





"""simple docstring"""




import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor





def  a_       (							_lowercase     ):
						_UpperCamelCase    :      Tuple						     =					TimesformerConfig()

						if "large" in model_name:
												_UpperCamelCase    :      List[Any]						     =					96

						if "hr" in model_name:
												_UpperCamelCase    :      List[str]						     =					16
												_UpperCamelCase    :      Union[str, Any]						     =					448

						_UpperCamelCase    :      Union[str, Any]						     =					'''huggingface/label-files'''
						if "k400" in model_name:
												_UpperCamelCase    :      Optional[int]						     =					400
												_UpperCamelCase    :      int						     =					'''kinetics400-id2label.json'''
						elif "k600" in model_name:
												_UpperCamelCase    :      List[str]						     =					600
												_UpperCamelCase    :      Optional[Any]						     =					'''kinetics600-id2label.json'''
						elif "ssv2" in model_name:
												_UpperCamelCase    :      Optional[int]						     =					174
												_UpperCamelCase    :      Optional[int]						     =					'''something-something-v2-id2label.json'''
						else:
												raise ValueError('''Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'''     )
						_UpperCamelCase    :      Optional[int]						     =					json.load(open(hf_hub_download(_lowercase ,		_lowercase ,		repo_type='''dataset'''     ) ,		'''r'''     )     )
						_UpperCamelCase    :      Tuple						     =					{int(_lowercase     ): v for k, v in idalabel.items()}
						_UpperCamelCase    :      int						     =					idalabel
						_UpperCamelCase    :      int						     =					{v: k for k, v in idalabel.items()}

						return config





def  a_       (							_lowercase     ):
						if "encoder." in name:
												_UpperCamelCase    :      Dict						     =					name.replace('''encoder.''' ,		''''''     )
						if "cls_token" in name:
												_UpperCamelCase    :      Optional[Any]						     =					name.replace('''cls_token''' ,		'''timesformer.embeddings.cls_token'''     )
						if "pos_embed" in name:
												_UpperCamelCase    :      Optional[Any]						     =					name.replace('''pos_embed''' ,		'''timesformer.embeddings.position_embeddings'''     )
						if "time_embed" in name:
												_UpperCamelCase    :      Any						     =					name.replace('''time_embed''' ,		'''timesformer.embeddings.time_embeddings'''     )
						if "patch_embed.proj" in name:
												_UpperCamelCase    :      str						     =					name.replace('''patch_embed.proj''' ,		'''timesformer.embeddings.patch_embeddings.projection'''     )
						if "patch_embed.norm" in name:
												_UpperCamelCase    :      List[str]						     =					name.replace('''patch_embed.norm''' ,		'''timesformer.embeddings.norm'''     )
						if "blocks" in name:
												_UpperCamelCase    :      str						     =					name.replace('''blocks''' ,		'''timesformer.encoder.layer'''     )
						if "attn.proj" in name:
												_UpperCamelCase    :      Tuple						     =					name.replace('''attn.proj''' ,		'''attention.output.dense'''     )
						if "attn" in name and "bias" not in name and "temporal" not in name:
												_UpperCamelCase    :      int						     =					name.replace('''attn''' ,		'''attention.self'''     )
						if "attn" in name and "temporal" not in name:
												_UpperCamelCase    :      List[Any]						     =					name.replace('''attn''' ,		'''attention.attention'''     )
						if "temporal_norm1" in name:
												_UpperCamelCase    :      List[Any]						     =					name.replace('''temporal_norm1''' ,		'''temporal_layernorm'''     )
						if "temporal_attn.proj" in name:
												_UpperCamelCase    :      Optional[int]						     =					name.replace('''temporal_attn''' ,		'''temporal_attention.output.dense'''     )
						if "temporal_fc" in name:
												_UpperCamelCase    :      List[str]						     =					name.replace('''temporal_fc''' ,		'''temporal_dense'''     )
						if "norm1" in name and "temporal" not in name:
												_UpperCamelCase    :      Tuple						     =					name.replace('''norm1''' ,		'''layernorm_before'''     )
						if "norm2" in name:
												_UpperCamelCase    :      Dict						     =					name.replace('''norm2''' ,		'''layernorm_after'''     )
						if "mlp.fc1" in name:
												_UpperCamelCase    :      Optional[int]						     =					name.replace('''mlp.fc1''' ,		'''intermediate.dense'''     )
						if "mlp.fc2" in name:
												_UpperCamelCase    :      Tuple						     =					name.replace('''mlp.fc2''' ,		'''output.dense'''     )
						if "norm.weight" in name and "fc" not in name and "temporal" not in name:
												_UpperCamelCase    :      List[str]						     =					name.replace('''norm.weight''' ,		'''timesformer.layernorm.weight'''     )
						if "norm.bias" in name and "fc" not in name and "temporal" not in name:
												_UpperCamelCase    :      List[str]						     =					name.replace('''norm.bias''' ,		'''timesformer.layernorm.bias'''     )
						if "head" in name:
												_UpperCamelCase    :      str						     =					name.replace('''head''' ,		'''classifier'''     )

						return name





def  a_       (							_lowercase ,		_lowercase     ):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase    :      Optional[Any]						     =					orig_state_dict.pop(_lowercase     )

												if key.startswith('''model.'''     ):
																		_UpperCamelCase    :      List[Any]						     =					key.replace('''model.''' ,		''''''     )

												if "qkv" in key:
																		_UpperCamelCase    :      Union[str, Any]						     =					key.split('''.'''     )
																		_UpperCamelCase    :      int						     =					int(key_split[1]     )
																		_UpperCamelCase    :      Tuple						     =					'''timesformer.encoder.layer.'''
																		if "temporal" in key:
																								_UpperCamelCase    :      int						     =					'''.temporal_attention.attention.qkv.'''
																		else:
																								_UpperCamelCase    :      List[Any]						     =					'''.attention.attention.qkv.'''
																		if "weight" in key:
																								_UpperCamelCase    :      Optional[int]						     =					val
																		else:
																								_UpperCamelCase    :      List[str]						     =					val
												else:
																		_UpperCamelCase    :      Optional[int]						     =					val

						return orig_state_dict





def  a_       (							):
						_UpperCamelCase    :      Dict						     =					hf_hub_download(
						    repo_id='''hf-internal-testing/spaghetti-video''' ,		filename='''eating_spaghetti.npy''' ,		repo_type='''dataset'''     )
						_UpperCamelCase    :      str						     =					np.load(_lowercase     )
						return list(_lowercase     )





def  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      str						     =					get_timesformer_config(_lowercase     )

						_UpperCamelCase    :      Optional[Any]						     =					TimesformerForVideoClassification(_lowercase     )

						# download original checkpoint, hosted on Google Drive
						_UpperCamelCase    :      str						     =					'''pytorch_model.bin'''
						gdown.cached_download(_lowercase ,		_lowercase ,		quiet=_lowercase     )
						_UpperCamelCase    :      Dict						     =					torch.load(_lowercase ,		map_location='''cpu'''     )
						if "model" in files:
												_UpperCamelCase    :      Optional[int]						     =					files['''model''']
						elif "module" in files:
												_UpperCamelCase    :      Optional[Any]						     =					files['''module''']
						else:
												_UpperCamelCase    :      Optional[int]						     =					files['''model_state''']
						_UpperCamelCase    :      List[str]						     =					convert_state_dict(_lowercase ,		_lowercase     )

						model.load_state_dict(_lowercase     )
						model.eval()

						# verify model on basic input
						_UpperCamelCase    :      Optional[Any]						     =					VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] ,		image_std=[0.5, 0.5, 0.5]     )
						_UpperCamelCase    :      Optional[Any]						     =					prepare_video()
						_UpperCamelCase    :      Any						     =					image_processor(video[:8] ,		return_tensors='''pt'''     )

						_UpperCamelCase    :      Union[str, Any]						     =					model(**_lowercase     )
						_UpperCamelCase    :      List[str]						     =					outputs.logits

						_UpperCamelCase    :      Union[str, Any]						     =					[
						    # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
						    '''timesformer-base-finetuned-k400''',
						    '''timesformer-large-finetuned-k400''',
						    '''timesformer-hr-finetuned-k400''',
						    # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
						    '''timesformer-base-finetuned-k600''',
						    '''timesformer-large-finetuned-k600''',
						    '''timesformer-hr-finetuned-k600''',
						    # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
						    '''timesformer-base-finetuned-ssv2''',
						    '''timesformer-large-finetuned-ssv2''',
						    '''timesformer-hr-finetuned-ssv2''',
						]

						# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
						if model_name == "timesformer-base-finetuned-k400":
												_UpperCamelCase    :      Union[str, Any]						     =					torch.Size([1, 400]     )
												_UpperCamelCase    :      str						     =					torch.tensor([-0.3016, -0.7713, -0.4205]     )
						elif model_name == "timesformer-base-finetuned-k600":
												_UpperCamelCase    :      str						     =					torch.Size([1, 600]     )
												_UpperCamelCase    :      Union[str, Any]						     =					torch.tensor([-0.7267, -0.7466, 3.2404]     )
						elif model_name == "timesformer-base-finetuned-ssv2":
												_UpperCamelCase    :      Union[str, Any]						     =					torch.Size([1, 174]     )
												_UpperCamelCase    :      List[Any]						     =					torch.tensor([-0.9059, 0.6433, -3.1457]     )
						elif model_name == "timesformer-large-finetuned-k400":
												_UpperCamelCase    :      str						     =					torch.Size([1, 400]     )
												_UpperCamelCase    :      int						     =					torch.tensor([0, 0, 0]     )
						elif model_name == "timesformer-large-finetuned-k600":
												_UpperCamelCase    :      Any						     =					torch.Size([1, 600]     )
												_UpperCamelCase    :      int						     =					torch.tensor([0, 0, 0]     )
						elif model_name == "timesformer-large-finetuned-ssv2":
												_UpperCamelCase    :      List[str]						     =					torch.Size([1, 174]     )
												_UpperCamelCase    :      Dict						     =					torch.tensor([0, 0, 0]     )
						elif model_name == "timesformer-hr-finetuned-k400":
												_UpperCamelCase    :      List[Any]						     =					torch.Size([1, 400]     )
												_UpperCamelCase    :      Tuple						     =					torch.tensor([-0.9617, -3.7311, -3.7708]     )
						elif model_name == "timesformer-hr-finetuned-k600":
												_UpperCamelCase    :      List[str]						     =					torch.Size([1, 600]     )
												_UpperCamelCase    :      Dict						     =					torch.tensor([2.5273, 0.7127, 1.8848]     )
						elif model_name == "timesformer-hr-finetuned-ssv2":
												_UpperCamelCase    :      str						     =					torch.Size([1, 174]     )
												_UpperCamelCase    :      Tuple						     =					torch.tensor([-3.6756, -0.7513, 0.7180]     )
						else:
												raise ValueError(F"""Model name not supported. Should be one of {model_names}"""     )

						# verify logits
						assert logits.shape == expected_shape
						assert torch.allclose(logits[0, :3] ,		_lowercase ,		atol=1E-4     )
						print('''Logits ok!'''     )

						if pytorch_dump_folder_path is not None:
												print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""     )
												image_processor.save_pretrained(_lowercase     )
												model.save_pretrained(_lowercase     )

						if push_to_hub:
												print('''Pushing to the hub...'''     )
												model.push_to_hub(F"""fcakyon/{model_name}"""     )


if __name__ == "__main__":
						UpperCamelCase_     =argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--checkpoint_url""",
						    default="""https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download""",
						    type=str,
						    help=(
						        """URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
						        """ download link."""
						    ),
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default="""""",
						    type=str,
						    help="""Path to the output PyTorch model directory.""",
						)
						parser.add_argument("""--model_name""", default="""timesformer-base-finetuned-k400""", type=str, help="""Name of the model.""")
						parser.add_argument(
						    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
						)

						UpperCamelCase_     =parser.parse_args()
						convert_timesformer_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
						)










"""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}"""     )



