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
  
  __magic_name__	:   Union[str, Any]          =							logging.get_logger(__name__)
  
  
  
  
  
  def 			a_   (  ):
  			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  __snake_case      (_a     ):
  
  
  
  
  							def __init__(    self:						List[str]    ,       A_:						str      ):
  										# set global weakref object
  										set_hf_deepspeed_config(self      )
  										dep_version_check("""accelerate"""      )
  										dep_version_check("""deepspeed"""      )
  										super().__init__(lowerCamelCase__      )
  
  
  
  
  class  __snake_case      (_a     ):
  
  
  
  
  							def __init__(    self:						Tuple    ,       A_:						Optional[int]      ):
  										super().__init__(lowerCamelCase__      )
  										__lowerCamelCase        =			None
  										__lowerCamelCase        =			[]
  
  
  
  
  							def      __a      (    self:						Dict      ):
  										if self._dtype is None:
  													raise ValueError("""trainer_config_process() wasn't called yet to tell dtype"""      )
  										return self._dtype
  
  
  
  
  							def      __a      (    self:						List[str]    ,       A_:						Optional[int]      ):
  										__lowerCamelCase        =			self.get_value(lowerCamelCase__      )
  										if val is None:
  													return False
  										else:
  													return val == "auto"
  
  
  
  
  							def      __a      (    self:						List[Any]    ,       A_:						List[str]    ,       A_:						Dict    ,       A_:						str=None    ,       A_:						Optional[Any]=True      ):
  										__lowerCamelCase        =			self.find_config_node(lowerCamelCase__      )
  										if config is None:
  													return
  
  										if config.get(lowerCamelCase__      ) == "auto":
  													__lowerCamelCase        =			hf_val
  													return
  
  										if not must_match:
  													return
  
  										__lowerCamelCase        =			config.get(lowerCamelCase__      )
  										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}'      )
  
  							__a				   =					partialmethod(_a ,		must_match=_a     )
  
  
  
  
  							def      __a      (    self:						Any    ,       A_:						List[Any]      ):
  										__lowerCamelCase        =			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"""    ,       lowerCamelCase__    ,       """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:
  													__lowerCamelCase        =			"apex" if args.fpaa_backend == "apex" else "amp"
  										else:
  													__lowerCamelCase        =			None
  
  										if args.save_on_each_node:
  													# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
  													__lowerCamelCase        =			self.config.get("""checkpoint"""    ,       {}      )
  													__lowerCamelCase        =			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"""      ):
  													__lowerCamelCase        =			torch.bfloataa
  										elif self.is_false("""fp16.enabled"""      ):
  													__lowerCamelCase        =			torch.floataa
  										else:
  													__lowerCamelCase        =			torch.floataa
  
  
  
  
  							def      __a      (    self:						Tuple    ,       A_:						Optional[int]    ,       A_:						Optional[Any]    ,       A_:						List[Any]      ):
  										__lowerCamelCase        =			[
  										    "zero_optimization.reduce_bucket_size",
  										    "zero_optimization.stage3_prefetch_bucket_size",
  										    "zero_optimization.stage3_param_persistence_threshold",
  										]
  										__lowerCamelCase        =			[x for x in hidden_size_based_keys if self.is_auto(lowerCamelCase__      )]
  
  										if len(lowerCamelCase__      ) > 0:
  													if hasattr(model.config    ,       """hidden_size"""      ):
  																__lowerCamelCase        =			model.config.hidden_size
  													elif hasattr(model.config    ,       """hidden_sizes"""      ):
  																# if there are many hidden sizes pick the largest one
  																__lowerCamelCase        =			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"""    ,       lowerCamelCase__    ,       """num_training_steps (calculated)"""      )
  										self.fill_match("""scheduler.params.warmup_num_steps"""    ,       args.get_warmup_steps(lowerCamelCase__      )    ,       """warmup_steps"""      )
  
  										if len(self.mismatches      ) > 0:
  													__lowerCamelCase        =			"\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\'.'      )
  
  __magic_name__	:   Optional[int]          =							None
  
  
  
  
  
  def 			a_   (  lowercase__   :List[str]    ):
  			# this is a special weakref global object to allow us to get to Deepspeed config from APIs
  			# that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
  			global _hf_deepspeed_config_weak_ref
  			# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
  			__lowerCamelCase        =			weakref.ref(lowercase__    )
  
  
  
  
  
  def 			a_   (  ):
  			# useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
  			global _hf_deepspeed_config_weak_ref
  			__lowerCamelCase        =			None
  
  
  
  
  
  def 			a_   (  ):
  			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 			a_   (  ):
  			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 			a_   (  lowercase__   :Union[str, Any], lowercase__   :int, lowercase__   :int, lowercase__   :Dict, lowercase__   :Tuple    ):
  			from accelerate.utils import DummyOptim, DummyScheduler
  
  			__lowerCamelCase        =			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)
  
  			__lowerCamelCase        =			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."""    )
  						__lowerCamelCase        =			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.
  						__lowerCamelCase        =			trainer.create_optimizer()
  						# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
  						__lowerCamelCase        =			True
  
  			__lowerCamelCase        =			None
  			if "scheduler" in config:
  						__lowerCamelCase        =			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."""    )
  						__lowerCamelCase        =			trainer.create_scheduler(num_training_steps=lowercase__, optimizer=lowercase__    )
  
  			return optimizer, lr_scheduler
  
  
  
  
  
  def 			a_   (  lowercase__   :Any, lowercase__   :Any, lowercase__   :Union[str, Any]=False    ):
  			from deepspeed.utils import logger as ds_logger
  
  			__lowerCamelCase        =			trainer.model
  			__lowerCamelCase        =			trainer.args
  
  			__lowerCamelCase        =			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"""    )
  						__lowerCamelCase        =			None, None
  						__lowerCamelCase        =			None
  			else:
  						__lowerCamelCase        =			None  # important for when deepspeed_init is used as re-init
  						__lowerCamelCase        =			list(filter(lambda lowercase__    : p.requires_grad, model.parameters()    )    )
  						__lowerCamelCase        =			deepspeed_optim_sched(
  						    lowercase__, lowercase__, lowercase__, lowercase__, lowercase__    )
  
  			# keep for quick debug:
  			# from pprint import pprint; pprint(config)
  
  			return optimizer, lr_scheduler
  
  
  
  
  
  def 			a_   (  lowercase__   :List[Any], lowercase__   :Optional[Any]    ):
  			# it's possible that the user is trying to resume from model_path, which doesn't necessarily
  			# contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
  			# a resume from a checkpoint and not just a local pretrained weight. So we check here if the
  			# path contains what looks like a deepspeed checkpoint
  			import glob
  
  			__lowerCamelCase        =			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
  						__lowerCamelCase        =			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}'    )
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import unittest
  
  from transformers import load_tool
  from transformers.tools.agent_types import AGENT_TYPE_MAPPING
  
  from .test_tools_common import ToolTesterMixin, output_types
  
  
  
  
  class  __snake_case      (unittest.TestCase ,		lowerCamelCase     ):
  
  
  
  
  							def      __a      (    self:						Optional[Any]      ):
  										__lowerCamelCase        =			load_tool("""translation"""      )
  										self.tool.setup()
  										__lowerCamelCase        =			load_tool("""translation"""    ,       remote=A_      )
  
  
  
  
  							def      __a      (    self:						int      ):
  										__lowerCamelCase        =			self.tool("""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						int      ):
  										__lowerCamelCase        =			self.remote_tool("""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						Union[str, Any]      ):
  										__lowerCamelCase        =			self.tool(text="""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						Any      ):
  										__lowerCamelCase        =			self.remote_tool(text="""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						List[Any]      ):
  										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]
  										__lowerCamelCase        =			self.tool(*A_      )
  
  										# There is a single output
  										if len(self.tool.outputs      ) == 1:
  													__lowerCamelCase        =			[outputs]
  
  										self.assertListEqual(output_types(A_      )    ,       self.tool.outputs      )
  
  
  
  
  							def      __a      (    self:						Union[str, Any]      ):
  										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]
  										__lowerCamelCase        =			self.tool(*A_      )
  
  										if not isinstance(A_    ,       A_      ):
  													__lowerCamelCase        =			[outputs]
  
  										self.assertEqual(len(A_      )    ,       len(self.tool.outputs      )      )
  
  										for output, output_type in zip(A_    ,       self.tool.outputs      ):
  													__lowerCamelCase        =			AGENT_TYPE_MAPPING[output_type]
  													self.assertTrue(isinstance(A_    ,       A_      )      )
  
  
  
  
  							def      __a      (    self:						Dict      ):
  										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]
  
  										__lowerCamelCase        =			[]
  
  										for _input, input_type in zip(A_    ,       self.tool.inputs      ):
  													if isinstance(A_    ,       A_      ):
  																_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input      ) for _input_type in input_type]      )
  													else:
  																_inputs.append(AGENT_TYPE_MAPPING[input_type](_input      )      )
  
          # Should not raise an error
  										__lowerCamelCase        =			self.tool(*A_      )
  
  										if not isinstance(A_    ,       A_      ):
  													__lowerCamelCase        =			[outputs]
  
  										self.assertEqual(len(A_      )    ,       len(self.tool.outputs      )      )
  
  
  
  
  