





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

__lowercase							:int							=	logging.get_logger(__name__)







def    UpperCAmelCase				(  ):





						'''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  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  List[Any]				,						a					:  Optional[Any]     )						->str:
													# set global weakref object
													set_hf_deepspeed_config(self     )
													dep_version_check("accelerate"     )
													dep_version_check("deepspeed"     )
													super().__init__(a     )







class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  Optional[Any]				,						a					:  Any     )						->List[Any]:
													super().__init__(a     )
													SCREAMING_SNAKE_CASE__  :					List[str] =       None
													SCREAMING_SNAKE_CASE__  :					int =       []

							def   A_	(   self					:  Optional[int]     )						->Dict:
													if self._dtype is None:
																			raise ValueError("trainer_config_process() wasn't called yet to tell dtype"     )
													return self._dtype

							def   A_	(   self					:  Optional[int]				,						a					:  str     )						->Dict:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self.get_value(a     )
													if val is None:
																			return False
													else:
																			return val == "auto"

							def   A_	(   self					:  Optional[int]				,						a					:  List[str]				,						a					:  Optional[int]				,						a					:  List[str]=None				,						a					:  Union[str, Any]=True     )						->Optional[int]:
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Tuple =       self.find_config_node(a     )
													if config is None:
																			return

													if config.get(a     ) == "auto":
																			SCREAMING_SNAKE_CASE__  :					List[str] =       hf_val
																			return

													if not must_match:
																			return

													SCREAMING_SNAKE_CASE__  :					int =       config.get(a     )
													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(lowercase__       ,				must_match=lowercase__				)

							def   A_	(   self					:  Any				,						a					:  Optional[int]     )						->Optional[int]:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       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"				,						a				,						"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:
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       "apex" if args.fpaa_backend == "apex" else "amp"
													else:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       None

													if args.save_on_each_node:
																			# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
																			SCREAMING_SNAKE_CASE__  :					Any =       self.config.get("checkpoint"				,						{}     )
																			SCREAMING_SNAKE_CASE__  :					Dict =       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"     ):
																			SCREAMING_SNAKE_CASE__  :					Dict =       torch.bfloataa
													elif self.is_false("fp16.enabled"     ):
																			SCREAMING_SNAKE_CASE__  :					Any =       torch.floataa
													else:
																			SCREAMING_SNAKE_CASE__  :					Dict =       torch.floataa






							def   A_	(   self					:  Optional[int]				,						a					:  List[Any]				,						a					:  Any				,						a					:  Any     )						->Any:
													SCREAMING_SNAKE_CASE__  :					str =       [
													    "zero_optimization.reduce_bucket_size",
													    "zero_optimization.stage3_prefetch_bucket_size",
													    "zero_optimization.stage3_param_persistence_threshold",
													]
													SCREAMING_SNAKE_CASE__  :					Dict =       [x for x in hidden_size_based_keys if self.is_auto(a     )]

													if len(a     ) > 0:
																			if hasattr(model.config				,						"hidden_size"     ):
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       model.config.hidden_size
																			elif hasattr(model.config				,						"hidden_sizes"     ):
																									# if there are many hidden sizes pick the largest one
																									SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       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"				,						a				,						"num_training_steps (calculated)"     )
													self.fill_match("scheduler.params.warmup_num_steps"				,						args.get_warmup_steps(a     )				,						"warmup_steps"     )

													if len(self.mismatches     ) > 0:
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       "\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'."""     )







__lowercase							:List[Any]							=	None







def    UpperCAmelCase				(  _lowerCamelCase   :	Tuple  ):





						'''simple docstring'''


						global _hf_deepspeed_config_weak_ref
						# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
						SCREAMING_SNAKE_CASE__  :					Dict =       weakref.ref(_lowerCamelCase  )







def    UpperCAmelCase				(  ):





						'''simple docstring'''


						global _hf_deepspeed_config_weak_ref
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       None







def    UpperCAmelCase				(  ):





						'''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    UpperCAmelCase				(  ):





						'''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    UpperCAmelCase				(  _lowerCamelCase   :	Any      , _lowerCamelCase   :	Tuple      , _lowerCamelCase   :	Dict      , _lowerCamelCase   :	List[str]      , _lowerCamelCase   :	str  ):





						'''simple docstring'''


						from accelerate.utils import DummyOptim, DummyScheduler

						SCREAMING_SNAKE_CASE__  :					Dict =       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)

						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       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."  )
												SCREAMING_SNAKE_CASE__  :					List[str] =       DummyOptim(params=_lowerCamelCase  )
						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.
												SCREAMING_SNAKE_CASE__  :					int =       trainer.create_optimizer()
												# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
												SCREAMING_SNAKE_CASE__  :					int =       True

						SCREAMING_SNAKE_CASE__  :					List[Any] =       None
						if "scheduler" in config:
												SCREAMING_SNAKE_CASE__  :					Dict =       DummyScheduler(_lowerCamelCase  )
						else:
												if isinstance(_lowerCamelCase      , _lowerCamelCase  ):
																		raise ValueError(
																		    "Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. "
																		    "Please configure a scheduler in the DeepSpeed config."  )
												SCREAMING_SNAKE_CASE__  :					List[Any] =       trainer.create_scheduler(num_training_steps=_lowerCamelCase      , optimizer=_lowerCamelCase  )

						return optimizer, lr_scheduler







def    UpperCAmelCase				(  _lowerCamelCase   :	Dict      , _lowerCamelCase   :	Union[str, Any]      , _lowerCamelCase   :	Any=False  ):





						'''simple docstring'''


						from deepspeed.utils import logger as ds_logger

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       trainer.model
						SCREAMING_SNAKE_CASE__  :					Tuple =       trainer.args

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       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(_lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase  )

						# 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"  )
												SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       None, None
												SCREAMING_SNAKE_CASE__  :					int =       None
						else:
												SCREAMING_SNAKE_CASE__  :					int =       None  # important for when deepspeed_init is used as re-init
												SCREAMING_SNAKE_CASE__  :					List[Any] =       list(filter(lambda _lowerCamelCase  : p.requires_grad      , model.parameters()  )  )
												SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Tuple =       deepspeed_optim_sched(
												    _lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase  )

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

						return optimizer, lr_scheduler







def    UpperCAmelCase				(  _lowerCamelCase   :	Dict      , _lowerCamelCase   :	Optional[int]  ):





						'''simple docstring'''


						import glob

						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       sorted(glob.glob(f"""{checkpoint_path}/global_step*"""  )  )

						if len(_lowerCamelCase  ) > 0:
												logger.info(f"""Attempting to resume from {checkpoint_path}"""  )
												# this magically updates self.optimizer and self.lr_scheduler
												SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       deepspeed_engine.load_checkpoint(
												    _lowerCamelCase      , load_optimizer_states=_lowerCamelCase      , load_lr_scheduler_states=_lowerCamelCase  )
												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}"""  )






from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__lowercase							:Optional[int]							=	logging.get_logger(__name__)



class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    ["input_values", "attention_mask"]

							def __init__(   self					:  List[str]				,						a					:  Dict=1				,						a					:  Tuple=1_60_00				,						a					:  Union[str, Any]=0.0				,						a					:  Dict=False				,						a					:  List[Any]=True				,						**a					:  Any				,						)						->Optional[int]:
													super().__init__(feature_size=a				,						sampling_rate=a				,						padding_value=a				,						**a     )
													SCREAMING_SNAKE_CASE__  :					int =       return_attention_mask
													SCREAMING_SNAKE_CASE__  :					str =       do_normalize

							@staticmethod
							def   A_	(   a					:  List[np.ndarray]				,						a					:  List[np.ndarray]				,						a					:  float = 0.0     )						->List[np.ndarray]:
													if attention_mask is not None:
																			SCREAMING_SNAKE_CASE__  :					int =       np.array(a				,						np.intaa     )
																			SCREAMING_SNAKE_CASE__  :					List[str] =       []

																			for vector, length in zip(a				,						attention_mask.sum(-1     )     ):
																									SCREAMING_SNAKE_CASE__  :					List[str] =       (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7     )
																									if length < normed_slice.shape[0]:
																															SCREAMING_SNAKE_CASE__  :					Tuple =       padding_value

																									normed_input_values.append(a     )
													else:
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       [(x - x.mean()) / np.sqrt(x.var() + 1E-7     ) for x in input_values]

													return normed_input_values






							def __call__(   self					:  Union[str, Any]				,						a					:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,						a					:  Union[bool, str, PaddingStrategy] = False				,						a					:  Optional[int] = None				,						a					:  bool = False				,						a					:  Optional[int] = None				,						a					:  Optional[bool] = None				,						a					:  Optional[Union[str, TensorType]] = None				,						a					:  Optional[int] = None				,						**a					:  Optional[Any]				,						)						->BatchFeature:

													if sampling_rate is not None:
																			if sampling_rate != self.sampling_rate:
																									raise ValueError(
																									    f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
																									    f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
																									    f""" {self.sampling_rate} and not {sampling_rate}."""     )
													else:
																			logger.warning(
																			    "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
																			    "Failing to do so can result in silent errors that might be hard to debug."     )

													SCREAMING_SNAKE_CASE__  :					List[Any] =       isinstance(a				,						np.ndarray     ) and len(raw_speech.shape     ) > 1
													if is_batched_numpy and len(raw_speech.shape     ) > 2:
																			raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""     )
													SCREAMING_SNAKE_CASE__  :					List[Any] =       is_batched_numpy or (
													    isinstance(a				,						(list, tuple)     ) and (isinstance(raw_speech[0]				,						(np.ndarray, tuple, list)     ))
													)

													# always return batch
													if not is_batched:
																			SCREAMING_SNAKE_CASE__  :					Any =       [raw_speech]

													# convert into correct format for padding
													SCREAMING_SNAKE_CASE__  :					Any =       BatchFeature({"input_values": raw_speech}     )

													SCREAMING_SNAKE_CASE__  :					Tuple =       self.pad(
													    a				,						padding=a				,						max_length=a				,						truncation=a				,						pad_to_multiple_of=a				,						return_attention_mask=a				,						)

													# convert input values to correct format
													SCREAMING_SNAKE_CASE__  :					Any =       padded_inputs["input_values"]
													if not isinstance(input_values[0]				,						np.ndarray     ):
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       [np.asarray(a				,						dtype=np.floataa     ) for array in input_values]
													elif (
													    not isinstance(a				,						np.ndarray     )
													    and isinstance(input_values[0]				,						np.ndarray     )
													    and input_values[0].dtype is np.dtype(np.floataa     )
													):
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       [array.astype(np.floataa     ) for array in input_values]
													elif isinstance(a				,						np.ndarray     ) and input_values.dtype is np.dtype(np.floataa     ):
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       input_values.astype(np.floataa     )

													# convert attention_mask to correct format
													SCREAMING_SNAKE_CASE__  :					Dict =       padded_inputs.get("attention_mask"     )
													if attention_mask is not None:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       [np.asarray(a				,						dtype=np.intaa     ) for array in attention_mask]

													# zero-mean and unit-variance normalization
													if self.do_normalize:
																			SCREAMING_SNAKE_CASE__  :					int =       (
																			    attention_mask
																			    if self._get_padding_strategies(a				,						max_length=a     ) is not PaddingStrategy.DO_NOT_PAD
																			    else None
																			)
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.zero_mean_unit_var_norm(
																			    padded_inputs["input_values"]				,						attention_mask=a				,						padding_value=self.padding_value     )

													if return_tensors is not None:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       padded_inputs.convert_to_tensors(a     )

													return padded_inputs