




'''simple docstring'''

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict





class __magic_name__(						__lowerCAmelCase      ):
						pass







def    snake_case__     (  a       ,      a       ,      a=None       ,      a=False	)			->       Union[str, Any]:





			'''simple docstring'''






			try:
						snake_case__          =						ast.parse(a	)
			except SyntaxError as e:
						print("""The code generated by the agent is not valid.\n"""       ,      a	)
						return
			if state is None:
						snake_case__          =						{}
			snake_case__          =						None
			for idx, node in enumerate(expression.body	):
						try:
									snake_case__          =						evaluate_ast(a       ,      a       ,      a	)
						except InterpretorError as e:
									snake_case__          =						F"""Evaluation of the code stopped at line {idx} before the end because of the following error"""
									if chat_mode:
												msg += (
												    F""". Copy paste the following error message and send it back to the agent:\nI get an error: '{e}'"""
												)
									else:
												msg += F""":\n{e}"""
									print(a	)
									break
						if line_result is not None:
									snake_case__          =						line_result

			return result




def    snake_case__     (  a       ,      a       ,      a	)			->       int:





			'''simple docstring'''






			if isinstance(a       ,      ast.Assign	):
						# Assignement -> we evaluate the assignement which should update the state
						# We return the variable assigned as it may be used to determine the final result.
						return evaluate_assign(a       ,      a       ,      a	)
			elif isinstance(a       ,      ast.Call	):
						# Function call -> we return the value of the function call
						return evaluate_call(a       ,      a       ,      a	)
			elif isinstance(a       ,      ast.Constant	):
						# Constant -> just return the value
						return expression.value
			elif isinstance(a       ,      ast.Dict	):
						# Dict -> evaluate all keys and values
						snake_case__          =						[evaluate_ast(a       ,      a       ,      a	) for k in expression.keys]
						snake_case__          =						[evaluate_ast(a       ,      a       ,      a	) for v in expression.values]
						return dict(zip(a       ,      a	)	)
			elif isinstance(a       ,      ast.Expr	):
						# Expression -> evaluate the content
						return evaluate_ast(expression.value       ,      a       ,      a	)
			elif isinstance(a       ,      ast.For	):
						# For loop -> execute the loop
						return evaluate_for(a       ,      a       ,      a	)
			elif isinstance(a       ,      ast.FormattedValue	):
						# Formatted value (part of f-string) -> evaluate the content and return
						return evaluate_ast(expression.value       ,      a       ,      a	)
			elif isinstance(a       ,      ast.If	):
						# If -> execute the right branch
						return evaluate_if(a       ,      a       ,      a	)
			elif hasattr(a       ,      """Index"""	) and isinstance(a       ,      ast.Index	):
						return evaluate_ast(expression.value       ,      a       ,      a	)
			elif isinstance(a       ,      ast.JoinedStr	):
						return "".join([str(evaluate_ast(a       ,      a       ,      a	)	) for v in expression.values]	)
			elif isinstance(a       ,      ast.List	):
						# List -> evaluate all elements
						return [evaluate_ast(a       ,      a       ,      a	) for elt in expression.elts]
			elif isinstance(a       ,      ast.Name	):
						# Name -> pick up the value in the state
						return evaluate_name(a       ,      a       ,      a	)
			elif isinstance(a       ,      ast.Subscript	):
						# Subscript -> return the value of the indexing
						return evaluate_subscript(a       ,      a       ,      a	)
			else:
						# For now we refuse anything else. Let's add things as we need them.
						raise InterpretorError(F"""{expression.__class__.__name__} is not supported."""	)




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





			'''simple docstring'''






			snake_case__          =						assign.targets
			snake_case__          =						evaluate_ast(assign.value       ,      a       ,      a	)

			if len(a	) == 1:
						snake_case__          =						result
			else:
						if len(a	) != len(a	):
									raise InterpretorError(F"""Expected {len(a	)} values but got {len(a	)}."""	)
						for var_name, r in zip(a       ,      a	):
									snake_case__          =						r
			return result




def    snake_case__     (  a       ,      a       ,      a	)			->       int:





			'''simple docstring'''






			if not isinstance(call.func       ,      ast.Name	):
						raise InterpretorError(
						    F"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of """
						    F"""type {type(call.func	)}."""	)
			snake_case__          =						call.func.id
			if func_name not in tools:
						raise InterpretorError(
						    F"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id})."""	)

			snake_case__          =						tools[func_name]
			# Todo deal with args
			snake_case__          =						[evaluate_ast(a       ,      a       ,      a	) for arg in call.args]
			snake_case__          =						{keyword.arg: evaluate_ast(keyword.value       ,      a       ,      a	) for keyword in call.keywords}
			return func(*a       ,      **a	)




def    snake_case__     (  a       ,      a       ,      a	)			->       Tuple:





			'''simple docstring'''






			snake_case__          =						evaluate_ast(subscript.slice       ,      a       ,      a	)
			snake_case__          =						evaluate_ast(subscript.value       ,      a       ,      a	)
			if isinstance(a       ,      (list, tuple)	):
						return value[int(a	)]
			if index in value:
						return value[index]
			if isinstance(a       ,      a	) and isinstance(a       ,      a	):
						snake_case__          =						difflib.get_close_matches(a       ,      list(value.keys()	)	)
						if len(a	) > 0:
									return value[close_matches[0]]

			raise InterpretorError(F"""Could not index {value} with '{index}'."""	)




def    snake_case__     (  a       ,      a       ,      a	)			->       Any:





			'''simple docstring'''






			if name.id in state:
						return state[name.id]
			snake_case__          =						difflib.get_close_matches(name.id       ,      list(state.keys()	)	)
			if len(a	) > 0:
						return state[close_matches[0]]
			raise InterpretorError(F"""The variable `{name.id}` is not defined."""	)




def    snake_case__     (  a       ,      a       ,      a	)			->       str:





			'''simple docstring'''






			if len(condition.ops	) > 1:
						raise InterpretorError("""Cannot evaluate conditions with multiple operators"""	)

			snake_case__          =						evaluate_ast(condition.left       ,      a       ,      a	)
			snake_case__          =						condition.ops[0]
			snake_case__          =						evaluate_ast(condition.comparators[0]       ,      a       ,      a	)

			if isinstance(a       ,      ast.Eq	):
						return left == right
			elif isinstance(a       ,      ast.NotEq	):
						return left != right
			elif isinstance(a       ,      ast.Lt	):
						return left < right
			elif isinstance(a       ,      ast.LtE	):
						return left <= right
			elif isinstance(a       ,      ast.Gt	):
						return left > right
			elif isinstance(a       ,      ast.GtE	):
						return left >= right
			elif isinstance(a       ,      ast.Is	):
						return left is right
			elif isinstance(a       ,      ast.IsNot	):
						return left is not right
			elif isinstance(a       ,      ast.In	):
						return left in right
			elif isinstance(a       ,      ast.NotIn	):
						return left not in right
			else:
						raise InterpretorError(F"""Operator not supported: {comparator}"""	)




def    snake_case__     (  a       ,      a       ,      a	)			->       Optional[int]:





			'''simple docstring'''






			snake_case__          =						None
			if evaluate_condition(if_statement.test       ,      a       ,      a	):
						for line in if_statement.body:
									snake_case__          =						evaluate_ast(a       ,      a       ,      a	)
									if line_result is not None:
												snake_case__          =						line_result
			else:
						for line in if_statement.orelse:
									snake_case__          =						evaluate_ast(a       ,      a       ,      a	)
									if line_result is not None:
												snake_case__          =						line_result
			return result




def    snake_case__     (  a       ,      a       ,      a	)			->       Any:





			'''simple docstring'''






			snake_case__          =						None
			snake_case__          =						evaluate_ast(for_loop.iter       ,      a       ,      a	)
			for counter in iterator:
						snake_case__          =						counter
						for expression in for_loop.body:
									snake_case__          =						evaluate_ast(a       ,      a       ,      a	)
									if line_result is not None:
												snake_case__          =						line_result
			return result







'''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

__A     						= logging.get_logger(__name__)




def 							_SCREAMING_SNAKE_CASE	(					)     ->			Optional[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 a_     (     UpperCamelCase_				):







   def __init__(self       , __a)      ->   Optional[Any]:
      """simple docstring"""






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




class a_     (     UpperCamelCase_				):







   def __init__(self       , __a)      ->   List[str]:
      """simple docstring"""






      super().__init__(__a)
      __snake_case					:   Dict  				=			None
      __snake_case					:   Optional[Any]  				=			[]







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """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       , __a)      ->   int:
      """simple docstring"""






      __snake_case					:   Dict  				=			self.get_value(__a)
      if val is None:
         return False
      else:
         return val == "auto"







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a=None       , __a=True)      ->   int:
      """simple docstring"""






      __snake_case				,__snake_case					:   str  				=			self.find_config_node(__a)
      if config is None:
         return

      if config.get(__a) == "auto":
         __snake_case					:   List[Any]  				=			hf_val
         return

      if not must_match:
         return

      __snake_case					:   Optional[Any]  				=			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}""")

   _snake_case	   =				partialmethod(UpperCamelCase_		,   must_match=UpperCamelCase_				)







   def      SCREAMING_SNAKE_CASE__       (self       , __a)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   str  				=			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:
         __snake_case					:   Optional[Any]  				=			'apex' if args.fpaa_backend == 'apex' else 'amp'
      else:
         __snake_case					:   Optional[int]  				=			None

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







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a)      ->   Any:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			[
          'zero_optimization.reduce_bucket_size',
          'zero_optimization.stage3_prefetch_bucket_size',
          'zero_optimization.stage3_param_persistence_threshold',
      ]
      __snake_case					:   int  				=			[x for x in hidden_size_based_keys if self.is_auto(__a)]

      if len(__a) > 0:
         if hasattr(model.config       , 'hidden_size'):
            __snake_case					:   Optional[int]  				=			model.config.hidden_size
         elif hasattr(model.config       , 'hidden_sizes'):
            # if there are many hidden sizes pick the largest one
            __snake_case					:   Tuple  				=			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:
         __snake_case					:   Any  				=			'\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 							_SCREAMING_SNAKE_CASE	(					A	:							Dict	)     ->			Union[str, Any]:







   """simple docstring"""
   # 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)
   __snake_case					:   List[Any]  				=			weakref.ref(A	)




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







   """simple docstring"""
   # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
   global _hf_deepspeed_config_weak_ref
   __snake_case					:   Optional[Any]  				=			None




def 							_SCREAMING_SNAKE_CASE	(					)     ->			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().is_zeroa()
   else:
      return False




def 							_SCREAMING_SNAKE_CASE	(					)     ->			List[Any]:







   """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 							_SCREAMING_SNAKE_CASE	(					A	:							Dict	,		A	:							int	,		A	:							Optional[Any]	,		A	:							Optional[Any]	,		A	:							str	)     ->			Union[str, Any]:







   """simple docstring"""
   from accelerate.utils import DummyOptim, DummyScheduler

   __snake_case					:   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)

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

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

   return optimizer, lr_scheduler




def 							_SCREAMING_SNAKE_CASE	(					A	:							Dict	,		A	:							List[Any]	,		A	:							str=False	)     ->			Union[str, Any]:







   """simple docstring"""
   from deepspeed.utils import logger as ds_logger

   __snake_case					:   int  				=			trainer.model
   __snake_case					:   Any  				=			trainer.args

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

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

   return optimizer, lr_scheduler





def 							_SCREAMING_SNAKE_CASE	(					A	:							Any	,		A	:							int	)     ->			List[Any]:







   """simple docstring"""
   # 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

   __snake_case					:   int  				=			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
      __snake_case				,__snake_case					:   Optional[Any]  				=			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}"""	)