code: 
  
  
  '''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
  
  __lowercase:		List[Any]							=     logging.get_logger(__name__)
  
  
  
  def        SCREAMING_SNAKE_CASE__(		)		->					Dict:
  
  
  
  
  
  			'''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 (       SCREAMING_SNAKE_CASE__):
  
  	def __init__( self :	Optional[Any],					a_ :	List[Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				set_hf_deepspeed_config(self					)
  				dep_version_check("accelerate"					)
  				dep_version_check("deepspeed"					)
  				super().__init__(a_					)
  
  
  
  
  
  
  
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
  
  	def __init__( self :	Any,					a_ :	int					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__(a_					)
  				UpperCamelCase__ 						=						None
  				UpperCamelCase__ 						=						[]
  
  	def 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if self._dtype is None:
  							raise ValueError("trainer_config_process() wasn't called yet to tell dtype"					)
  				return self._dtype
  
  	def 		lowercase_						( self :	Union[str, Any],					a_ :	Any					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						self.get_value(a_					)
  				if val is None:
  							return False
  				else:
  							return val == "auto"
  
  	def 		lowercase_						( self :	int,					a_ :	str,					a_ :	Optional[Any],					a_ :	Any=None,					a_ :	Union[str, Any]=True					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__						,		UpperCamelCase__ 						=						self.find_config_node(a_					)
  				if config is None:
  							return
  
  				if config.get(a_					) == "auto":
  							UpperCamelCase__ 						=						hf_val
  							return
  
  				if not must_match:
  							return
  
  				UpperCamelCase__ 						=						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}'					)
  
  	_lowerCamelCase :					Dict    					=					partialmethod(SCREAMING_SNAKE_CASE__		,			must_match=SCREAMING_SNAKE_CASE__)
  
  	def 		lowercase_						( self :	List[Any],					a_ :	Optional[int]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						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:
  							UpperCamelCase__ 						=						"apex" if args.fpaa_backend == "apex" else "amp"
  				else:
  							UpperCamelCase__ 						=						None
  
  				if args.save_on_each_node:
  							# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
  							UpperCamelCase__ 						=						self.config.get("checkpoint",					{}					)
  							UpperCamelCase__ 						=						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"					):
  							UpperCamelCase__ 						=						torch.bfloataa
  				elif self.is_false("fp16.enabled"					):
  							UpperCamelCase__ 						=						torch.floataa
  				else:
  							UpperCamelCase__ 						=						torch.floataa
  
  	def 		lowercase_						( self :	str,					a_ :	Tuple,					a_ :	Tuple,					a_ :	int					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						[
  				    "zero_optimization.reduce_bucket_size",
  				    "zero_optimization.stage3_prefetch_bucket_size",
  				    "zero_optimization.stage3_param_persistence_threshold",
  				]
  				UpperCamelCase__ 						=						[x for x in hidden_size_based_keys if self.is_auto(a_					)]
  
  				if len(a_					) > 0:
  							if hasattr(model.config,					"hidden_size"					):
  										UpperCamelCase__ 						=						model.config.hidden_size
  							elif hasattr(model.config,					"hidden_sizes"					):
  										# if there are many hidden sizes pick the largest one
  										UpperCamelCase__ 						=						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:
  							UpperCamelCase__ 						=						"\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:		Optional[int]							=     None
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				str   )		->					Tuple:
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			global _hf_deepspeed_config_weak_ref
  			# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
  			UpperCamelCase__ 						=						weakref.ref(_UpperCamelCase   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		)		->					Dict:
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			global _hf_deepspeed_config_weak_ref
  			UpperCamelCase__ 						=						None
  
  
  
  def        SCREAMING_SNAKE_CASE__(		)		->					List[Any]:
  
  
  
  
  
  			'''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        SCREAMING_SNAKE_CASE__(		)		->					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().config
  			else:
  						return None
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Any    ,		_UpperCamelCase      :				Any    ,		_UpperCamelCase      :				Optional[int]    ,		_UpperCamelCase      :				Any    ,		_UpperCamelCase      :				Optional[int]   )		->					str:
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			from accelerate.utils import DummyOptim, DummyScheduler
  
  			UpperCamelCase__ 						=						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)
  
  			UpperCamelCase__ 						=						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."   )
  						UpperCamelCase__ 						=						DummyOptim(params=_UpperCamelCase   )
  			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.
  						UpperCamelCase__ 						=						trainer.create_optimizer()
  						# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
  						UpperCamelCase__ 						=						True
  
  			UpperCamelCase__ 						=						None
  			if "scheduler" in config:
  						UpperCamelCase__ 						=						DummyScheduler(_UpperCamelCase   )
  			else:
  						if isinstance(_UpperCamelCase    ,		_UpperCamelCase   ):
  									raise ValueError(
  									    "Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. "
  									    "Please configure a scheduler in the DeepSpeed config."   )
  						UpperCamelCase__ 						=						trainer.create_scheduler(num_training_steps=_UpperCamelCase    ,		optimizer=_UpperCamelCase   )
  
  			return optimizer, lr_scheduler
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Tuple    ,		_UpperCamelCase      :				Dict    ,		_UpperCamelCase      :				str=False   )		->					List[Any]:
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			from deepspeed.utils import logger as ds_logger
  
  			UpperCamelCase__ 						=						trainer.model
  			UpperCamelCase__ 						=						trainer.args
  
  			UpperCamelCase__ 						=						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(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
  
  			# 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"   )
  						UpperCamelCase__						,		UpperCamelCase__ 						=						None, None
  						UpperCamelCase__ 						=						None
  			else:
  						UpperCamelCase__ 						=						None  # important for when deepspeed_init is used as re-init
  						UpperCamelCase__ 						=						list(filter(lambda _UpperCamelCase   : p.requires_grad    ,		model.parameters()   )   )
  						UpperCamelCase__						,		UpperCamelCase__ 						=						deepspeed_optim_sched(
  						    _UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
  
  			# keep for quick debug:
  			# from pprint import pprint; pprint(config)
  
  			return optimizer, lr_scheduler
  
  
  
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				List[Any]    ,		_UpperCamelCase      :				Any   )		->					Dict:
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			import glob
  
  			UpperCamelCase__ 						=						sorted(glob.glob(F'{checkpoint_path}/global_step*'   )   )
  
  			if len(_UpperCamelCase   ) > 0:
  						logger.info(F'Attempting to resume from {checkpoint_path}'   )
  						# this magically updates self.optimizer and self.lr_scheduler
  						UpperCamelCase__						,		UpperCamelCase__ 						=						deepspeed_engine.load_checkpoint(
  						    _UpperCamelCase    ,		load_optimizer_states=_UpperCamelCase    ,		load_lr_scheduler_states=_UpperCamelCase   )
  						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}'   )
style_context: 
  
  
  '''simple docstring'''
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
         import torch
  
  
  
  
  
  
  
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
   _lowerCamelCase :					Tuple    					=					['image_processor', 'tokenizer']
   _lowerCamelCase :					Tuple    					=					'OneFormerImageProcessor'
   _lowerCamelCase :					List[Any]    					=					('CLIPTokenizer', 'CLIPTokenizerFast')
  
   def __init__( self :	Any,					a_ :	Dict=None,					a_ :	int=None,					a_ :	int = 77,					a_ :	int = 77,					**a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."					)
      if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."					)
  
      UpperCamelCase__ 						=						max_seq_length
      UpperCamelCase__ 						=						task_seq_length
  
      super().__init__(a_,					a_					)
  
   def 		lowercase_						( self :	Any,					a_ :	Optional[Any]=None,					a_ :	Optional[int]=77					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if text_list is None:
         raise ValueError("tokens cannot be None."					)
  
      UpperCamelCase__ 						=						self.tokenizer(a_,					padding="max_length",					max_length=a_,					truncation=a_					)
  
      UpperCamelCase__						,		UpperCamelCase__ 						=						tokens["attention_mask"], tokens["input_ids"]
  
      UpperCamelCase__ 						=						[]
      for attn_mask, input_id in zip(a_,					a_					):
         UpperCamelCase__ 						=						torch.tensor(a_					) * torch.tensor(a_					)
         token_inputs.append(token.unsqueeze(0					)					)
  
      UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
      return token_inputs
  
   def __call__( self :	str,					a_ :	Dict=None,					a_ :	str=None,					a_ :	Tuple=None,					**a_ :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if task_inputs is None:
         raise ValueError("You have to specify the task_input. Found None."					)
      elif images is None:
         raise ValueError("You have to specify the image. Found None."					)
  
      if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
         raise ValueError("task_inputs must be semantic, instance, or panoptic."					)
  
      UpperCamelCase__ 						=						self.image_processor(a_,					a_,					a_,					**a_					)
  
      if isinstance(a_,					a_					):
         UpperCamelCase__ 						=						[task_inputs]
  
      if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
         UpperCamelCase__ 						=						[]
         for task in task_inputs:
            UpperCamelCase__ 						=						f'the task is {task}'
            task_token_inputs.append(a_					)
         UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
      else:
         raise TypeError("Task Inputs should be a string or a list of strings."					)
  
      if hasattr(a_,					"text_inputs"					):
         UpperCamelCase__ 						=						encoded_inputs.text_inputs
  
         UpperCamelCase__ 						=						[]
         for texts in texts_list:
            UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
            text_inputs.append(text_input_list.unsqueeze(0					)					)
  
         UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
  
      return encoded_inputs
  
   def 		lowercase_						( self :	Union[str, Any],					a_ :	Tuple=None,					a_ :	List[str]=None,					a_ :	Optional[Any]=None,					**a_ :	Tuple					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if task_inputs is None:
         raise ValueError("You have to specify the task_input. Found None."					)
      elif images is None:
         raise ValueError("You have to specify the image. Found None."					)
  
      if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
         raise ValueError("task_inputs must be semantic, instance, or panoptic."					)
  
      UpperCamelCase__ 						=						self.image_processor.encode_inputs(a_,					a_,					a_,					**a_					)
  
      if isinstance(a_,					a_					):
         UpperCamelCase__ 						=						[task_inputs]
  
      if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
         UpperCamelCase__ 						=						[]
         for task in task_inputs:
            UpperCamelCase__ 						=						f'the task is {task}'
            task_token_inputs.append(a_					)
         UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
      else:
         raise TypeError("Task Inputs should be a string or a list of strings."					)
  
      if hasattr(a_,					"text_inputs"					):
         UpperCamelCase__ 						=						encoded_inputs.text_inputs
  
         UpperCamelCase__ 						=						[]
         for texts in texts_list:
            UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
            text_inputs.append(text_input_list.unsqueeze(0					)					)
  
         UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
  
      return encoded_inputs
  
   def 		lowercase_						( self :	Any,					*a_ :	int,					**a_ :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_semantic_segmentation(*a_,					**a_					)
  
   def 		lowercase_						( self :	str,					*a_ :	Tuple,					**a_ :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_instance_segmentation(*a_,					**a_					)
  
   def 		lowercase_						( self :	Dict,					*a_ :	str,					**a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_panoptic_segmentation(*a_,					**a_					)