



"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler


class    SCREAMING_SNAKE_CASE__ :

    def __init__(				self			:				Optional[Any] ,  lowerCAmelCase_			:				str):







      """simple docstring"""







      if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_):
        # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
        # modified it, it will not be accepted here again, since `auto` values would have been overridden
        lowercase_								= deepcopy(lowerCAmelCase_)
      elif os.path.exists(lowerCAmelCase_):
        with io.open(lowerCAmelCase_ ,  """r""" ,  encoding="""utf-8""") as f:
          lowercase_								= json.load(lowerCAmelCase_)
      else:
        try:
          lowercase_								= baseaa.urlsafe_baadecode(lowerCAmelCase_).decode("""utf-8""")
          lowercase_								= json.loads(lowerCAmelCase_)
        except (UnicodeDecodeError, AttributeError, ValueError):
          raise ValueError(
              F'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}''')

      lowercase_								= config

      self.set_stage_and_offload()

    def 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







      lowercase_								= self.get_value("""zero_optimization.stage""" ,  -1)

      # offload
      lowercase_								= False
      if self.is_zeroa() or self.is_zeroa():
        lowercase_								= set(["""cpu""", """nvme"""])
        lowercase_								= set(
            [
                self.get_value("""zero_optimization.offload_optimizer.device"""),
                self.get_value("""zero_optimization.offload_param.device"""),
            ])
        if len(offload_devices & offload_devices_valid) > 0:
          lowercase_								= True

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  lowerCAmelCase_			:				Any):







      """simple docstring"""







      lowercase_								= self.config

      # find the config node of interest if it exists
      lowercase_								= ds_key_long.split(""".""")
      lowercase_								= nodes.pop()
      for node in nodes:
        lowercase_								= config.get(lowerCAmelCase_)
        if config is None:
          return None, ds_key

      return config, ds_key

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				Dict ,  lowerCAmelCase_			:				Tuple=None):







      """simple docstring"""







      lowercase_					,    lowercase_								= self.find_config_node(lowerCAmelCase_)
      if config is None:
        return default
      return config.get(lowerCAmelCase_ ,  lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				Optional[Any] ,  lowerCAmelCase_			:				Optional[Any]=False):







      """simple docstring"""







      lowercase_								= self.config

      # find the config node of interest if it exists
      lowercase_								= ds_key_long.split(""".""")
      for node in nodes:
        lowercase_								= config
        lowercase_								= config.get(lowerCAmelCase_)
        if config is None:
          if must_exist:
            raise ValueError(F'''Can\'t find {ds_key_long} entry in the config: {self.config}''')
          else:
            return

        # if found remove it
      if parent_config is not None:
        parent_config.pop(lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				Tuple):







      """simple docstring"""







      lowercase_								= self.get_value(lowerCAmelCase_)
      return False if value is None else bool(lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				Tuple):







      """simple docstring"""







      lowercase_								= self.get_value(lowerCAmelCase_)
      return False if value is None else not bool(lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







      return self._stage == 2

    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      return self._stage == 3




    def 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







      return self._offload







class    SCREAMING_SNAKE_CASE__ :

    def __init__(				self			:				Any ,  lowerCAmelCase_			:				str):







      """simple docstring"""







      lowercase_								= engine




    def 					_UpperCAmelCase	(				self			:				Any ,  lowerCAmelCase_			:				str ,  **lowerCAmelCase_			:				str):







      """simple docstring"""







      self.engine.backward(lowerCAmelCase_ ,  **lowerCAmelCase_)

      # Deepspeed's `engine.step` performs the following operations:
      # - gradient accumulation check
      # - gradient clipping
      # - optimizer step
      # - zero grad
      # - checking overflow
      # - lr_scheduler step (only if engine.lr_scheduler is not None)
      self.engine.step()
      # and this plugin overrides the above calls with no-ops when Accelerate runs under
      # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
      # training loop that works transparently under many training regimes.







class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):

    def __init__(				self			:				List[Any] ,  lowerCAmelCase_			:				str):







      """simple docstring"""







      super().__init__(lowerCAmelCase_ ,  device_placement=lowerCAmelCase_ ,  scaler=lowerCAmelCase_)
      lowercase_								= hasattr(self.optimizer ,  """overflow""")

    def 					_UpperCAmelCase	(				self			:				Any ,  lowerCAmelCase_			:				List[Any]=None):







      """simple docstring"""







      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

    def 					_UpperCAmelCase	(				self			:				Tuple):







      """simple docstring"""







      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




    @property
    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







      if self.__has_overflow__:
        return self.optimizer.overflow
      return False







class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):

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







      """simple docstring"""







      super().__init__(lowerCAmelCase_ ,  lowerCAmelCase_)




    def 					_UpperCAmelCase	(				self			:				Tuple):







      """simple docstring"""







      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







class    SCREAMING_SNAKE_CASE__ :

    def __init__(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				Any ,  lowerCAmelCase_			:				Any=0.001 ,  lowerCAmelCase_			:				Optional[int]=0 ,  **lowerCAmelCase_			:				Optional[int]):







      """simple docstring"""







      lowercase_								= params
      lowercase_								= lr
      lowercase_								= weight_decay
      lowercase_								= kwargs







class    SCREAMING_SNAKE_CASE__ :

    def __init__(				self			:				str ,  lowerCAmelCase_			:				Dict ,  lowerCAmelCase_			:				str=None ,  lowerCAmelCase_			:				Dict=0 ,  **lowerCAmelCase_			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= optimizer
      lowercase_								= total_num_steps
      lowercase_								= warmup_num_steps
      lowercase_								= kwargs






"""simple docstring"""
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 ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class    SCREAMING_SNAKE_CASE__ :
    # setable values
    lowercase__      =   None
    lowercase__      =   None
    lowercase__      =   None  # sigma(t_i)

    @classmethod
    def 					_UpperCAmelCase	(				cls			:				Any):







      """simple docstring"""







      return cls()







@dataclass
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   42
    lowercase__      =   42
    lowercase__      =   42







class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  __UpperCAmelCase   ):

    @property
    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      return True

    @register_to_config
    def __init__(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				float = 0.02 ,  lowerCAmelCase_			:				float = 1_0_0 ,  lowerCAmelCase_			:				float = 1.007 ,  lowerCAmelCase_			:				float = 8_0 ,  lowerCAmelCase_			:				float = 0.05 ,  lowerCAmelCase_			:				float = 5_0 ,  ):







      """simple docstring"""







      pass

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      return KarrasVeSchedulerState.create()

    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Tuple = ()):







      """simple docstring"""







      lowercase_								= jnp.arange(0 ,  lowerCAmelCase_)[::-1].copy()
      lowercase_								= [
          (
              self.config.sigma_max**2
              * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
          )
          for i in timesteps
      ]

      return state.replace(
          num_inference_steps=lowerCAmelCase_ ,  schedule=jnp.array(lowerCAmelCase_ ,  dtype=jnp.floataa) ,  timesteps=lowerCAmelCase_ ,  )

    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				random.KeyArray ,  ):







      """simple docstring"""







      if self.config.s_min <= sigma <= self.config.s_max:
        lowercase_								= min(self.config.s_churn / state.num_inference_steps ,  2**0.5 - 1)
      else:
        lowercase_								= 0

      # sample eps ~ N(0, S_noise^2 * I)
      lowercase_								= random.split(lowerCAmelCase_ ,  num=1)
      lowercase_								= self.config.s_noise * random.normal(key=lowerCAmelCase_ ,  shape=sample.shape)
      lowercase_								= sigma + gamma * sigma
      lowercase_								= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

      return sample_hat, sigma_hat

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				bool = True ,  ):







      """simple docstring"""







      lowercase_								= sample_hat + sigma_hat * model_output
      lowercase_								= (sample_hat - pred_original_sample) / sigma_hat
      lowercase_								= sample_hat + (sigma_prev - sigma_hat) * derivative

      if not return_dict:
        return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_ ,  derivative=lowerCAmelCase_ ,  state=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				bool = True ,  ):







      """simple docstring"""







      lowercase_								= sample_prev + sigma_prev * model_output
      lowercase_								= (sample_prev - pred_original_sample) / sigma_prev
      lowercase_								= sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

      if not return_dict:
        return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_ ,  derivative=lowerCAmelCase_ ,  state=lowerCAmelCase_)




    def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      raise NotImplementedError()

