





"""simple docstring"""




import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
		from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import PoolFormerImageProcessor




class 				_a		(   _lowerCAmelCase       ):






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




										'''simple docstring'''



										_UpperCamelCase    :      Any						     =					self.config_class(**self.inputs_dict							)
										self.parent.assertTrue(hasattr(lowerCAmelCase__,				'''hidden_sizes'''							)							)
										self.parent.assertTrue(hasattr(lowerCAmelCase__,				'''num_encoder_blocks'''							)							)




class 				_a		:






				def __init__(				self	:					Optional[int],				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					Optional[int]=1_3,				lowerCAmelCase__	:					List[str]=6_4,				lowerCAmelCase__	:					Tuple=3,				lowerCAmelCase__	:					str=4,				lowerCAmelCase__	:					Union[str, Any]=[2, 2, 2, 2],				lowerCAmelCase__	:					Any=[8, 4, 2, 1],				lowerCAmelCase__	:					Any=[1_6, 3_2, 6_4, 1_2_8],				lowerCAmelCase__	:					int=[1, 4, 8, 1_6],				lowerCAmelCase__	:					Optional[int]=False,				lowerCAmelCase__	:					int=True,				lowerCAmelCase__	:					str="gelu",				lowerCAmelCase__	:					Union[str, Any]=0.1,				lowerCAmelCase__	:					Optional[Any]=0.02,				lowerCAmelCase__	:					int=3,				lowerCAmelCase__	:					Union[str, Any]=None,				)			->   List[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					parent
										_UpperCamelCase    :      Any						     =					batch_size
										_UpperCamelCase    :      int						     =					image_size
										_UpperCamelCase    :      Tuple						     =					num_channels
										_UpperCamelCase    :      Optional[int]						     =					num_encoder_blocks
										_UpperCamelCase    :      List[str]						     =					sr_ratios
										_UpperCamelCase    :      List[Any]						     =					depths
										_UpperCamelCase    :      Optional[Any]						     =					hidden_sizes
										_UpperCamelCase    :      Dict						     =					downsampling_rates
										_UpperCamelCase    :      int						     =					is_training
										_UpperCamelCase    :      Tuple						     =					use_labels
										_UpperCamelCase    :      Tuple						     =					hidden_act
										_UpperCamelCase    :      Optional[Any]						     =					hidden_dropout_prob
										_UpperCamelCase    :      List[Any]						     =					initializer_range
										_UpperCamelCase    :      List[str]						     =					num_labels
										_UpperCamelCase    :      int						     =					scope






				def        snake_case			(				self	:					Dict							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

										_UpperCamelCase    :      Dict						     =					None
										if self.use_labels:
																_UpperCamelCase    :      Optional[Any]						     =					ids_tensor([self.batch_size, self.image_size, self.image_size],				self.num_labels							)

										_UpperCamelCase    :      Tuple						     =					PoolFormerConfig(
										    image_size=self.image_size,				num_channels=self.num_channels,				num_encoder_blocks=self.num_encoder_blocks,				depths=self.depths,				hidden_sizes=self.hidden_sizes,				hidden_act=self.hidden_act,				hidden_dropout_prob=self.hidden_dropout_prob,				initializer_range=self.initializer_range,				)

										return config, pixel_values, labels






				def        snake_case			(				self	:					int,				lowerCAmelCase__	:					Dict,				lowerCAmelCase__	:					Dict,				lowerCAmelCase__	:					Optional[Any]							)			->   Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					PoolFormerModel(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase    :      Union[str, Any]						     =					model(lowerCAmelCase__							)
										_UpperCamelCase    :      str						     =					self.image_size // 32.0
										self.parent.assertEqual(
										    result.last_hidden_state.shape,				(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)							)






				def        snake_case			(				self	:					Any							)			->   Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      List[str]						     =					self.prepare_config_and_inputs()
										_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase    :      Dict						     =					config_and_inputs
										_UpperCamelCase    :      Optional[int]						     =					{'''pixel_values''': pixel_values}
										return config, inputs_dict




@require_torch
class 				_a		(   _lowerCAmelCase			,       _lowerCAmelCase			,       unittest.TestCase       ):
				UpperCamelCase			=					(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
				UpperCamelCase			=					(
				    {'''feature-extraction''': PoolFormerModel, '''image-classification''': PoolFormerForImageClassification}
				    if is_torch_available()
				    else {}
				)

				UpperCamelCase			=					False
				UpperCamelCase			=					False
				UpperCamelCase			=					False
				UpperCamelCase			=					False
				UpperCamelCase			=					False






				def        snake_case			(				self	:					List[Any]							)			->   List[str]:




										'''simple docstring'''



										_UpperCamelCase    :      Dict						     =					PoolFormerModelTester(self							)
										_UpperCamelCase    :      Dict						     =					PoolFormerConfigTester(self,				config_class=lowerCAmelCase__							)






				def        snake_case			(				self	:					Tuple							)			->   Any:




										'''simple docstring'''



										self.config_tester.run_common_tests()






				def        snake_case			(				self	:					Optional[Any]							)			->   Optional[int]:




										'''simple docstring'''



										_UpperCamelCase    :      Tuple						     =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__							)






				@unittest.skip('''PoolFormer does not use inputs_embeds'''							)
				def        snake_case			(				self	:					List[Any]							)			->   Optional[int]:




										'''simple docstring'''



										pass






				@unittest.skip('''PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'''							)
				def        snake_case			(				self	:					Optional[int]							)			->   Dict:




										'''simple docstring'''



										pass






				def        snake_case			(				self	:					List[Any]							)			->   str:




										'''simple docstring'''



										def check_hidden_states_output(lowerCAmelCase__	:					List[Any],				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					int							):
																_UpperCamelCase    :      Any						     =					model_class(lowerCAmelCase__							)
																model.to(lowerCAmelCase__							)
																model.eval()

																with torch.no_grad():
																						_UpperCamelCase    :      Tuple						     =					model(**self._prepare_for_class(lowerCAmelCase__,				lowerCAmelCase__							)							)

																_UpperCamelCase    :      List[str]						     =					outputs.hidden_states

																_UpperCamelCase    :      str						     =					self.model_tester.num_encoder_blocks
																self.assertEqual(len(lowerCAmelCase__							),				lowerCAmelCase__							)

																# verify the first hidden states (first block)
																self.assertListEqual(
																    list(hidden_states[0].shape[-3:]							),				[
																        self.model_tester.hidden_sizes[0],
																        self.model_tester.image_size // 4,
																        self.model_tester.image_size // 4,
																    ],				)

										_UpperCamelCase     ,							_UpperCamelCase    :      Optional[int]						     =					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																_UpperCamelCase    :      Tuple						     =					True
																check_hidden_states_output(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__							)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																_UpperCamelCase    :      List[str]						     =					True

																check_hidden_states_output(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__							)






				def        snake_case			(				self	:					Any							)			->   List[str]:




										'''simple docstring'''



										if not self.model_tester.is_training:
																return

										_UpperCamelCase     ,							_UpperCamelCase    :      List[str]						     =					self.model_tester.prepare_config_and_inputs_for_common()
										_UpperCamelCase    :      Optional[int]						     =					True

										for model_class in self.all_model_classes:
																if model_class in get_values(lowerCAmelCase__							):
																						continue
																_UpperCamelCase    :      Tuple						     =					model_class(lowerCAmelCase__							)
																model.to(lowerCAmelCase__							)
																model.train()
																_UpperCamelCase    :      Dict						     =					self._prepare_for_class(lowerCAmelCase__,				lowerCAmelCase__,				return_labels=lowerCAmelCase__							)
																_UpperCamelCase    :      Any						     =					model(**lowerCAmelCase__							).loss
																loss.backward()






				def        snake_case			(				self	:					Tuple							)			->   Any:




										'''simple docstring'''



										_UpperCamelCase     ,							_UpperCamelCase    :      List[Any]						     =					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																_UpperCamelCase    :      Tuple						     =					model_class(lowerCAmelCase__							)
																_UpperCamelCase    :      Any						     =					inspect.signature(model.forward							)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																_UpperCamelCase    :      str						     =					[*signature.parameters.keys()]

																_UpperCamelCase    :      List[str]						     =					['''pixel_values''']
																self.assertListEqual(arg_names[:1],				lowerCAmelCase__							)






				@slow
				def        snake_case			(				self	:					Dict							)			->   List[str]:




										'''simple docstring'''



										for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCamelCase    :      str						     =					PoolFormerModel.from_pretrained(lowerCAmelCase__							)
																self.assertIsNotNone(lowerCAmelCase__							)





def  a_       (							):
						_UpperCamelCase    :      Union[str, Any]						     =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
						return image






@require_torch
class 				_a		(   unittest.TestCase       ):






				@slow
				def        snake_case			(				self	:					str							)			->   Dict:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					PoolFormerImageProcessor()
										_UpperCamelCase    :      Tuple						     =					PoolFormerForImageClassification.from_pretrained('''sail/poolformer_s12'''							).to(lowerCAmelCase__							)

										_UpperCamelCase    :      Dict						     =					image_processor(images=prepare_img(),				return_tensors='''pt'''							).to(lowerCAmelCase__							)

										# forward pass
										with torch.no_grad():
																_UpperCamelCase    :      Union[str, Any]						     =					model(**lowerCAmelCase__							)

										# verify the logits
										_UpperCamelCase    :      Tuple						     =					torch.Size((1, 1_0_0_0)							)
										self.assertEqual(outputs.logits.shape,				lowerCAmelCase__							)

										_UpperCamelCase    :      Optional[Any]						     =					torch.tensor([-0.6_113, 0.1_685, -0.0_492]							).to(lowerCAmelCase__							)
										self.assertTrue(torch.allclose(outputs.logits[0, :3],				lowerCAmelCase__,				atol=1e-4							)							)










"""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

UpperCamelCase_     =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 				_a		(   _lowerCAmelCase       ):






				def __init__(				self	:					Optional[Any],				lowerCAmelCase__	:					Tuple							)			->   List[Any]:




										'''simple docstring'''



										set_hf_deepspeed_config(self							)
										dep_version_check('''accelerate'''							)
										dep_version_check('''deepspeed'''							)
										super().__init__(lowerCAmelCase__							)




class 				_a		(   _lowerCAmelCase       ):






				def __init__(				self	:					Any,				lowerCAmelCase__	:					int							)			->   Union[str, Any]:




										'''simple docstring'''



										super().__init__(lowerCAmelCase__							)
										_UpperCamelCase    :      Any						     =					None
										_UpperCamelCase    :      List[str]						     =					[]






				def        snake_case			(				self	:					Optional[Any]							)			->   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        snake_case			(				self	:					int,				lowerCAmelCase__	:					Any							)			->   List[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					self.get_value(lowerCAmelCase__							)
										if val is None:
																return False
										else:
																return val == "auto"






				def        snake_case			(				self	:					Optional[Any],				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					List[str]=None,				lowerCAmelCase__	:					Dict=True							)			->   int:




										'''simple docstring'''



										_UpperCamelCase     ,							_UpperCamelCase    :      Tuple						     =					self.find_config_node(lowerCAmelCase__							)
										if config is None:
																return

										if config.get(lowerCAmelCase__							) == "auto":
																_UpperCamelCase    :      int						     =					hf_val
																return

										if not must_match:
																return

										_UpperCamelCase    :      Any						     =					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}"""							)

				UpperCamelCase			=					partialmethod(_lowerCAmelCase			,       must_match=_lowerCAmelCase       )






				def        snake_case			(				self	:					List[str],				lowerCAmelCase__	:					List[str]							)			->   int:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[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''',				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:
																_UpperCamelCase    :      Union[str, Any]						     =					'''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
										else:
																_UpperCamelCase    :      List[str]						     =					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    :      Tuple						     =					self.config.get('''checkpoint''',				{}							)
																_UpperCamelCase    :      str						     =					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    :      Optional[int]						     =					torch.bfloataa
										elif self.is_false('''fp16.enabled'''							):
																_UpperCamelCase    :      Optional[Any]						     =					torch.floataa
										else:
																_UpperCamelCase    :      str						     =					torch.floataa






				def        snake_case			(				self	:					Tuple,				lowerCAmelCase__	:					Any,				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					str							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Tuple						     =					[
										    '''zero_optimization.reduce_bucket_size''',
										    '''zero_optimization.stage3_prefetch_bucket_size''',
										    '''zero_optimization.stage3_param_persistence_threshold''',
										]
										_UpperCamelCase    :      Dict						     =					[x for x in hidden_size_based_keys if self.is_auto(lowerCAmelCase__							)]

										if len(lowerCAmelCase__							) > 0:
																if hasattr(model.config,				'''hidden_size'''							):
																						_UpperCamelCase    :      str						     =					model.config.hidden_size
																elif hasattr(model.config,				'''hidden_sizes'''							):
																						# if there are many hidden sizes pick the largest one
																						_UpperCamelCase    :      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''',				1_0 * 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:
																_UpperCamelCase    :      int						     =					'''\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'."""							)

UpperCamelCase_     =None





def  a_       (							_lowercase     ):
						# 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)
						_UpperCamelCase    :      int						     =					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
						_UpperCamelCase    :      Optional[int]						     =					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 ,		_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     ):
						from accelerate.utils import DummyOptim, DummyScheduler

						_UpperCamelCase    :      Tuple						     =					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    :      str						     =					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    :      Optional[int]						     =					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.
												_UpperCamelCase    :      Union[str, Any]						     =					trainer.create_optimizer()
												# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
												_UpperCamelCase    :      Tuple						     =					True

						_UpperCamelCase    :      Optional[int]						     =					None
						if "scheduler" in config:
												_UpperCamelCase    :      List[Any]						     =					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.'''     )
												_UpperCamelCase    :      Any						     =					trainer.create_scheduler(num_training_steps=_lowercase ,		optimizer=_lowercase     )

						return optimizer, lr_scheduler





def  a_       (							_lowercase ,		_lowercase ,		_lowercase=False     ):
						from deepspeed.utils import logger as ds_logger

						_UpperCamelCase    :      Tuple						     =					trainer.model
						_UpperCamelCase    :      List[str]						     =					trainer.args

						_UpperCamelCase    :      str						     =					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'''     )
												_UpperCamelCase     ,							_UpperCamelCase    :      Tuple						     =					None, None
												_UpperCamelCase    :      Optional[int]						     =					None
						else:
												_UpperCamelCase    :      Any						     =					None  # important for when deepspeed_init is used as re-init
												_UpperCamelCase    :      Any						     =					list(filter(lambda _lowercase     : p.requires_grad ,		model.parameters()     )     )
												_UpperCamelCase     ,							_UpperCamelCase    :      Dict						     =					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 ,		_lowercase     ):
						# 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

						_UpperCamelCase    :      Tuple						     =					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
												_UpperCamelCase     ,							_UpperCamelCase    :      Any						     =					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}"""     )



