
import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

SCREAMING_SNAKE_CASE   :		Any					 =       logging.get_logger(__name__)



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase = True     ):
				A_       	=  {}
				for name in model.attn_processors.keys():
								A_       	=  name.endswith('''attn2.processor'''     ) and not (
								    name.startswith('''transformer_in'''     ) or '''temp_attentions''' in name.split('''.'''     )
								)
								A_       	=  model.config.cross_attention_dim if has_cross_attention else None
								if name.startswith('''mid_block'''     ):
												A_       	=  model.config.block_out_channels[-1]
								elif name.startswith('''up_blocks'''     ):
												A_       	=  int(name[len('''up_blocks.'''     )]     )
												A_       	=  list(reversed(model.config.block_out_channels     )     )[block_id]
								elif name.startswith('''down_blocks'''     ):
												A_       	=  int(name[len('''down_blocks.'''     )]     )
												A_       	=  model.config.block_out_channels[block_id]
								elif name.startswith('''transformer_in'''     ):
												# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
												A_       	=  8 * model.config.attention_head_dim

								A_       	=  LoRAAttnProcessor(hidden_size=__UpperCamelCase ,						cross_attention_dim=__UpperCamelCase     )
								A_       	=  lora_attn_procs[name].to(model.device     )

								if mock_weights:
												# add 1 to weights to mock trained weights
												with torch.no_grad():
																lora_attn_procs[name].to_q_lora.up.weight += 1
																lora_attn_procs[name].to_k_lora.up.weight += 1
																lora_attn_procs[name].to_v_lora.up.weight += 1
																lora_attn_procs[name].to_out_lora.up.weight += 1

				return lora_attn_procs






@skip_mps
class 					__lowercase		(					A							,       A							,       unittest.TestCase       ):
					__magic_name__						:							Optional[Any]     	=	UNetaDConditionModel
					__magic_name__						:							List[str]     	=	'''sample'''


					@property
					def        lowerCAmelCase_    (	self						):





									'''simple docstring'''
									A_       	=  4
									A_       	=  4
									A_       	=  4
									A_       	=  (3_2, 3_2)

									A_       	=  floats_tensor((batch_size, num_channels, num_frames) + sizes						).to(a__						)
									A_       	=  torch.tensor([1_0]						).to(a__						)
									A_       	=  floats_tensor((batch_size, 4, 3_2)						).to(a__						)

									return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}


					@property
					def        lowerCAmelCase_    (	self						):





									'''simple docstring'''
									return (4, 4, 3_2, 3_2)


					@property
					def        lowerCAmelCase_    (	self						):





									'''simple docstring'''
									return (4, 4, 3_2, 3_2)


					def        lowerCAmelCase_    (	self						):





									'''simple docstring'''
									A_       	=  {
									    '''block_out_channels''': (3_2, 6_4),
									    '''down_block_types''': (
									        '''CrossAttnDownBlock3D''',
									        '''DownBlock3D''',
									    ),
									    '''up_block_types''': ('''UpBlock3D''', '''CrossAttnUpBlock3D'''),
									    '''cross_attention_dim''': 3_2,
									    '''attention_head_dim''': 8,
									    '''out_channels''': 4,
									    '''in_channels''': 4,
									    '''layers_per_block''': 1,
									    '''sample_size''': 3_2,
									}
									A_       	=  self.dummy_input
									return init_dict, inputs_dict


					@unittest.skipIf(
					    torch_device != '''cuda''' or not is_xformers_available()       ,       reason='''XFormers attention is only available with CUDA and `xformers` installed'''       ,       )
					def        lowerCAmelCase_    (	self						):





									'''simple docstring'''
									A_	,					A_       	=  self.prepare_init_args_and_inputs_for_common()
									A_       	=  self.model_class(**a__						)

									model.enable_xformers_memory_efficient_attention()

									assert (
									    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
									    == "XFormersAttnProcessor"
									), "xformers is not enabled"


					def        lowerCAmelCase_    (	self						):





									'''simple docstring'''
									A_	,					A_       	=  self.prepare_init_args_and_inputs_for_common()

									A_       	=  3_2

									A_       	=  self.model_class(**a__						)
									model.to(a__						)
									model.eval()

									with torch.no_grad():
													A_       	=  model(**a__						)

													if isinstance(a__       ,       a__						):
																	A_       	=  output.sample

									self.assertIsNotNone(a__						)
									A_       	=  inputs_dict['''sample'''].shape
									self.assertEqual(output.shape       ,       a__       ,       '''Input and output shapes do not match'''						)


					def        lowerCAmelCase_    (	self						):





									'''simple docstring'''
									A_	,					A_       	=  self.prepare_init_args_and_inputs_for_common()
									A_       	=  self.model_class(**a__						)
									model.to(a__						)
									model.eval()

									with torch.no_grad():
													# Warmup pass when using mps (see #372)
													if torch_device == "mps" and isinstance(a__       ,       a__						):
																	model(**self.dummy_input						)

													A_       	=  model(**a__						)
													if isinstance(a__       ,       a__						):
																	A_       	=  first.sample

													A_       	=  model(**a__						)
													if isinstance(a__       ,       a__						):
																	A_       	=  second.sample

									A_       	=  first.cpu().numpy()
									A_       	=  second.cpu().numpy()
									A_       	=  out_a[~np.isnan(a__						)]
									A_       	=  out_a[~np.isnan(a__						)]
									A_       	=  np.amax(np.abs(out_a - out_a						)						)
									self.assertLessEqual(a__       ,       1E-5						)


					def        lowerCAmelCase_    (	self						):





									'''simple docstring'''
									A_	,					A_       	=  self.prepare_init_args_and_inputs_for_common()

									A_       	=  8

									A_       	=  self.model_class(**a__						)
									model.to(a__						)
									model.eval()

									model.set_attention_slice('''auto'''						)
									with torch.no_grad():
													A_       	=  model(**a__						)
									assert output is not None

									model.set_attention_slice('''max'''						)
									with torch.no_grad():
													A_       	=  model(**a__						)
									assert output is not None

									model.set_attention_slice(2						)
									with torch.no_grad():
													A_       	=  model(**a__						)
									assert output is not None


					def        lowerCAmelCase_    (	self						):





									'''simple docstring'''
									A_	,					A_       	=  self.prepare_init_args_and_inputs_for_common()

									A_       	=  8

									A_       	=  self.model_class(**a__						)
									model.to(a__						)

									with torch.no_grad():
													A_       	=  model(**a__						).sample

									A_       	=  create_lora_layers(a__						)

									# make sure we can set a list of attention processors
									model.set_attn_processor(a__						)
									model.to(a__						)

									# test that attn processors can be set to itself
									model.set_attn_processor(model.attn_processors						)

									with torch.no_grad():
													A_       	=  model(**a__       ,       cross_attention_kwargs={'''scale''': 0.0}						).sample
													A_       	=  model(**a__       ,       cross_attention_kwargs={'''scale''': 0.5}						).sample
													A_       	=  model(**a__       ,       cross_attention_kwargs={'''scale''': 0.5}						).sample

									assert (samplea - samplea).abs().max() < 3E-3
									assert (samplea - samplea).abs().max() < 3E-3

									# sample 2 and sample 3 should be different
									assert (samplea - samplea).abs().max() > 3E-3


					def        lowerCAmelCase_    (	self						):





									'''simple docstring'''
									A_	,					A_       	=  self.prepare_init_args_and_inputs_for_common()

									A_       	=  8

									torch.manual_seed(0						)
									A_       	=  self.model_class(**a__						)
									model.to(a__						)

									with torch.no_grad():
													A_       	=  model(**a__						).sample

									A_       	=  create_lora_layers(a__						)
									model.set_attn_processor(a__						)

									with torch.no_grad():
													A_       	=  model(**a__       ,       cross_attention_kwargs={'''scale''': 0.5}						).sample

									with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_attn_procs(a__						)
													self.assertTrue(os.path.isfile(os.path.join(a__       ,       '''pytorch_lora_weights.bin'''						)						)						)
													torch.manual_seed(0						)
													A_       	=  self.model_class(**a__						)
													new_model.to(a__						)
													new_model.load_attn_procs(a__						)

									with torch.no_grad():
													A_       	=  new_model(**a__       ,       cross_attention_kwargs={'''scale''': 0.5}						).sample

									assert (sample - new_sample).abs().max() < 1E-3

									# LoRA and no LoRA should NOT be the same
									assert (sample - old_sample).abs().max() > 1E-4


					def        lowerCAmelCase_    (	self						):





									'''simple docstring'''
									A_	,					A_       	=  self.prepare_init_args_and_inputs_for_common()

									A_       	=  8

									torch.manual_seed(0						)
									A_       	=  self.model_class(**a__						)
									model.to(a__						)

									with torch.no_grad():
													A_       	=  model(**a__						).sample

									A_       	=  create_lora_layers(a__						)
									model.set_attn_processor(a__						)

									with torch.no_grad():
													A_       	=  model(**a__       ,       cross_attention_kwargs={'''scale''': 0.5}						).sample

									with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_attn_procs(a__       ,       safe_serialization=a__						)
													self.assertTrue(os.path.isfile(os.path.join(a__       ,       '''pytorch_lora_weights.safetensors'''						)						)						)
													torch.manual_seed(0						)
													A_       	=  self.model_class(**a__						)
													new_model.to(a__						)
													new_model.load_attn_procs(a__						)

									with torch.no_grad():
													A_       	=  new_model(**a__       ,       cross_attention_kwargs={'''scale''': 0.5}						).sample

									assert (sample - new_sample).abs().max() < 3E-3

									# LoRA and no LoRA should NOT be the same
									assert (sample - old_sample).abs().max() > 1E-4


					def        lowerCAmelCase_    (	self						):





									'''simple docstring'''
									A_	,					A_       	=  self.prepare_init_args_and_inputs_for_common()

									A_       	=  8

									torch.manual_seed(0						)
									A_       	=  self.model_class(**a__						)
									model.to(a__						)

									A_       	=  create_lora_layers(a__       ,       mock_weights=a__						)
									model.set_attn_processor(a__						)
									# Saving as torch, properly reloads with directly filename
									with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_attn_procs(a__						)
													self.assertTrue(os.path.isfile(os.path.join(a__       ,       '''pytorch_lora_weights.bin'''						)						)						)
													torch.manual_seed(0						)
													A_       	=  self.model_class(**a__						)
													new_model.to(a__						)
													new_model.load_attn_procs(a__       ,       weight_name='''pytorch_lora_weights.bin'''						)


					def        lowerCAmelCase_    (	self						):





									'''simple docstring'''
									A_	,					A_       	=  self.prepare_init_args_and_inputs_for_common()

									A_       	=  8

									torch.manual_seed(0						)
									A_       	=  self.model_class(**a__						)
									model.to(a__						)

									A_       	=  create_lora_layers(a__       ,       mock_weights=a__						)
									model.set_attn_processor(a__						)
									# Saving as torch, properly reloads with directly filename
									with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_attn_procs(a__						)
													self.assertTrue(os.path.isfile(os.path.join(a__       ,       '''pytorch_lora_weights.bin'''						)						)						)
													torch.manual_seed(0						)
													A_       	=  self.model_class(**a__						)
													new_model.to(a__						)
													with self.assertRaises(a__						) as e:
																	new_model.load_attn_procs(a__       ,       use_safetensors=a__						)
													self.assertIn('''Error no file named pytorch_lora_weights.safetensors'''       ,       str(e.exception						)						)


					def        lowerCAmelCase_    (	self						):





									'''simple docstring'''
									A_	,					A_       	=  self.prepare_init_args_and_inputs_for_common()

									A_       	=  8

									torch.manual_seed(0						)
									A_       	=  self.model_class(**a__						)
									model.to(a__						)

									with torch.no_grad():
													A_       	=  model(**a__						).sample

									A_       	=  create_lora_layers(a__						)
									model.set_attn_processor(a__						)

									with torch.no_grad():
													A_       	=  model(**a__       ,       cross_attention_kwargs={'''scale''': 0.0}						).sample

									model.set_attn_processor(AttnProcessor()						)

									with torch.no_grad():
													A_       	=  model(**a__						).sample

									assert (sample - new_sample).abs().max() < 1E-4
									assert (sample - old_sample).abs().max() < 3E-3


					@unittest.skipIf(
					    torch_device != '''cuda''' or not is_xformers_available()       ,       reason='''XFormers attention is only available with CUDA and `xformers` installed'''       ,       )
					def        lowerCAmelCase_    (	self						):





									'''simple docstring'''
									A_	,					A_       	=  self.prepare_init_args_and_inputs_for_common()

									A_       	=  4

									torch.manual_seed(0						)
									A_       	=  self.model_class(**a__						)
									model.to(a__						)
									A_       	=  create_lora_layers(a__						)
									model.set_attn_processor(a__						)

									# default
									with torch.no_grad():
													A_       	=  model(**a__						).sample

													model.enable_xformers_memory_efficient_attention()
													A_       	=  model(**a__						).sample

													model.disable_xformers_memory_efficient_attention()
													A_       	=  model(**a__						).sample

									assert (sample - on_sample).abs().max() < 1E-4
									assert (sample - off_sample).abs().max() < 1E-4


					def        lowerCAmelCase_    (	self						):





									'''simple docstring'''
									A_	,					A_       	=  self.prepare_init_args_and_inputs_for_common()
									A_       	=  3_2

									A_       	=  self.model_class(**a__						)
									model.to(a__						)
									model.eval()

									with torch.no_grad():
													A_       	=  model(**a__						)[0]

									model.enable_forward_chunking()
									with torch.no_grad():
													A_       	=  model(**a__						)[0]

									self.assertEqual(output.shape       ,       output_a.shape       ,       '''Shape doesn\'t match'''						)
									assert np.abs(output.cpu() - output_a.cpu()						).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.

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

SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       logging.get_logger(__name__)



def        lowerCamelCase_  (   ):
				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 					__lowercase		(					A       ):


					def __init__(	self       ,       a__						)		->							Dict:





									'''simple docstring'''
									# set global weakref object
									set_hf_deepspeed_config(self						)
									dep_version_check('''accelerate'''						)
									dep_version_check('''deepspeed'''						)
									super().__init__(a__						)






class 					__lowercase		(					A       ):


					def __init__(	self       ,       a__						)		->							List[Any]:





									'''simple docstring'''
									super().__init__(a__						)
									A_       	=  None
									A_       	=  []


					def        lowerCAmelCase_    (	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        lowerCAmelCase_    (	self       ,       a__						)		->							Optional[int]:





									'''simple docstring'''
									A_       	=  self.get_value(a__						)
									if val is None:
													return False
									else:
													return val == "auto"


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__=None       ,       a__=True						)		->							List[str]:





									'''simple docstring'''
									A_	,					A_       	=  self.find_config_node(a__						)
									if config is None:
													return

									if config.get(a__						) == "auto":
													A_       	=  hf_val
													return

									if not must_match:
													return

									A_       	=  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}"						)

					__magic_name__						:							str     	=	partialmethod(A							,       must_match=A       )


					def        lowerCAmelCase_    (	self       ,       a__						)		->							str:





									'''simple docstring'''
									A_       	=  args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
									self.fill_match(
									    '''train_micro_batch_size_per_gpu'''       ,       args.per_device_train_batch_size       ,       '''per_device_train_batch_size'''						)
									self.fill_match('''gradient_accumulation_steps'''       ,       args.gradient_accumulation_steps       ,       '''gradient_accumulation_steps'''						)
									self.fill_match('''train_batch_size'''       ,       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:
													A_       	=  '''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
									else:
													A_       	=  None

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

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

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

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

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


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__						)		->							Tuple:





									'''simple docstring'''
									A_       	=  [
									    '''zero_optimization.reduce_bucket_size''',
									    '''zero_optimization.stage3_prefetch_bucket_size''',
									    '''zero_optimization.stage3_param_persistence_threshold''',
									]
									A_       	=  [x for x in hidden_size_based_keys if self.is_auto(a__						)]

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

													self.fill_only('''zero_optimization.reduce_bucket_size'''       ,       hidden_size * hidden_size						)
													if self.is_zeroa():
																	# automatically assign the optimal config values based on model config
																	self.fill_only('''zero_optimization.stage3_prefetch_bucket_size'''       ,       0.9 * hidden_size * hidden_size						)
																	self.fill_only('''zero_optimization.stage3_param_persistence_threshold'''       ,       1_0 * 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:
													A_       	=  '''\n'''.join(self.mismatches						)
													raise ValueError(
													    '''Please correct the following DeepSpeed config values that mismatch TrainingArguments'''
													    F" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'."						)
SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       None



def        lowerCamelCase_  (   __UpperCamelCase     ):
				# 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)
				A_       	=  weakref.ref(__UpperCamelCase     )



def        lowerCamelCase_  (   ):
				# useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
				global _hf_deepspeed_config_weak_ref
				A_       	=  None



def        lowerCamelCase_  (   ):
				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        lowerCamelCase_  (   ):
				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        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				from accelerate.utils import DummyOptim, DummyScheduler

				A_       	=  hf_deepspeed_config.config

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

				A_       	=  None
				if "optimizer" in config:
								if args.adafactor:
												raise ValueError(
												    '''--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '''
												    '''Only one optimizer can be configured.'''     )
								A_       	=  DummyOptim(params=__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.
								A_       	=  trainer.create_optimizer()
								# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
								A_       	=  True

				A_       	=  None
				if "scheduler" in config:
								A_       	=  DummyScheduler(__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.'''     )
								A_       	=  trainer.create_scheduler(num_training_steps=__UpperCamelCase ,						optimizer=__UpperCamelCase     )

				return optimizer, lr_scheduler



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase=False     ):
				from deepspeed.utils import logger as ds_logger

				A_       	=  trainer.model
				A_       	=  trainer.args

				A_       	=  trainer.accelerator.state.deepspeed_plugin.hf_ds_config

				# resume config update - some bits like `model` and `num_training_steps` only become available during train
				hf_deepspeed_config.trainer_config_finalize(__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'''     )
								A_	,					A_       	=  None, None
								A_       	=  None
				else:
								A_       	=  None  # important for when deepspeed_init is used as re-init
								A_       	=  list(filter(lambda __UpperCamelCase     : p.requires_grad ,						model.parameters()     )     )
								A_	,					A_       	=  deepspeed_optim_sched(
								    __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     )

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

				return optimizer, lr_scheduler



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				# 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

				A_       	=  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
								A_	,					A_       	=  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}"     )