

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

A__							=				logging.get_logger(__name__)




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




					'''simple docstring'''







					return importlib.util.find_spec('deepspeed'					) is not None


if is_accelerate_available() and is_deepspeed_available():
						from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
						# Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
						# Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
						from builtins import object as DeepSpeedConfig

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):







	def __init__( self:			str	,			__UpperCamelCase:			List[str]   ):

						'''simple docstring'''





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




class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):







	def __init__( self:			int	,			__UpperCamelCase:			Optional[int]   ):

						'''simple docstring'''





						super().__init__(__UpperCamelCase   )
						__magic_name__				=     None
						__magic_name__				=     []







	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]   ):

						'''simple docstring'''





						if self._dtype is None:
											raise ValueError('trainer_config_process() wasn\'t called yet to tell dtype'   )
						return self._dtype







	def 						_SCREAMING_SNAKE_CASE				( self:			Any	,			__UpperCamelCase:			Union[str, Any]   ):

						'''simple docstring'''





						__magic_name__				=     self.get_value(__UpperCamelCase   )
						if val is None:
											return False
						else:
											return val == "auto"







	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]	,			__UpperCamelCase:			List[Any]	,			__UpperCamelCase:			Dict	,			__UpperCamelCase:			Optional[int]=None	,			__UpperCamelCase:			Union[str, Any]=True   ):

						'''simple docstring'''





						__magic_name__,						__magic_name__				=     self.find_config_node(__UpperCamelCase   )
						if config is None:
											return

						if config.get(__UpperCamelCase   ) == "auto":
											__magic_name__				=     hf_val
											return

						if not must_match:
											return

						__magic_name__				=     config.get(__UpperCamelCase   )
						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}'   )

	_lowercase	: int												=	partialmethod(SCREAMING_SNAKE_CASE	,    must_match=SCREAMING_SNAKE_CASE      )







	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]	,			__UpperCamelCase:			Tuple   ):

						'''simple docstring'''





						__magic_name__				=     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'	,			__UpperCamelCase	,			'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:
											__magic_name__				=     'apex' if args.fpaa_backend == 'apex' else 'amp'
						else:
											__magic_name__				=     None

						if args.save_on_each_node:
											# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
											__magic_name__				=     self.config.get('checkpoint'	,			{}   )
											__magic_name__				=     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'   ):
											__magic_name__				=     torch.bfloataa
						elif self.is_false('fp16.enabled'   ):
											__magic_name__				=     torch.floataa
						else:
											__magic_name__				=     torch.floataa







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]	,			__UpperCamelCase:			Tuple	,			__UpperCamelCase:			int	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     [
						    'zero_optimization.reduce_bucket_size',
						    'zero_optimization.stage3_prefetch_bucket_size',
						    'zero_optimization.stage3_param_persistence_threshold',
						]
						__magic_name__				=     [x for x in hidden_size_based_keys if self.is_auto(__UpperCamelCase   )]

						if len(__UpperCamelCase   ) > 0:
											if hasattr(model.config	,			'hidden_size'   ):
																__magic_name__				=     model.config.hidden_size
											elif hasattr(model.config	,			'hidden_sizes'   ):
																# if there are many hidden sizes pick the largest one
																__magic_name__				=     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'	,			__UpperCamelCase	,			'num_training_steps (calculated)'   )
						self.fill_match('scheduler.params.warmup_num_steps'	,			args.get_warmup_steps(__UpperCamelCase   )	,			'warmup_steps'   )

						if len(self.mismatches   ) > 0:
											__magic_name__				=     '\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\'.'   )




A__							=				None




def        _lowercase  (					a_					:       List[Any]					)					->						Union[str, Any]:




					'''simple docstring'''







					global _hf_deepspeed_config_weak_ref
					# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
					__magic_name__				=     weakref.ref(a_					)




def        _lowercase  (					)					->						Dict:




					'''simple docstring'''







					global _hf_deepspeed_config_weak_ref
					__magic_name__				=     None




def        _lowercase  (					)					->						Dict:




					'''simple docstring'''







					if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
										return _hf_deepspeed_config_weak_ref().is_zeroa()
					else:
										return False




def        _lowercase  (					)					->						str:




					'''simple docstring'''







					if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
										return _hf_deepspeed_config_weak_ref().config
					else:
										return None




def        _lowercase  (					a_					:       Union[str, Any]       ,a_					:       Union[str, Any]       ,a_					:       Dict       ,a_					:       Tuple       ,a_					:       Tuple					)					->						Union[str, Any]:




					'''simple docstring'''







					from accelerate.utils import DummyOptim, DummyScheduler

					__magic_name__				=     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)

					__magic_name__				=     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.'					)
										__magic_name__				=     DummyOptim(params=a_					)
					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.
										__magic_name__				=     trainer.create_optimizer()
										# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
										__magic_name__				=     True

					__magic_name__				=     None
					if "scheduler" in config:
										__magic_name__				=     DummyScheduler(a_					)
					else:
										if isinstance(a_       ,a_					):
															raise ValueError(
															    'Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '
															    'Please configure a scheduler in the DeepSpeed config.'					)
										__magic_name__				=     trainer.create_scheduler(num_training_steps=a_       ,optimizer=a_					)

					return optimizer, lr_scheduler




def        _lowercase  (					a_					:       Optional[int]       ,a_					:       Optional[int]       ,a_					:       Any=False					)					->						int:




					'''simple docstring'''







					from deepspeed.utils import logger as ds_logger

					__magic_name__				=     trainer.model
					__magic_name__				=     trainer.args

					__magic_name__				=     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(a_       ,a_       ,a_					)

					# 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'					)
										__magic_name__,						__magic_name__				=     None, None
										__magic_name__				=     None
					else:
										__magic_name__				=     None  # important for when deepspeed_init is used as re-init
										__magic_name__				=     list(filter(lambda a_					: p.requires_grad       ,model.parameters()					)					)
										__magic_name__,						__magic_name__				=     deepspeed_optim_sched(
										    a_       ,a_       ,a_       ,a_       ,a_					)

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

					return optimizer, lr_scheduler




def        _lowercase  (					a_					:       List[Any]       ,a_					:       List[str]					)					->						Dict:




					'''simple docstring'''







					import glob

					__magic_name__				=     sorted(glob.glob(F'{checkpoint_path}/global_step*'					)					)

					if len(a_					) > 0:
										logger.info(F'Attempting to resume from {checkpoint_path}'					)
										# this magically updates self.optimizer and self.lr_scheduler
										__magic_name__,						__magic_name__				=     deepspeed_engine.load_checkpoint(
										    a_       ,load_optimizer_states=a_       ,load_lr_scheduler_states=a_					)
										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}'					)







from typing import Any

import numpy as np

class 					__UpperCamelCase					:







	def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						if np.any(tableau[:, -1]	,			where=tableau[:, -1] < 0   ):
											raise ValueError('RHS must be > 0'   )

						__magic_name__				=     tableau
						__magic_name__,						__magic_name__				=     tableau.shape

						# Number of decision variables x1, x2, x3...
						__magic_name__				=     n_vars

						# Number of artificial variables to be minimised
						__magic_name__				=     len(np.where(tableau[self.n_vars : -1] == -1   )[0]   )

						# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
						__magic_name__				=     (self.n_art_vars > 0) + 1

						# Number of slack variables added to make inequalities into equalities
						__magic_name__				=     self.n_rows - self.n_stages

						# Objectives for each stage
						__magic_name__				=     ['max']

						# In two stage simplex, first minimise then maximise
						if self.n_art_vars:
											self.objectives.append('min'   )

						__magic_name__				=     ['']

						# Index of current pivot row and column
						__magic_name__				=     None
						__magic_name__				=     None

						# Does objective row only contain (non)-negative values?
						__magic_name__				=     False







	@staticmethod
	def 						_SCREAMING_SNAKE_CASE				( *__UpperCamelCase:			int   ):

						'''simple docstring'''





						if len(__UpperCamelCase   ) != 3:
											raise ValueError('Must provide n_vars, n_slack, and n_art_vars'   )

						if not all(x >= 0 and isinstance(__UpperCamelCase	,			__UpperCamelCase   ) for x in args   ):
											raise ValueError('All arguments must be non-negative integers'   )

						# decision | slack | artificial
						__magic_name__				=     ['x', 's', 'a']
						__magic_name__				=     []
						for i in range(3   ):
											for j in range(args[i]   ):
																titles.append(string_starts[i] + str(j + 1   )   )
						titles.append('RHS'   )
						return titles







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]	,			__UpperCamelCase:			np.ndarray   ):

						'''simple docstring'''





						__magic_name__				=     self.objectives[-1]

						# Find entries of highest magnitude in objective rows
						__magic_name__				=     (objective == 'min') - (objective == 'max')
						__magic_name__				=     np.argmax(sign * tableau[0, : self.n_vars]   )

						# Choice is only valid if below 0 for maximise, and above for minimise
						if sign * self.tableau[0, col_idx] <= 0:
											__magic_name__				=     True
											return 0, 0

						# Pivot row is chosen as having the lowest quotient when elements of
						# the pivot column divide the right-hand side

						# Slice excluding the objective rows
						__magic_name__				=     slice(self.n_stages	,			self.n_rows   )

						# RHS
						__magic_name__				=     tableau[s, -1]

						# Elements of pivot column within slice
						__magic_name__				=     tableau[s, col_idx]

						# Array filled with nans
						__magic_name__				=     np.full(self.n_rows - self.n_stages	,			np.nan   )

						# If element in pivot column is greater than zeron_stages, return
						# quotient or nan otherwise
						__magic_name__				=     np.divide(__UpperCamelCase	,			__UpperCamelCase	,			out=__UpperCamelCase	,			where=divisor > 0   )

						# Arg of minimum quotient excluding the nan values. n_stages is added
						# to compensate for earlier exclusion of objective columns
						__magic_name__				=     np.nanargmin(__UpperCamelCase   ) + self.n_stages
						return row_idx, col_idx







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			int	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						__magic_name__				=     tableau[row_idx].copy()

						__magic_name__				=     piv_row[col_idx]

						# Entry becomes 1
						piv_row *= 1 / piv_val

						# Variable in pivot column becomes basic, ie the only non-zero entry
						for idx, coeff in enumerate(tableau[:, col_idx]   ):
											tableau[idx] += -coeff * piv_row
						__magic_name__				=     piv_row
						return tableau







	def 						_SCREAMING_SNAKE_CASE				( self:			int	,			__UpperCamelCase:			np.ndarray   ):

						'''simple docstring'''





						self.objectives.pop()

						if not self.objectives:
											return tableau

						# Slice containing ids for artificial columns
						__magic_name__				=     slice(-self.n_art_vars - 1	,			-1   )

						# Delete the artificial variable columns
						__magic_name__				=     np.delete(__UpperCamelCase	,			__UpperCamelCase	,			axis=1   )

						# Delete the objective row of the first stage
						__magic_name__				=     np.delete(__UpperCamelCase	,			0	,			axis=0   )

						__magic_name__				=     1
						self.n_rows -= 1
						__magic_name__				=     0
						__magic_name__				=     False
						return tableau







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						for _ in range(1_00   ):
											# Completion of each stage removes an objective. If both stages
											# are complete, then no objectives are left
											if not self.objectives:
																__magic_name__				=     self.generate_col_titles(
																    self.n_vars	,			self.n_slack	,			self.n_art_vars   )

																# Find the values of each variable at optimal solution
																return self.interpret_tableau(self.tableau	,			self.col_titles   )

											__magic_name__,						__magic_name__				=     self.find_pivot(self.tableau   )

											# If there are no more negative values in objective row
											if self.stop_iter:
																# Delete artificial variable columns and rows. Update attributes
																__magic_name__				=     self.change_stage(self.tableau   )
											else:
																__magic_name__				=     self.pivot(self.tableau	,			__UpperCamelCase	,			__UpperCamelCase   )
						return {}







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			list[str]   ):

						'''simple docstring'''





						__magic_name__				=     {'P': abs(tableau[0, -1]   )}

						for i in range(self.n_vars   ):
											# Gives ids of nonzero entries in the ith column
											__magic_name__				=     np.nonzero(tableau[:, i]   )
											__magic_name__				=     len(nonzero[0]   )

											# First entry in the nonzero ids
											__magic_name__				=     nonzero[0][0]
											__magic_name__				=     tableau[nonzero_rowidx, i]

											# If there is only one nonzero value in column, which is one
											if n_nonzero == nonzero_val == 1:
																__magic_name__				=     tableau[nonzero_rowidx, -1]
																__magic_name__				=     rhs_val

        # Check for basic variables
						for title in col_titles:
											# Don't add RHS or slack variables to output dict
											if title[0] not in "R-s-a":
																output_dict.setdefault(__UpperCamelCase	,			0   )
						return output_dict


if __name__ == "__main__":
						import doctest

						doctest.testmod()




