






'''simple docstring'''





import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
		from tests.trainer.test_trainer import (  # noqa
		    RegressionModelConfig,
		    RegressionPreTrainedModel,
		    get_regression_trainer,
		)


set_seed(4_2)

lowerCamelCase_		  =						get_tests_dir("""fixtures""")
lowerCamelCase_		  =						os.path.join(dirname(get_tests_dir()))
lowerCamelCase_		  =						dirname(os.path.abspath(__file__))

# default torch.distributed port
lowerCamelCase_		  =						"""10999"""

lowerCamelCase_		  =						"""t5-small"""

# *** Working Models ***
lowerCamelCase_		  =						"""hf-internal-testing/tiny-albert"""
lowerCamelCase_		  =						"""sshleifer/bart-tiny-random"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-bert"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-bigbird_pegasus"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-big_bird"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-blenderbot"""
lowerCamelCase_		  =						"""bigscience/bigscience-small-testing"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-deberta"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-deberta-v2"""
lowerCamelCase_		  =						"""sshleifer/tiny-distilbert-base-cased"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-electra"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-flaubert"""
lowerCamelCase_		  =						"""stas/tiny-wmt19-en-de"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-funnel"""
lowerCamelCase_		  =						"""sshleifer/tiny-gpt2"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-gptj"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-gpt_neo"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-layoutlm"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-led"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-longformer"""
lowerCamelCase_		  =						"""stas/tiny-m2m_100"""  # hf tiny model is unsuitable
lowerCamelCase_		  =						"""sshleifer/tiny-marian-en-de"""
lowerCamelCase_		  =						"""sshleifer/tiny-mbart"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-mobilebert"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-mpnet"""
lowerCamelCase_		  =						"""stas/pegasus-cnn_dailymail-tiny-random"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-prophetnet"""
lowerCamelCase_		  =						"""sshleifer/tiny-distilroberta-base"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-squeezebert"""
lowerCamelCase_		  =						"""patrickvonplaten/t5-tiny-random"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-t5-v1.1"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-vit"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-xlm-roberta"""
lowerCamelCase_		  =						"""sshleifer/tiny-xlnet-base-cased"""


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-mt5"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-camembert"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-convbert"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-layoutlmv2"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-ctrl"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
lowerCamelCase_		  =						""""""
# - "luke" Entities classes - no plan to make such example
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-speech_to_text"""
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-tapas"""
def   lowerCamelCase      (		a_=False			)			->    Tuple:
							# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
							# - it won't be able to handle that
							# 2. for now testing with just 2 gpus max (since some quality tests may give different
							# results with mode gpus because we use very little data)
							lowerCAmelCase_       		=  min(2				,		get_gpu_count()			) if distributed else 1
							lowerCAmelCase_       		=  os.environ.get('DS_TEST_PORT'				,		a_			)
							return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()
def   lowerCamelCase      (		)			->    Union[str, Any]:
							lowerCAmelCase_       		=  F'''{FIXTURE_DIRECTORY}/tests_samples'''
							lowerCAmelCase_       		=  F'''{data_dir_samples}/wmt_en_ro'''
							lowerCAmelCase_       		=  F'''{data_dir_samples}/xsum'''
							lowerCAmelCase_       		=  '\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        '.split()

							# try to cover as many models as possible once (it's enough to run on one task per model)
							# but need a tiny model for each
							#
							# should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
							lowerCAmelCase_       		=  {
							    'trans': [
							        'bart',
							        'fsmt',
							        'm2m_100',
							        'marian',
							        'mbart',
							        't5',
							        't5_v1',
							        # "mt5", missing model files
							    ],
							    'sum': [
							        'pegasus',
							    ],
							    'clm': [
							        'big_bird',
							        'bigbird_pegasus',
							        'blenderbot',
							        'bloom',
							        'gpt2',
							        'gpt_neo',
							        'gptj',
							        'xlm-roberta',
							        'prophetnet',
							        # "camembert", missing model files
							    ],
							    'mlm': [
							        'albert',
							        'deberta',
							        'deberta-v2',
							        'distilbert',
							        'electra',
							        'flaubert',
							        'funnel',
							        'layoutlm',
							        # "reformer", # multiple issues with either mlm/qa/clas
							    ],
							    'qa': [
							        'led',
							        'longformer',
							        'mobilebert',
							        'mpnet',
							        'roberta',
							        'squeezebert',
							        # "convbert", # missing tokenizer files
							        # "layoutlmv2", missing model files
							    ],
							    'clas': [
							        'bert',
							        'xlnet',
							        # "hubert", # missing tokenizer files
							        # "ibert", # multiple issues with either mlm/qa/clas
							        # "transfo-xl", # tokenizer issues as ctrl
							        # "ctrl", # tokenizer issues
							        # "openai-gpt", missing model files
							        # "tapas", multiple issues
							    ],
							    'img_clas': [
							        'vit',
							    ],
							}

							lowerCAmelCase_       		=  F'''{ROOT_DIRECTORY}/examples/pytorch'''

							lowerCAmelCase_       		=  {
							    'trans': F'''
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        ''',
							    'sum': F'''
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        ''',
							    'clm': F'''
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        ''',
							    'mlm': F'''
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        ''',
							    'qa': F'''
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        ''',
							    'clas': F'''
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        ''',
							    'img_clas': F'''
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        ''',
							}

							lowerCAmelCase_       		=  get_launcher(distributed=a_			)

							lowerCAmelCase_       		=  {}
							for task, args in tasks.items():
														lowerCAmelCase_       		=  args.split()
														for model in tasksamodels[task]:
																					lowerCAmelCase_       		=  globals()[F'''{model.upper().replace("-"				,		"_"			)}_TINY''']
																					lowerCAmelCase_       		=  F'''--model_name_or_path {model_name}'''.split()
																					lowerCAmelCase_       		=  launcher + args + args_model + args_main

																					# # generation special case
																					# if task == "gen":
																					#     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
																					#     args_model += f"--model_type {model}".split()
																					#     cmds[f"{task}_{model}"] = launcher + args + args_model
																					# else:

							return cmds


lowerCamelCase_		  =						make_task_cmds()

lowerCamelCase_		  =						"""zero2"""
lowerCamelCase_		  =						"""zero3"""

lowerCamelCase_		  =						[ZEROa, ZEROa]
def   lowerCamelCase      (		a_				,		a_				,		a_			)			->    Tuple:
							# customize the test name generator function as we want both params to appear in the sub-test
							# name, as by default it shows only the first param
							lowerCAmelCase_       		=  parameterized.to_safe_name('_'.join(str(a_			) for x in param.args			)			)
							return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
lowerCamelCase_		  =						list(itertools.product(stages, task_cmds.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class 							a_				(		a_	):


							'''simple docstring'''






							def   _lowercase (	self    ,    lowercase_    ,    lowercase_     )      ->      Any:
														'''simple docstring'''

														if task not in task_cmds:
																					raise ValueError(f'''don\'t know of task {task}, have {task_cmds.keys()}'''     )

														lowerCAmelCase_       		=  task_cmds[task]
														lowerCAmelCase_       		=  f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

														lowerCAmelCase_       		=  self.get_auto_remove_tmp_dir()
														lowerCAmelCase_       		=  f'''--output_dir {output_dir}'''.split()

														cmd += args_ds + args_out

														return cmd, output_dir






							@parameterized.expand(lowercase_    ,    name_func=lowercase_     )
							def   _lowercase (	self    ,    lowercase_    ,    lowercase_     )      ->      Optional[int]:
														'''simple docstring'''

														lowerCAmelCase_		,     lowerCAmelCase_       		=  self.get_task_cmd(lowercase_    ,    lowercase_     )

														# 1. generate the checkpoint
														cmd += "--save_steps 1".split()
														# keep for quick debug
														# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
														execute_subprocess_async(lowercase_    ,    env=self.get_env()     )

														# 2. test that the fp32 weights get reconsolidated
														lowerCAmelCase_       		=  f'''{output_dir}/checkpoint-1'''
														lowerCAmelCase_       		=  f'''{chkpt_dir}/out.bin'''
														lowerCAmelCase_       		=  f'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
														# keep for quick debug
														# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
														subprocess.check_call(lowercase_    ,    shell=lowercase_     )
														assert os.path.exists(lowercase_     ), f'''{recovered_model_path} was not found'''

														# possibly could also test that the resulting saved model is usable but given that we use
														# random models we won't know if it's any good











import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed




class 							a_				(		a_	):


							'''simple docstring'''






							def __init__(	self    ,    lowercase_    ,    lowercase_    ,    lowercase_=False    ,    lowercase_=1_0_2_4    ,    lowercase_=1_0_2_4    ,    lowercase_=3.6    ,    lowercase_=False    ,    )      ->      Any:
														'''simple docstring'''

														lowerCAmelCase_       		=  tokenizer
														lowerCAmelCase_       		=  tokenizer.bos_token_id
														lowerCAmelCase_       		=  dataset
														lowerCAmelCase_       		=  seq_length
														lowerCAmelCase_       		=  0
														lowerCAmelCase_       		=  infinite
														lowerCAmelCase_       		=  0
														lowerCAmelCase_       		=  tokenized

														if self.tokenized:
																					lowerCAmelCase_       		=  seq_length * num_of_sequences
																					lowerCAmelCase_       		=  'input_ids'
														else:
																					lowerCAmelCase_       		=  seq_length * chars_per_token * num_of_sequences
																					lowerCAmelCase_       		=  'content'

							def __iter__(	self     )      ->      Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  iter(self.dataset     )
														lowerCAmelCase_       		=  True
														while more_examples:
																					lowerCAmelCase_		,     lowerCAmelCase_       		=  [], 0
																					while True:
																												if buffer_len >= self.max_buffer_size:
																																			break
																												try:
																																			buffer.append(next(lowercase_     )[self.content_field]     )
																																			buffer_len += len(buffer[-1]     )
																												except StopIteration:
																																			if self.infinite:
																																										lowerCAmelCase_       		=  iter(self.dataset     )
																																										self.epoch += 1
																																										logger.info(f'''Dataset epoch: {self.epoch}'''     )
																																			else:
																																										lowerCAmelCase_       		=  False
																																										break
																					if self.tokenized:
																												lowerCAmelCase_       		=  buffer
																					else:
																												lowerCAmelCase_       		=  self.tokenizer(lowercase_    ,    truncation=lowercase_     )['input_ids']
																					lowerCAmelCase_       		=  []
																					for tokenized_input in tokenized_inputs:
																												all_token_ids.extend(tokenized_input + [self.concat_token_id]     )
																					for i in range(0    ,    len(lowercase_     )    ,    self.seq_length     ):
																												lowerCAmelCase_       		=  all_token_ids[i : i + self.seq_length]
																												if len(lowercase_     ) == self.seq_length:
																																			self.current_size += 1
																																			yield torch.tensor(lowercase_     )






							def   _lowercase (	self    ,    lowercase_=1_0_0_0     )      ->      Any:
														'''simple docstring'''

														return ShufflerIterDataPipe(self    ,    buffer_size=lowercase_     )


def   lowerCamelCase      (		a_			)			->    Any:
							lowerCAmelCase_       		=  args.model_ckpt.split('/'			)[-1]
							lowerCAmelCase_       		=  logging.getLogger(__name__			)
							lowerCAmelCase_       		=  Path(args.save_dir			) / 'log/'
							log_dir.mkdir(exist_ok=a_			)
							lowerCAmelCase_       		=  F'''debug_{accelerator.process_index}.log'''
							logging.basicConfig(
							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'				,		datefmt='%m/%d/%Y %H:%M:%S'				,		level=logging.INFO				,		handlers=[logging.FileHandler(log_dir / filename			), logging.StreamHandler()]				,		)
							if accelerator.is_main_process:  # we only want to setup logging once
														accelerator.init_trackers(a_				,		vars(a_			)			)
														lowerCAmelCase_       		=  accelerator.trackers[0].run.name
														logger.setLevel(logging.INFO			)
														datasets.utils.logging.set_verbosity_info()
														transformers.utils.logging.set_verbosity_info()
							else:
														lowerCAmelCase_       		=  ''
														logger.setLevel(logging.ERROR			)
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()
							return logger, run_name
def   lowerCamelCase      (		a_			)			->    Union[str, Any]:
							lowerCAmelCase_       		=  {'streaming': True}
							lowerCAmelCase_       		=  load_dataset(args.dataset_name_train				,		split='train'				,		**a_			)
							lowerCAmelCase_       		=  train_data.shuffle(buffer_size=args.shuffle_buffer				,		seed=args.seed			)
							lowerCAmelCase_       		=  load_dataset(args.dataset_name_valid				,		split='train'				,		**a_			)
							lowerCAmelCase_       		=  ConstantLengthDataset(
							    a_				,		a_				,		infinite=a_				,		seq_length=args.seq_length				,		tokenized=args.tokenized			)
							lowerCAmelCase_       		=  ConstantLengthDataset(
							    a_				,		a_				,		infinite=a_				,		seq_length=args.seq_length				,		tokenized=args.tokenized			)
							lowerCAmelCase_       		=  train_dataset.shuffle(buffer_size=args.shuffle_buffer			)
							lowerCAmelCase_       		=  DataLoader(a_				,		batch_size=args.train_batch_size				,		shuffle=a_			)
							lowerCAmelCase_       		=  DataLoader(a_				,		batch_size=args.valid_batch_size			)
							return train_dataloader, eval_dataloader
def   lowerCamelCase      (		a_				,		a_				,		a_=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]			)			->    List[str]:
							lowerCAmelCase_		,     lowerCAmelCase_       		=  [], []
							for n, p in model.named_parameters():
														if any(nd in n for nd in no_decay			):
																					params_without_wd.append(a_			)
														else:
																					params_with_wd.append(a_			)
							return [
							    {"params": params_with_wd, "weight_decay": args.weight_decay},
							    {"params": params_without_wd, "weight_decay": 0.0},
							]
def   lowerCamelCase      (		a_				,		a_			)			->    List[str]:
							logger.info(F'''Step {step}: {metrics}'''			)
							if accelerator.is_main_process:
														accelerator.log(a_				,		a_			)
def   lowerCamelCase      (		a_				,		a_				,		a_			)			->    Tuple:
							# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
							lowerCAmelCase_       		=  accelerator.unwrap_model(a_			).config
							lowerCAmelCase_       		=  4 if args.gradient_checkpointing else 3
							lowerCAmelCase_       		=  args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
							lowerCAmelCase_       		=  24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
							lowerCAmelCase_       		=  factor * (
							    1.0
							    + (args.seq_length / (6.0 * config_model.n_embd))
							    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
							)
							lowerCAmelCase_       		=  flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
							return tflops
def   lowerCamelCase      (		a_			)			->    List[Any]:
							model.eval()
							lowerCAmelCase_       		=  []
							for step, batch in enumerate(a_			):
														with torch.no_grad():
																					lowerCAmelCase_       		=  model(a_				,		labels=a_			)
														lowerCAmelCase_       		=  outputs.loss.repeat(args.valid_batch_size			)
														losses.append(accelerator.gather(a_			)			)
														if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																					break
							lowerCAmelCase_       		=  torch.cat(a_			)
							lowerCAmelCase_       		=  losses[: eval_dataloader.dataset.current_size].mean()
							try:
														lowerCAmelCase_       		=  torch.exp(a_			)
							except OverflowError:
														lowerCAmelCase_       		=  float('inf'			)
							return loss.item(), perplexity.item()


# Settings
lowerCamelCase_		  =						HfArgumentParser(TrainingArguments)
lowerCamelCase_		  =						parser.parse_args()

# Accelerator
lowerCamelCase_		  =						ProjectConfiguration(project_dir=args.save_dir, logging_dir="""log""")
lowerCamelCase_		  =						Accelerator(log_with=["""wandb""", """tensorboard"""], project_config=config)
lowerCamelCase_		  =						{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

lowerCamelCase_		  =						Namespace(**vars(args), **acc_state)
lowerCamelCase_		  =						accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
		lowerCamelCase_		  =						Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
lowerCamelCase_  ,  lowerCamelCase_		  =						setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
		hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
lowerCamelCase_		  =						AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
		model.gradient_checkpointing_enable()
lowerCamelCase_		  =						AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
lowerCamelCase_  ,  lowerCamelCase_		  =						create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
lowerCamelCase_		  =						AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowerCamelCase_		  =						get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)
def   lowerCamelCase      (		)			->    str:
							return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
lowerCamelCase_  ,  lowerCamelCase_  ,  lowerCamelCase_  ,  lowerCamelCase_		  =						accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
				accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}''')
				accelerator.load_state(args.resume_from_checkpoint)
				lowerCamelCase_		  =						os.path.basename(args.resume_from_checkpoint)
		else:
				# Get the most recent checkpoint
				lowerCamelCase_		  =						[f.name for f in os.scandir(args.save_dir) if f.is_dir() and """step""" in str(f)]
				dirs.sort(key=os.path.getctime)
				lowerCamelCase_		  =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract the step of the checkpoint to continue from there
		lowerCamelCase_		  =						os.path.splitext(path)[0]
		lowerCamelCase_		  =						int(training_difference.replace("""step_""", """"""))

# Train model
model.train()
lowerCamelCase_		  =						0
lowerCamelCase_		  =						time.time()
lowerCamelCase_		  =						0
for step, batch in enumerate(train_dataloader, start=1):
		if args.resume_from_checkpoint and step < resume_step:
				continue  # we need to skip steps until we reach the resumed step
		lowerCamelCase_		  =						model(batch, labels=batch, use_cache=False).loss
		lowerCamelCase_		  =						accelerator.gather(loss.repeat(args.train_batch_size)).mean()
		loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
		log_metrics(step, {"""samples""": step * samples_per_step, """loss_per_step/train""": loss.item()})
		lowerCamelCase_		  =						loss / args.gradient_accumulation_steps
		if step % args.gradient_accumulation_steps != 0:
				# Prevent backward from doing gradient all_reduce in every step
				if accelerator.distributed_type == DistributedType.MULTI_GPU:
						with model.no_sync():
								accelerator.backward(loss)
				else:
						accelerator.backward(loss)
		else:
				lowerCamelCase_		  =						get_lr()
				accelerator.backward(loss)
				accelerator.clip_grad_norm_(model.parameters(), 1.0)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
				lowerCamelCase_		  =						time.time() - t_start
				lowerCamelCase_		  =						compute_tflops(elapsed_time, accelerator, args)
				log_metrics(
				    step,
				    {
				        """steps""": completed_steps,
				        """loss/train""": loss_tracking,
				        """lr""": lr,
				        """tflops""": tflops,
				        """time_per_iteration""": elapsed_time,
				    },
				)
				lowerCamelCase_		  =						time.time()
				lowerCamelCase_		  =						0
				completed_steps += 1
		if step % args.save_checkpoint_steps == 0:
				logger.info("""Evaluating and saving model checkpoint""")
				lowerCamelCase_  ,  lowerCamelCase_		  =						evaluate(args)
				log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
				accelerator.wait_for_everyone()
				lowerCamelCase_		  =						os.path.join(args.save_dir, f'''step_{step}''')
				accelerator.save_state(save_dir)
				if accelerator.is_main_process:
						hf_repo.push_to_hub(commit_message=f'''step {step}''')
				model.train()
		if completed_steps >= args.max_train_steps:
				break

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
lowerCamelCase_  ,  lowerCamelCase_		  =						evaluate(args)
log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
accelerator.wait_for_everyone()
lowerCamelCase_		  =						accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
lowerCamelCase_		  =						os.path.join(args.save_dir, f'''step_{step}''')
accelerator.save_state(save_dir)
if accelerator.is_main_process:
		hf_repo.push_to_hub(commit_message="""final model""")



