






'''simple docstring'''




import math
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 KarrasDiffusionSchedulers, SchedulerMixin


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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   snake_case					       =		42
   snake_case					       =		None





def       __lowercase		(    __lowercase     ,						__lowercase=0.999     ,						__lowercase="cosine"     ,						) ->		Dict:

      '''simple docstring'''


      if alpha_transform_type == "cosine":

            def alpha_bar_fn(__lowercase ):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(__lowercase ):
                  return math.exp(t * -12.0 )

      else:
            raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )

      _A      =			[]
      for i in range(__lowercase ):
            _A      =			i / num_diffusion_timesteps
            _A      =			(i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(__lowercase ) / alpha_bar_fn(__lowercase )     ,						__lowercase ) )
      return torch.tensor(__lowercase     ,						dtype=torch.floataa )


class      _UpperCAmelCase    (  snake_case_							,					snake_case_		):




   """simple docstring"""

   snake_case					       =		[e.name for e in KarrasDiffusionSchedulers]
   snake_case					       =		1

   @register_to_config
   def __init__(	self				:			Dict   ,   __UpperCAmelCase				:			int = 1000   ,   __UpperCAmelCase				:			float = 0.0001   ,   __UpperCAmelCase				:			float = 0.02   ,   __UpperCAmelCase				:			str = "linear"   ,   __UpperCAmelCase				:			Optional[Union[np.ndarray, List[float]]] = None   ,   __UpperCAmelCase				:			str = "epsilon"   ,   __UpperCAmelCase				:			str = "linear"   ,   __UpperCAmelCase				:			Optional[bool] = False   ,   __UpperCAmelCase				:			str = "linspace"   ,   __UpperCAmelCase				:			int = 0   ,   ):





         '''simple docstring'''





         if trained_betas is not None:
               _A      =			torch.tensor(__UpperCAmelCase   ,   dtype=torch.floataa	)
         elif beta_schedule == "linear":
               _A      =			torch.linspace(__UpperCAmelCase   ,   __UpperCAmelCase   ,   __UpperCAmelCase   ,   dtype=torch.floataa	)
         elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _A      =			(
                   torch.linspace(beta_start**0.5   ,   beta_end**0.5   ,   __UpperCAmelCase   ,   dtype=torch.floataa	) ** 2
               )
         elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _A      =			betas_for_alpha_bar(__UpperCAmelCase	)
         else:
               raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''	)

         _A      =			1.0 - self.betas
         _A      =			torch.cumprod(self.alphas   ,   dim=0	)

         _A      =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
         _A      =			np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
         _A      =			torch.from_numpy(__UpperCAmelCase	)

         # setable values
         _A      =			None
         _A      =			np.linspace(0   ,   num_train_timesteps - 1   ,   __UpperCAmelCase   ,   dtype=__UpperCAmelCase	)[::-1].copy()
         _A      =			torch.from_numpy(__UpperCAmelCase	)
         _A      =			False
         _A      =			use_karras_sigmas

   @property
   def 		lowerCAmelCase				(	self				:			Optional[int]	):





         '''simple docstring'''





         if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5

   def 		lowerCAmelCase				(	self				:			Optional[int]   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			Union[float, torch.FloatTensor]	):





         '''simple docstring'''





         if isinstance(__UpperCAmelCase   ,   torch.Tensor	):
               _A      =			timestep.to(self.timesteps.device	)
         _A      =			(self.timesteps == timestep).nonzero().item()
         _A      =			self.sigmas[step_index]

         _A      =			sample / ((sigma**2 + 1) ** 0.5)

         _A      =			True
         return sample

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





         '''simple docstring'''





         _A      =			num_inference_steps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
               _A      =			np.linspace(0   ,   self.config.num_train_timesteps - 1   ,   __UpperCAmelCase   ,   dtype=__UpperCAmelCase	)[
                   ::-1
               ].copy()
         elif self.config.timestep_spacing == "leading":
               _A      =			self.config.num_train_timesteps // self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               _A      =			(np.arange(0   ,   __UpperCAmelCase	) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase	)
               timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
               _A      =			self.config.num_train_timesteps / self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               _A      =			(np.arange(self.config.num_train_timesteps   ,   0   ,   -step_ratio	)).round().copy().astype(__UpperCAmelCase	)
               timesteps -= 1
         else:
               raise ValueError(
                   f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''	)

         _A      =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
         _A      =			np.log(__UpperCAmelCase	)

         if self.config.interpolation_type == "linear":
               _A      =			np.interp(__UpperCAmelCase   ,   np.arange(0   ,   len(__UpperCAmelCase	)	)   ,   __UpperCAmelCase	)
         elif self.config.interpolation_type == "log_linear":
               _A      =			torch.linspace(np.log(sigmas[-1]	)   ,   np.log(sigmas[0]	)   ,   num_inference_steps + 1	).exp()
         else:
               raise ValueError(
                   f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
                   " 'linear' or 'log_linear'"	)

         if self.use_karras_sigmas:
               _A      =			self._convert_to_karras(in_sigmas=__UpperCAmelCase   ,   num_inference_steps=self.num_inference_steps	)
               _A      =			np.array([self._sigma_to_t(__UpperCAmelCase   ,   __UpperCAmelCase	) for sigma in sigmas]	)

         _A      =			np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
         _A      =			torch.from_numpy(__UpperCAmelCase	).to(device=__UpperCAmelCase	)
         if str(__UpperCAmelCase	).startswith("mps"	):
               # mps does not support float64
               _A      =			torch.from_numpy(__UpperCAmelCase	).to(__UpperCAmelCase   ,   dtype=torch.floataa	)
         else:
               _A      =			torch.from_numpy(__UpperCAmelCase	).to(device=__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Union[str, Any]   ,   __UpperCAmelCase				:			Dict   ,   __UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





         _A      =			np.log(__UpperCAmelCase	)

         # get distribution
         _A      =			log_sigma - log_sigmas[:, np.newaxis]

         # get sigmas range
         _A      =			np.cumsum((dists >= 0)   ,   axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
         _A      =			low_idx + 1

         _A      =			log_sigmas[low_idx]
         _A      =			log_sigmas[high_idx]

         # interpolate sigmas
         _A      =			(low - log_sigma) / (low - high)
         _A      =			np.clip(__UpperCAmelCase   ,   0   ,   1	)

         # transform interpolation to time range
         _A      =			(1 - w) * low_idx + w * high_idx
         _A      =			t.reshape(sigma.shape	)
         return t

   def 		lowerCAmelCase				(	self				:			Dict   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





         _A      =			in_sigmas[-1].item()
         _A      =			in_sigmas[0].item()

         _A      =			7.0  # 7.0 is the value used in the paper
         _A      =			np.linspace(0   ,   1   ,   __UpperCAmelCase	)
         _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 		lowerCAmelCase				(	self				:			str   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			Union[float, torch.FloatTensor]   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			float = 0.0   ,   __UpperCAmelCase				:			float = 0.0   ,   __UpperCAmelCase				:			float = float("inf"	)   ,   __UpperCAmelCase				:			float = 1.0   ,   __UpperCAmelCase				:			Optional[torch.Generator] = None   ,   __UpperCAmelCase				:			bool = True   ,   ):





         '''simple docstring'''





         if (
             isinstance(__UpperCAmelCase   ,   __UpperCAmelCase	)
             or isinstance(__UpperCAmelCase   ,   torch.IntTensor	)
             or isinstance(__UpperCAmelCase   ,   torch.LongTensor	)
         ):
               raise ValueError(
                   (
                       "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                       " `EulerDiscreteScheduler.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(__UpperCAmelCase   ,   torch.Tensor	):
               _A      =			timestep.to(self.timesteps.device	)

         _A      =			(self.timesteps == timestep).nonzero().item()
         _A      =			self.sigmas[step_index]

         _A      =			min(s_churn / (len(self.sigmas	) - 1)   ,   2**0.5 - 1	) if s_tmin <= sigma <= s_tmax else 0.0

         _A      =			randn_tensor(
             model_output.shape   ,   dtype=model_output.dtype   ,   device=model_output.device   ,   generator=__UpperCAmelCase	)

         _A      =			noise * s_noise
         _A      =			sigma * (gamma + 1)

         if gamma > 0:
               _A      =			sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         # NOTE: "original_sample" should not be an expected prediction_type but is left in for
         # backwards compatibility
         if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
               _A      =			model_output
         elif self.config.prediction_type == "epsilon":
               _A      =			sample - sigma_hat * model_output
         elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               _A      =			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         else:
               raise ValueError(
                   f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''	)

         # 2. Convert to an ODE derivative
         _A      =			(sample - pred_original_sample) / sigma_hat

         _A      =			self.sigmas[step_index + 1] - sigma_hat

         _A      =			sample + derivative * dt

         if not return_dict:
               return (prev_sample,)

         return EulerDiscreteSchedulerOutput(prev_sample=__UpperCAmelCase   ,   pred_original_sample=__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			str   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			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(__UpperCAmelCase	):
               # 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				:			Optional[int]	):





         '''simple docstring'''





         return self.config.num_train_timesteps











'''simple docstring'''




import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
    import torch

lowerCamelCase_							      =      logging.get_logger(__name__)

def       __lowercase		(    ) ->		Optional[Any]:

      '''simple docstring'''


      return importlib.util.find_spec("deepspeed" ) is not None


if is_accelerate_available() and is_deepspeed_available():
    from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
    # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
    # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
    from builtins import object as DeepSpeedConfig

class      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   def __init__(	self				:			Dict   ,   __UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





         set_hf_deepspeed_config(self	)
         dep_version_check("accelerate"	)
         dep_version_check("deepspeed"	)
         super().__init__(__UpperCAmelCase	)





class      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   def __init__(	self				:			List[str]   ,   __UpperCAmelCase				:			List[str]	):





         '''simple docstring'''





         super().__init__(__UpperCAmelCase	)
         _A      =			None
         _A      =			[]

   def 		lowerCAmelCase				(	self				:			Optional[int]	):





         '''simple docstring'''





         if self._dtype is None:
               raise ValueError("trainer_config_process() wasn't called yet to tell dtype"	)
         return self._dtype

   def 		lowerCAmelCase				(	self				:			Dict   ,   __UpperCAmelCase				:			Dict	):





         '''simple docstring'''





         _A      =			self.get_value(__UpperCAmelCase	)
         if val is None:
               return False
         else:
               return val == "auto"

   def 		lowerCAmelCase				(	self				:			List[Any]   ,   __UpperCAmelCase				:			int   ,   __UpperCAmelCase				:			Optional[int]   ,   __UpperCAmelCase				:			Any=None   ,   __UpperCAmelCase				:			Union[str, Any]=True	):





         '''simple docstring'''





         _A  ,			_A      =			self.find_config_node(__UpperCAmelCase	)
         if config is None:
               return

         if config.get(__UpperCAmelCase	) == "auto":
               _A      =			hf_val
               return

         if not must_match:
               return

         _A      =			config.get(__UpperCAmelCase	)
         if ds_val is not None and ds_val != hf_val:
               self.mismatches.append(f'''- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}'''	)

   snake_case					       =		partialmethod(snake_case_							,					must_match=snake_case_		)

   def 		lowerCAmelCase				(	self				:			str   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         _A      =			args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
         self.fill_match(
             "train_micro_batch_size_per_gpu"   ,   args.per_device_train_batch_size   ,   "per_device_train_batch_size"	)
         self.fill_match("gradient_accumulation_steps"   ,   args.gradient_accumulation_steps   ,   "gradient_accumulation_steps"	)
         self.fill_match("train_batch_size"   ,   __UpperCAmelCase   ,   "train_batch_size (calculated)"	)
         self.fill_match("gradient_clipping"   ,   args.max_grad_norm   ,   "max_grad_norm"	)

         self.fill_match("optimizer.params.lr"   ,   args.learning_rate   ,   "learning_rate"	)
         self.fill_match("optimizer.params.betas"   ,   [args.adam_betaa, args.adam_betaa]   ,   "adam_beta1+adam_beta2"	)
         self.fill_match("optimizer.params.eps"   ,   args.adam_epsilon   ,   "adam_epsilon"	)
         self.fill_match("optimizer.params.weight_decay"   ,   args.weight_decay   ,   "weight_decay"	)

         self.fill_only("scheduler.params.warmup_min_lr"   ,   0	)  # not a trainer arg
         self.fill_match("scheduler.params.warmup_max_lr"   ,   args.learning_rate   ,   "learning_rate"	)
         # total_num_steps - will get set in trainer_config_finalize

         # fp16
         if args.fpaa or args.fpaa_full_eval:
               _A      =			"apex" if args.fpaa_backend == "apex" else "amp"
         else:
               _A      =			None

         if args.save_on_each_node:
               # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
               _A      =			self.config.get("checkpoint"   ,   {}	)
               _A      =			args.save_on_each_node

         # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
         # any here unless the user did the work
         self.fill_match(
             "fp16.enabled"   ,   ((args.fpaa or args.fpaa_full_eval) and fpaa_backend == "amp")   ,   "fp16|fp16_full_eval+fp16_backend(amp)"   ,   )

         # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
         # ZeRO features
         self.fill_match("amp.enabled"   ,   fpaa_backend == "apex"   ,   "fp16+fp16_backend(apex)"	)
         self.fill_match("amp.opt_level"   ,   args.fpaa_opt_level   ,   "fp16_opt_level"	)

         self.fill_match("bf16.enabled"   ,   (args.bfaa or args.bfaa_full_eval)   ,   "bf16|bf16_full_eval"	)

         # deepspeed's default mode is fp16 unless there is a config that says differently
         if self.is_true("bf16.enabled"	):
               _A      =			torch.bfloataa
         elif self.is_false("fp16.enabled"	):
               _A      =			torch.floataa
         else:
               _A      =			torch.floataa

   def 		lowerCAmelCase				(	self				:			Optional[int]   ,   __UpperCAmelCase				:			Dict   ,   __UpperCAmelCase				:			str   ,   __UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





         _A      =			[
             "zero_optimization.reduce_bucket_size",
             "zero_optimization.stage3_prefetch_bucket_size",
             "zero_optimization.stage3_param_persistence_threshold",
         ]
         _A      =			[x for x in hidden_size_based_keys if self.is_auto(__UpperCAmelCase	)]

         if len(__UpperCAmelCase	) > 0:
               if hasattr(model.config   ,   "hidden_size"	):
                     _A      =			model.config.hidden_size
               elif hasattr(model.config   ,   "hidden_sizes"	):
                     # if there are many hidden sizes pick the largest one
                     _A      =			max(model.config.hidden_sizes	)
               else:
                     raise ValueError(
                         "The model's config file has neither `hidden_size` nor `hidden_sizes` entry, "
                         "therefore it's not possible to automatically fill out the following `auto` entries "
                         f'''in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing '''
                         "`auto` values for these keys with an integer value of your choice."	)

               self.fill_only("zero_optimization.reduce_bucket_size"   ,   hidden_size * hidden_size	)
               if self.is_zeroa():
                     # automatically assign the optimal config values based on model config
                     self.fill_only("zero_optimization.stage3_prefetch_bucket_size"   ,   0.9 * hidden_size * hidden_size	)
                     self.fill_only("zero_optimization.stage3_param_persistence_threshold"   ,   10 * hidden_size	)

        # scheduler
         self.fill_match("scheduler.params.total_num_steps"   ,   __UpperCAmelCase   ,   "num_training_steps (calculated)"	)
         self.fill_match("scheduler.params.warmup_num_steps"   ,   args.get_warmup_steps(__UpperCAmelCase	)   ,   "warmup_steps"	)

         if len(self.mismatches	) > 0:
               _A      =			"\n".join(self.mismatches	)
               raise ValueError(
                   "Please correct the following DeepSpeed config values that mismatch TrainingArguments"
                   f''' values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to \'auto\'.'''	)





lowerCamelCase_							      =      None

def       __lowercase		(    __lowercase ) ->		Optional[Any]:

      '''simple docstring'''


      global _hf_deepspeed_config_weak_ref
      # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
      _A      =			weakref.ref(__lowercase )

def       __lowercase		(    ) ->		str:

      '''simple docstring'''


      global _hf_deepspeed_config_weak_ref
      _A      =			None

def       __lowercase		(    ) ->		Optional[int]:

      '''simple docstring'''


      if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
            return _hf_deepspeed_config_weak_ref().is_zeroa()
      else:
            return False

def       __lowercase		(    ) ->		str:

      '''simple docstring'''


      if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
            return _hf_deepspeed_config_weak_ref().config
      else:
            return None

def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase ) ->		int:

      '''simple docstring'''


      from accelerate.utils import DummyOptim, DummyScheduler

      _A      =			hf_deepspeed_config.config

      # Optimizer + Scheduler
      # Currently supported combos:
      # 1. DS scheduler + DS optimizer: Yes
      # 2. HF scheduler + HF optimizer: Yes
      # 3. DS scheduler + HF optimizer: Yes
      # 4. HF scheduler + DS optimizer: No
      #
      # Unless Offload is enabled in which case it's:
      # 1. DS scheduler + DS optimizer: Yes
      # 2. HF scheduler + HF optimizer: Mostly*
      # 3. DS scheduler + HF optimizer: Mostly*
      # 4. HF scheduler + DS optimizer: No
      #
      # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

      _A      =			None
      if "optimizer" in config:
            if args.adafactor:
                  raise ValueError(
                      "--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. "
                      "Only one optimizer can be configured." )
            _A      =			DummyOptim(params=__lowercase )
      else:
            if hf_deepspeed_config.is_offload():
                  logger.info(
                      "Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the"
                      " custom optimizer has both CPU and GPU implementation (except LAMB)" )

            # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
            # But trainer uses AdamW by default.
            _A      =			trainer.create_optimizer()
            # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
            _A      =			True

      _A      =			None
      if "scheduler" in config:
            _A      =			DummyScheduler(__lowercase )
      else:
            if isinstance(__lowercase     ,						__lowercase ):
                  raise ValueError(
                      "Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. "
                      "Please configure a scheduler in the DeepSpeed config." )
            _A      =			trainer.create_scheduler(num_training_steps=__lowercase     ,						optimizer=__lowercase )

      return optimizer, lr_scheduler

def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase=False ) ->		Tuple:

      '''simple docstring'''


      from deepspeed.utils import logger as ds_logger

      _A      =			trainer.model
      _A      =			trainer.args

      _A      =			trainer.accelerator.state.deepspeed_plugin.hf_ds_config

      # resume config update - some bits like `model` and `num_training_steps` only become available during train
      hf_deepspeed_config.trainer_config_finalize(__lowercase     ,						__lowercase     ,						__lowercase )

      # set the Deepspeed log level consistent with the Trainer
      ds_logger.setLevel(args.get_process_log_level() )

      if inference:
            # only Z3 makes sense for the inference
            if not hf_deepspeed_config.is_zeroa():
                  raise ValueError("ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config" )

            # in case the training config is re-used for inference
            hf_deepspeed_config.del_config_sub_tree("optimizer" )
            hf_deepspeed_config.del_config_sub_tree("lr_scheduler" )
            _A  ,			_A      =			None, None
            _A      =			None
      else:
            _A      =			None  # important for when deepspeed_init is used as re-init
            _A      =			list(filter(lambda __lowercase : p.requires_grad     ,						model.parameters() ) )
            _A  ,			_A      =			deepspeed_optim_sched(
                __lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase )

      # keep for quick debug:
      # from pprint import pprint; pprint(config)

      return optimizer, lr_scheduler


def       __lowercase		(    __lowercase     ,						__lowercase ) ->		Union[str, Any]:

      '''simple docstring'''


      import glob

      _A      =			sorted(glob.glob(F'''{checkpoint_path}/global_step*''' ) )

      if len(__lowercase ) > 0:
            logger.info(F'''Attempting to resume from {checkpoint_path}''' )
            # this magically updates self.optimizer and self.lr_scheduler
            _A  ,			_A      =			deepspeed_engine.load_checkpoint(
                __lowercase     ,						load_optimizer_states=__lowercase     ,						load_lr_scheduler_states=__lowercase )
            if load_path is None:
                  raise ValueError(F'''[deepspeed] failed to resume from checkpoint {checkpoint_path}''' )
      else:
            raise ValueError(F'''Can\'t find a valid checkpoint at {checkpoint_path}''' )





