
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   :					Dict    =			logging.get_logger(__name__)


def  A			(   )  ->    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 							lowercase   (						_SCREAMING_SNAKE_CASE  ):



		def __init__(					self							, A_			)    ->   Optional[Any]:
				"""simple docstring"""
				# set global weakref object
				set_hf_deepspeed_config(self			)
				dep_version_check('accelerate'			)
				dep_version_check('deepspeed'			)
				super().__init__(A_			)






class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):



		def __init__(					self							, A_			)    ->   Any:
				"""simple docstring"""
				super().__init__(A_			)
				UpperCamelCase            =	None
				UpperCamelCase            =	[]



		def __UpperCamelCase					(					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 __UpperCamelCase					(					self							, A_			)    ->   Tuple:
				"""simple docstring"""
				UpperCamelCase            =	self.get_value(A_			)
				if val is None:
						return False
				else:
						return val == "auto"



		def __UpperCamelCase					(					self							, A_							, A_							, A_=None							, A_=True			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase      ,    UpperCamelCase            =	self.find_config_node(A_			)
				if config is None:
						return

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

				if not must_match:
						return

				UpperCamelCase            =	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}'''			)

		__lowercase			:			Any      	=							partialmethod(_SCREAMING_SNAKE_CASE    ,  must_match=_SCREAMING_SNAKE_CASE  )



		def __UpperCamelCase					(					self							, A_			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	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:
						UpperCamelCase            =	'apex' if args.fpaa_backend == 'apex' else 'amp'
				else:
						UpperCamelCase            =	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            =	self.config.get('checkpoint'							, {}			)
						UpperCamelCase            =	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            =	torch.bfloataa
				elif self.is_false('fp16.enabled'			):
						UpperCamelCase            =	torch.floataa
				else:
						UpperCamelCase            =	torch.floataa



		def __UpperCamelCase					(					self							, A_							, A_							, A_			)    ->   Dict:
				"""simple docstring"""
				UpperCamelCase            =	[
				    'zero_optimization.reduce_bucket_size',
				    'zero_optimization.stage3_prefetch_bucket_size',
				    'zero_optimization.stage3_param_persistence_threshold',
				]
				UpperCamelCase            =	[x for x in hidden_size_based_keys if self.is_auto(A_			)]

				if len(A_			) > 0:
						if hasattr(model.config							, 'hidden_size'			):
								UpperCamelCase            =	model.config.hidden_size
						elif hasattr(model.config							, 'hidden_sizes'			):
								# if there are many hidden sizes pick the largest one
								UpperCamelCase            =	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'							, 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:
						UpperCamelCase            =	'\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   :					List[Any]    =			None


def  A			(   lowercase					)  ->    List[str]:
		'''simple docstring'''

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


def  A			(   )  ->    int:
		'''simple docstring'''

		global _hf_deepspeed_config_weak_ref
		UpperCamelCase            =	None


def  A			(   )  ->    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  A			(   )  ->    Optional[int]:
		'''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  A			(   lowercase       ,			lowercase       ,			lowercase       ,			lowercase       ,			lowercase					)  ->    Optional[Any]:
		'''simple docstring'''

		from accelerate.utils import DummyOptim, DummyScheduler

		UpperCamelCase            =	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            =	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            =	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            =	trainer.create_optimizer()
				# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
				UpperCamelCase            =	True

		UpperCamelCase            =	None
		if "scheduler" in config:
				UpperCamelCase            =	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            =	trainer.create_scheduler(num_training_steps=lowercase       ,			optimizer=lowercase					)

		return optimizer, lr_scheduler


def  A			(   lowercase       ,			lowercase       ,			lowercase=False					)  ->    int:
		'''simple docstring'''

		from deepspeed.utils import logger as ds_logger

		UpperCamelCase            =	trainer.model
		UpperCamelCase            =	trainer.args

		UpperCamelCase            =	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            =	None, None
				UpperCamelCase            =	None
		else:
				UpperCamelCase            =	None  # important for when deepspeed_init is used as re-init
				UpperCamelCase            =	list(filter(lambda lowercase					: p.requires_grad       ,			model.parameters()					)					)
				UpperCamelCase      ,    UpperCamelCase            =	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					)  ->    Union[str, Any]:
		'''simple docstring'''

		import glob

		UpperCamelCase            =	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            =	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}'''					)


import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
_UpperCAmelCase   :					List[str]    =			logging.getLogger(__name__)


def  A			(   )  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	argparse.ArgumentParser(description='PyTorch Transformer Language Model'					)
		parser.add_argument('--model_name'       ,			type=lowercase       ,			default='transfo-xl-wt103'       ,			help='pretrained model name'					)
		parser.add_argument(
		    '--split'       ,			type=lowercase       ,			default='test'       ,			choices=['all', 'valid', 'test']       ,			help='which split to evaluate'					)
		parser.add_argument('--batch_size'       ,			type=lowercase       ,			default=10       ,			help='batch size'					)
		parser.add_argument('--tgt_len'       ,			type=lowercase       ,			default=128       ,			help='number of tokens to predict'					)
		parser.add_argument('--ext_len'       ,			type=lowercase       ,			default=0       ,			help='length of the extended context'					)
		parser.add_argument('--mem_len'       ,			type=lowercase       ,			default=1_600       ,			help='length of the retained previous heads'					)
		parser.add_argument('--clamp_len'       ,			type=lowercase       ,			default=1_000       ,			help='max positional embedding index'					)
		parser.add_argument('--no_cuda'       ,			action='store_true'       ,			help='Do not use CUDA even though CUA is available'					)
		parser.add_argument('--work_dir'       ,			type=lowercase       ,			required=lowercase       ,			help='path to the work_dir'					)
		parser.add_argument('--no_log'       ,			action='store_true'       ,			help='do not log the eval result'					)
		parser.add_argument('--same_length'       ,			action='store_true'       ,			help='set same length attention with masking'					)
		parser.add_argument('--server_ip'       ,			type=lowercase       ,			default=''       ,			help='Can be used for distant debugging.'					)
		parser.add_argument('--server_port'       ,			type=lowercase       ,			default=''       ,			help='Can be used for distant debugging.'					)
		UpperCamelCase            =	parser.parse_args()
		assert args.ext_len >= 0, "extended context length must be non-negative"

		if args.server_ip and args.server_port:
				# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
				import ptvsd

				print('Waiting for debugger attach'					)
				ptvsd.enable_attach(address=(args.server_ip, args.server_port)       ,			redirect_output=lowercase					)
				ptvsd.wait_for_attach()

		UpperCamelCase            =	torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'					)
		logger.info('device: {}'.format(lowercase					)					)

		# Load a pre-processed dataset
		# You can also build the corpus yourself using TransfoXLCorpus methods
		# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
		# and tokenizing the dataset
		# The pre-processed corpus is a convertion (using the conversion script )
		UpperCamelCase            =	TransfoXLCorpus.from_pretrained(args.model_name					)

		UpperCamelCase            =	corpus.get_iterator('valid'       ,			args.batch_size       ,			args.tgt_len       ,			device=lowercase       ,			ext_len=args.ext_len					)
		UpperCamelCase            =	corpus.get_iterator('test'       ,			args.batch_size       ,			args.tgt_len       ,			device=lowercase       ,			ext_len=args.ext_len					)

		# Load a pre-trained model
		UpperCamelCase            =	TransfoXLLMHeadModel.from_pretrained(args.model_name					)
		model.to(lowercase					)

		logger.info(
		    'Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
		        args.batch_size       ,			args.tgt_len       ,			args.ext_len       ,			args.mem_len       ,			args.clamp_len					)					)

		model.reset_memory_length(args.mem_len					)
		if args.clamp_len > 0:
				UpperCamelCase            =	args.clamp_len
		if args.same_length:
				UpperCamelCase            =	True

		###############################################################################
		# Evaluation code
		###############################################################################
		def evaluate(lowercase					):
				# Turn on evaluation mode which disables dropout.
				model.eval()
				UpperCamelCase      ,    UpperCamelCase            =	0, 0.0
				UpperCamelCase            =	time.time()
				with torch.no_grad():
						UpperCamelCase            =	None
						for idx, (data, target, seq_len) in enumerate(lowercase					):
								UpperCamelCase            =	model(lowercase       ,			lm_labels=lowercase       ,			mems=lowercase					)
								UpperCamelCase      ,    UpperCamelCase      ,    UpperCamelCase            =	ret
								UpperCamelCase            =	loss.mean()
								total_loss += seq_len * loss.item()
								total_len += seq_len
						UpperCamelCase            =	time.time() - start_time
				logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(lowercase       ,			1_000 * total_time / (idx + 1)					)					)
				return total_loss / total_len

		# Run on test data.
		if args.split == "all":
				UpperCamelCase            =	evaluate(lowercase					)
				UpperCamelCase            =	evaluate(lowercase					)
		elif args.split == "valid":
				UpperCamelCase            =	evaluate(lowercase					)
				UpperCamelCase            =	None
		elif args.split == "test":
				UpperCamelCase            =	evaluate(lowercase					)
				UpperCamelCase            =	None

		def format_log(lowercase       ,			lowercase					):
				UpperCamelCase            =	'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(lowercase       ,			lowercase       ,			math.exp(lowercase					)					)
				return log_str

		UpperCamelCase            =	''
		if valid_loss is not None:
				log_str += format_log(lowercase       ,			'valid'					)
		if test_loss is not None:
				log_str += format_log(lowercase       ,			'test'					)

		logger.info('=' * 100					)
		logger.info(lowercase					)
		logger.info('=' * 100					)


if __name__ == "__main__":
					main()
