





"""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 	__lowercase  (     )     ->List[str]:



    '''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 	__snake_case      (	SCREAMING_SNAKE_CASE__  ):





 """simple docstring"""


 def __init__(					self      ,		__lowerCamelCase						):



     '''simple docstring'''




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






class 	__snake_case      (	SCREAMING_SNAKE_CASE__  ):





 """simple docstring"""


 def __init__(					self      ,		__lowerCamelCase						):



     '''simple docstring'''




     super().__init__(__lowerCamelCase						)
     __A					:		List[Any]						=  None
     __A					:		Tuple						=  []

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




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

 def       UpperCamelCase__(					self      ,		__lowerCamelCase						):



     '''simple docstring'''




     __A					:		List[str]						=  self.get_value(__lowerCamelCase						)
     if val is None:
         return False
     else:
         return val == "auto"

 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase=None      ,		__lowerCamelCase=True						):



     '''simple docstring'''




     __A					:		List[Any]						=  self.find_config_node(__lowerCamelCase						)
     if config is None:
         return

     if config.get(__lowerCamelCase						) == "auto":
         __A					:		Dict						=  hf_val
         return

     if not must_match:
         return

     __A					:		Optional[int]						=  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}"""						)

 _lowerCamelCase				 =  partialmethod(SCREAMING_SNAKE_CASE__							,							must_match=SCREAMING_SNAKE_CASE__  )

 def       UpperCamelCase__(					self      ,		__lowerCamelCase						):



     '''simple docstring'''




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







 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase						):



     '''simple docstring'''




     __A					:		Tuple						=  [
         '''zero_optimization.reduce_bucket_size''',
         '''zero_optimization.stage3_prefetch_bucket_size''',
         '''zero_optimization.stage3_param_persistence_threshold''',
     ]
     __A					:		int						=  [x for x in hidden_size_based_keys if self.is_auto(__lowerCamelCase						)]

     if len(__lowerCamelCase						) > 0:
         if hasattr(model.config      ,		'''hidden_size'''						):
             __A					:		List[str]						=  model.config.hidden_size
         elif hasattr(model.config      ,		'''hidden_sizes'''						):
             # if there are many hidden sizes pick the largest one
             __A					:		List[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'''      ,		10 * 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:
         __A					:		List[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 	__lowercase  (     snake_case_						:						str		)     ->List[str]:



    '''simple docstring'''







    global _hf_deepspeed_config_weak_ref
    # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
    __A					:		Tuple						=  weakref.ref(snake_case_		)







def 	__lowercase  (     )     ->Optional[Any]:



    '''simple docstring'''







    global _hf_deepspeed_config_weak_ref
    __A					:		Optional[Any]						=  None







def 	__lowercase  (     )     ->Optional[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().is_zeroa()
    else:
        return False







def 	__lowercase  (     )     ->Tuple:



    '''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  (     snake_case_						:						List[Any]       ,snake_case_						:						Union[str, Any]       ,snake_case_						:						str       ,snake_case_						:						List[Any]       ,snake_case_						:						str		)     ->int:



    '''simple docstring'''







    from accelerate.utils import DummyOptim, DummyScheduler

    __A					:		int						=  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					:		Optional[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.'''		)
        __A					:		Dict						=  DummyOptim(params=snake_case_		)
    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					:		List[str]						=  trainer.create_optimizer()
        # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
        __A					:		Optional[Any]						=  True

    __A					:		Optional[Any]						=  None
    if "scheduler" in config:
        __A					:		List[str]						=  DummyScheduler(snake_case_		)
    else:
        if isinstance(snake_case_       ,snake_case_		):
            raise ValueError(
                '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
                '''Please configure a scheduler in the DeepSpeed config.'''		)
        __A					:		Any						=  trainer.create_scheduler(num_training_steps=snake_case_       ,optimizer=snake_case_		)

    return optimizer, lr_scheduler







def 	__lowercase  (     snake_case_						:						List[str]       ,snake_case_						:						List[str]       ,snake_case_						:						int=False		)     ->int:



    '''simple docstring'''







    from deepspeed.utils import logger as ds_logger

    __A					:		int						=  trainer.model
    __A					:		int						=  trainer.args

    __A					:		Dict						=  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(snake_case_       ,snake_case_       ,snake_case_		)

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

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

    return optimizer, lr_scheduler







def 	__lowercase  (     snake_case_						:						List[Any]       ,snake_case_						:						Any		)     ->List[Any]:



    '''simple docstring'''







    import glob

    __A					:		List[Any]						=  sorted(glob.glob(F"""{checkpoint_path}/global_step*"""		)		)

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











"""simple docstring"""



import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

a_				=    pathlib.Path.cwd().joinpath("""project_euler""")
a_				=    pathlib.Path.cwd().joinpath(
    """scripts""", """project_euler_answers.json"""
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
       a_				=    json.load(file_handle)







def 	__lowercase  (     snake_case_						:						pathlib.Path		)     ->ModuleType:



    '''simple docstring'''







    __A					:		str						=  importlib.util.spec_from_file_location(file_path.name       ,str(snake_case_		)		)
    __A					:		Optional[int]						=  importlib.util.module_from_spec(snake_case_		)  # type: ignore
    spec.loader.exec_module(snake_case_		)  # type: ignore
    return module







def 	__lowercase  (     )     ->list[pathlib.Path]:



    '''simple docstring'''







    __A					:		Optional[int]						=  []
    for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
        if problem_dir_path.is_file() or problem_dir_path.name.startswith('''_'''		):
            continue
        for file_path in problem_dir_path.iterdir():
            if file_path.suffix != ".py" or file_path.name.startswith(('''_''', '''test''')		):
                continue
            solution_file_paths.append(snake_case_		)
    return solution_file_paths







def 	__lowercase  (     )     ->str:



    '''simple docstring'''







    with open(os.environ['''GITHUB_EVENT_PATH''']		) as file:
        __A					:		Any						=  json.load(snake_case_		)
    return event["pull_request"]["url"] + "/files"







def 	__lowercase  (     )     ->list[pathlib.Path]:



    '''simple docstring'''







    __A					:		int						=  []
    __A					:		Optional[int]						=  {
        '''Accept''': '''application/vnd.github.v3+json''',
        '''Authorization''': '''token ''' + os.environ['''GITHUB_TOKEN'''],
    }
    __A					:		Optional[Any]						=  requests.get(get_files_url()       ,headers=snake_case_		).json()
    for file in files:
        __A					:		Union[str, Any]						=  pathlib.Path.cwd().joinpath(file['''filename''']		)
        if (
            filepath.suffix != ".py"
            or filepath.name.startswith(('''_''', '''test''')		)
            or not filepath.name.startswith('''sol'''		)
        ):
            continue
        solution_file_paths.append(snake_case_		)
    return solution_file_paths







def 	__lowercase  (     )     ->list[pathlib.Path]:



    '''simple docstring'''







    if os.environ.get('''CI'''		) and os.environ.get('''GITHUB_EVENT_NAME'''		) == "pull_request":
        # Return only if there are any, otherwise default to all solutions
        if filepaths := added_solution_file_path():
            return filepaths
    return all_solution_file_paths()







@pytest.mark.parametrize(
    '''solution_path'''       ,collect_solution_file_paths()       ,ids=lambda snake_case_		: F"""{path.parent.name}/{path.name}"""       ,)
def 	__lowercase  (     snake_case_						:						pathlib.Path		)     ->None:



    '''simple docstring'''







    __A					:		str						=  solution_path.parent.name[8:].zfill(3		)
    __A					:		str						=  PROBLEM_ANSWERS[problem_number]
    __A					:		Union[str, Any]						=  convert_path_to_module(snake_case_		)
    __A					:		List[str]						=  str(solution_module.solution()		)  # type: ignore
    __A					:		List[str]						=  hashlib.shaaaa(answer.encode()		).hexdigest()
    assert (
        answer == expected
    ), F"""Expected solution to {problem_number} to have hash {expected}, got {answer}"""




