List[Any]									=   Accelerator(step_scheduler_with_optimizer=__UpperCAmelCase ,   split_batches=__UpperCAmelCase			)
  lowercase				:							Optional[Any]									=   torch.nn.Linear(2 ,   4			)
  lowercase				:							List[str]									=   torch.optim.AdamW(model.parameters() ,   lr=1.0			)
  lowercase				:							Dict									=   torch.optim.lr_scheduler.LambdaLR(__UpperCAmelCase ,   lr_lambda=lambda __UpperCAmelCase			: 1 - n / 10			)
  lowercase				,      lowercase				,      lowercase				:							int									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

  # Optimizer has stepped
  lowercase				:							Any									=   False
  scheduler.step()
  lowercase				:							Optional[Any]									=   1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), f"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

  # Optimizer has not stepped
  lowercase				:							List[Any]									=   True
  scheduler.step()
  if not step_scheduler_with_optimizer:
    lowercase				:							List[Any]									=   1 - 2 / 10
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), f"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""







def 				__UpperCamelCase (					__UpperCAmelCase  :					int = 2			)							->	Dict:
  from transformers import get_linear_schedule_with_warmup

  lowercase				:							Optional[Any]									=   [1, 2, 4]
  for num_steps in steps:
    lowercase				:							Dict									=   GradientAccumulationPlugin(num_steps=__UpperCAmelCase ,   adjust_scheduler=num_steps > 1			)
    lowercase				:							Tuple									=   Accelerator(gradient_accumulation_plugin=__UpperCAmelCase			)
    lowercase				:							str									=   torch.nn.Linear(2 ,   4			)
    lowercase				:							Dict									=   torch.optim.AdamW(model.parameters() ,   lr=10.0			)
    lowercase				:							List[str]									=   get_linear_schedule_with_warmup(optimizer=__UpperCAmelCase ,   num_warmup_steps=0 ,   num_training_steps=20			)

    lowercase				,      lowercase				,      lowercase				:							Dict									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

    for i in range(10 * num_steps			):
      with accelerator.accumulate(__UpperCAmelCase			):
        optimizer.step()
        scheduler.step()

      if i == (10 * num_steps - 2):
        assert (
            scheduler.get_last_lr()[0] != 0
        ), f"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
    assert (
        scheduler.get_last_lr()[0] == 0
    ), f"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
    GradientState._reset_state()






@require_cpu
class _lowerCAmelCase			(	unittest.TestCase					):







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1     )  ,    num_processes=1     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1     )  ,    num_processes=1     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    step_scheduler_with_optimizer=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    step_scheduler_with_optimizer=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    step_scheduler_with_optimizer=lowercase     )     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    step_scheduler_with_optimizer=lowercase     )     )







      @require_huggingface_suite
      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1     )     )
        debug_launcher(lowercase     )