





import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase						:       int        =					logging.getLogger()
UpperCAmelCase						:       Tuple        =					torch.cuda.is_available()
UpperCAmelCase						:       int        =					{
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 42,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1E-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				List[Any]:
					__A     :						List[str]          =    '\n'.join(a  )
					Path(a  ).open('w'  ).writelines(a  )


UpperCAmelCase						:       Any        =					[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
UpperCAmelCase						:       List[str]        =					['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
UpperCAmelCase						:       Dict        =					'''patrickvonplaten/t5-tiny-random'''
UpperCAmelCase						:       Any        =					'''sshleifer/t5-tinier-random'''
UpperCAmelCase						:       Any        =					'''sshleifer/bart-tiny-random'''
UpperCAmelCase						:       str        =					'''sshleifer/tiny-mbart'''
UpperCAmelCase						:       Union[str, Any]        =					'''sshleifer/tiny-marian-en-de'''
UpperCAmelCase						:       Any        =					'''stas/tiny-wmt19-en-de'''


UpperCAmelCase						:       Optional[Any]        =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







def 			_SCREAMING_SNAKE_CASE    (       a  )				->				Tuple:
					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(a ,						F"""{split}.source"""  ) ,						a  )
										_dump_articles(os.path.join(a ,						F"""{split}.target"""  ) ,						a  )
					return tmp_dir





class 						_A( snake_case__       ):


				"""simple docstring"""


				@classmethod
				def  UpperCAmelCase_		(      cls					):
									logging.disable(logging.CRITICAL					)  # remove noisy download output from tracebacks
									return cls



				@slow
				@require_torch_gpu
				def  UpperCAmelCase_		(      self					):
									__A     :						Optional[int]          =    list_models()
									__A     :						Dict          =    'sshleifer'
									__A     :						Union[str, Any]          =    [x.modelId for x in model_list if x.modelId.startswith(_A					)]
									__A     :						Dict          =    ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
									__A     :						List[str]          =    []
									for m in model_ids:
														if m in allowed_to_be_broken:
																			continue
														try:
																			AutoConfig.from_pretrained(_A					)
														except Exception:
																			failures.append(_A					)
									assert not failures, F"""The following models could not be loaded through AutoConfig: {failures}"""



				def  UpperCAmelCase_		(      self					):
									__A     :						Union[str, Any]          =    {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
									self._test_distiller_cli(_A					)



				def  UpperCAmelCase_		(      self					):
									__A     :						List[str]          =    {
									    'student_encoder_layers': 2,
									    'student_decoder_layers': 1,
									    'max_epochs': 4,
									    'val_check_interval': 0.2_5,
									    'alpha_hid': 2.0,
									    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
									}
									__A     :						str          =    self._test_distiller_cli(_A					, check_contents=_A					)

									__A     :						List[Any]          =    list(Path(model.output_dir					).glob('*.ckpt'					)					)
									self.assertEqual(1					, len(_A					)					)
									__A     :						Tuple          =    list(Path(model.output_dir					).glob('**/*.bin'					)					)
									self.assertEqual(len(_A					)					, 2					)
									__A     :						List[str]          =    lmap(str.strip					, Path(model.hparams.data_dir					).joinpath('test.source'					).open().readlines()					)
									__A     :						Optional[int]          =    tempfile.mktemp()  # XXX: not being cleaned up
									generate_summaries_or_translations(_A					, _A					, str(model.output_dir / 'best_tfmr'					)					)
									self.assertTrue(Path(_A					).exists()					)

									__A     :						str          =    self.get_auto_remove_tmp_dir()
									convert_pl_to_hf(ckpts[0]					, transformer_ckpts[0].parent					, _A					)
									assert os.path.exists(os.path.join(_A					, 'pytorch_model.bin'					)					)



				def  UpperCAmelCase_		(      self					):
									__A     :						Union[str, Any]          =    AutoModelForSeqaSeqLM.from_pretrained(_A					)
									__A   ,    __A     :						Union[str, Any]          =    model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
									__A     :						Dict          =    torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]					, dtype=torch.long					, device=model.device					)
									__A     :						int          =    target_ids[:, :-1].contiguous()  # Why this line?
									__A     :						Any          =    target_ids[:, 1:].clone()  # why clone?
									__A     :						Union[str, Any]          =    model(
									    _A					, attention_mask=_A					, decoder_input_ids=_A					, labels=_A					, use_cache=_A					).loss

									__A     :						List[Any]          =    model(_A					, attention_mask=_A					, decoder_input_ids=_A					, use_cache=_A					).logits

									__A     :						List[Any]          =    nn.functional.log_softmax(_A					, dim=-1					)
									__A   ,    __A     :						List[Any]          =    label_smoothed_nll_loss(
									    _A					, _A					, 0.1					, ignore_index=model.config.pad_token_id					)
									with self.assertRaises(_A					):
														# TODO: understand why this breaks
														self.assertEqual(_A					, _A					)



				def  UpperCAmelCase_		(      self					):
									__A     :						List[str]          =    {
									    'student_encoder_layers': 2,
									    'student_decoder_layers': 1,
									    'num_train_epochs': 4,
									    'val_check_interval': 0.2_5,
									    'alpha_hid': 2.0,
									    'task': 'translation',
									    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
									    'tokenizer_name': MBART_TINY,
									    'teacher': MBART_TINY,
									    'src_lang': 'en_XX',
									    'tgt_lang': 'ro_RO',
									}
									__A     :						Optional[Any]          =    self._test_distiller_cli(_A					, check_contents=_A					)
									assert model.model.config.model_type == "mbart"

									__A     :						Optional[Any]          =    list(Path(model.output_dir					).glob('*.ckpt'					)					)
									self.assertEqual(1					, len(_A					)					)
									__A     :						Optional[int]          =    list(Path(model.output_dir					).glob('**/*.bin'					)					)
									__A     :						Optional[Any]          =    list(Path(model.output_dir					).glob('best_tfmr/*'					)					)
									assert len(_A					) > 2
									self.assertEqual(len(_A					)					, 2					)



				def  UpperCAmelCase_		(      self					):
									__A     :						List[str]          =    {
									    'student_encoder_layers': 1,
									    'student_decoder_layers': 1,
									    'alpha_hid': 2.0,
									    'teacher': T5_TINY,
									    'model_name_or_path': T5_TINY,
									    'tokenizer_name': T5_TINY,
									}
									self._test_distiller_cli(_A					)



				def  UpperCAmelCase_		(      self					):
									__A     :						Union[str, Any]          =    {
									    'teacher': T5_TINY,
									    'student': T5_TINIER,
									    'model_name_or_path': T5_TINIER,
									    'tokenizer_name': T5_TINIER,
									}
									self._test_distiller_cli(_A					)





				def  UpperCAmelCase_		(      self					, _A					, _A=True					):
									__A     :						List[str]          =    {
									    'label_smoothing': 0.0,
									    'early_stopping_patience': -1,
									    'train_batch_size': 1,
									    'eval_batch_size': 2,
									    'max_epochs': 2,
									    'alpha_mlm': 0.2,
									    'alpha_ce': 0.8,
									    'do_predict': True,
									    'model_name_or_path': 'sshleifer/tinier_bart',
									    'teacher': CHEAP_ARGS['model_name_or_path'],
									    'val_check_interval': 0.5,
									}
									default_updates.update(_A					)
									__A     :						dict          =    CHEAP_ARGS.copy()
									__A     :						List[str]          =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
									__A     :						Tuple          =    self.get_auto_remove_tmp_dir()

									args_d.update(data_dir=_A					, output_dir=_A					, **_A					)
									__A     :						List[str]          =    distill_main(argparse.Namespace(**_A					)					)
									if not check_contents:
														return model
									__A     :						Tuple          =    os.listdir(_A					)
									__A     :						List[str]          =    {os.path.basename(_A					) for p in contents}
									__A     :						List[str]          =    [p for p in contents if p.endswith('ckpt'					)]
									assert len(_A					) > 0

									self.assertIn('test_generations.txt'					, _A					)
									self.assertIn('test_results.txt'					, _A					)

									__A     :						Dict          =    load_json(model.metrics_save_path					)
									__A     :						List[str]          =    metrics['val'][-1]
									self.assertGreaterEqual(last_step_stats['val_avg_gen_time']					, 0.0_1					)
									self.assertGreaterEqual(1.0					, last_step_stats['val_avg_gen_time']					)
									self.assertIsInstance(last_step_stats[F"""val_avg_{model.val_metric}"""]					, _A					)
									__A     :						str          =    int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1					)
									self.assertEqual(len(metrics['val']					)					, _A					)
									self.assertEqual(len(metrics['test']					)					, 1					)
									return model





class 						_A( snake_case__       ):


				"""simple docstring"""


				@parameterized.expand(
				    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]					, )
				def  UpperCAmelCase_		(      self					, _A					):
									__A     :						dict          =    CHEAP_ARGS.copy()
									__A     :						Optional[int]          =    'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
									__A     :						Any          =    0.1 if task == 'translation' else 0

									__A     :						Optional[int]          =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
									__A     :						Any          =    self.get_auto_remove_tmp_dir()
									args_d.update(
									    data_dir=_A					, model_name_or_path=_A					, tokenizer_name=_A					, train_batch_size=2					, eval_batch_size=2					, output_dir=_A					, do_predict=_A					, task=_A					, src_lang='en_XX'					, tgt_lang='ro_RO'					, freeze_encoder=_A					, freeze_embeds=_A					, )
									assert "n_train" in args_d
									__A     :						Dict          =    argparse.Namespace(**_A					)
									__A     :						List[Any]          =    main(_A					)

									__A     :						Optional[Any]          =    module.model.get_input_embeddings()
									assert not input_embeds.weight.requires_grad
									if model == T5_TINY:
														__A     :						Optional[Any]          =    module.model.lm_head
														assert not lm_head.weight.requires_grad
														assert (lm_head.weight == input_embeds.weight).all().item()
									elif model == FSMT_TINY:
														__A     :						str          =    module.model.model
														__A     :						str          =    fsmt.decoder.embed_positions
														assert not embed_pos.weight.requires_grad
														assert not fsmt.decoder.embed_tokens.weight.requires_grad
														# check that embeds are not the same
														assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
									else:
														__A     :						Any          =    module.model.model
														__A     :						Tuple          =    bart.decoder.embed_positions
														assert not embed_pos.weight.requires_grad
														assert not bart.shared.weight.requires_grad
														# check that embeds are the same
														assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
														assert bart.decoder.embed_tokens == bart.shared

									__A     :						Tuple          =    load_json(module.output_dir / 'text_batch.json'					)
									assert isinstance(_A					, _A					)
									assert len(_A					) >= 4



				def  UpperCAmelCase_		(      self					):
									__A     :						dict          =    CHEAP_ARGS.copy()

									__A     :						List[Any]          =    'summarization'
									__A     :						int          =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)

									args_d.update(
									    data_dir=_A					, tokenizer_name=_A					, train_batch_size=2					, eval_batch_size=2					, do_predict=_A					, task=_A					, src_lang='en_XX'					, tgt_lang='ro_RO'					, freeze_encoder=_A					, freeze_embeds=_A					, )

									# test models whose config includes the extra_model_args
									__A     :						Dict          =    BART_TINY
									__A     :						Optional[int]          =    self.get_auto_remove_tmp_dir()
									__A     :						str          =    args_d.copy()
									args_da.update(
									    model_name_or_path=_A					, output_dir=_A					, )
									__A     :						str          =    ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
									for p in extra_model_params:
														__A     :						int          =    0.5
									__A     :						Dict          =    argparse.Namespace(**_A					)
									__A     :						Optional[Any]          =    main(_A					)
									for p in extra_model_params:
														assert getattr(model.config					, _A					) == 0.5, F"""failed to override the model config for param {p}"""

									# test models whose config doesn't include the extra_model_args
									__A     :						List[str]          =    T5_TINY
									__A     :						Union[str, Any]          =    self.get_auto_remove_tmp_dir()
									__A     :						Union[str, Any]          =    args_d.copy()
									args_da.update(
									    model_name_or_path=_A					, output_dir=_A					, )
									__A     :						Dict          =    'encoder_layerdrop'
									__A     :						Union[str, Any]          =    0.5
									__A     :						Optional[Any]          =    argparse.Namespace(**_A					)
									with pytest.raises(_A					) as excinfo:
														__A     :						int          =    main(_A					)
									assert str(excinfo.value					) == F"""model config doesn't have a `{unsupported_param}` attribute"""





				def  UpperCAmelCase_		(      self					):
									__A     :						dict          =    CHEAP_ARGS.copy()

									__A     :						Optional[Any]          =    'summarization'
									__A     :						str          =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)

									__A     :						int          =    BART_TINY
									__A     :						Optional[Any]          =    self.get_auto_remove_tmp_dir()

									args_d.update(
									    data_dir=_A					, model_name_or_path=_A					, output_dir=_A					, tokenizer_name=_A					, train_batch_size=2					, eval_batch_size=2					, do_predict=_A					, task=_A					, src_lang='en_XX'					, tgt_lang='ro_RO'					, freeze_encoder=_A					, freeze_embeds=_A					, )

									# emulate finetune.py
									__A     :						Tuple          =    argparse.ArgumentParser()
									__A     :						int          =    pl.Trainer.add_argparse_args(_A					)
									__A     :						str          =    SummarizationModule.add_model_specific_args(_A					, os.getcwd()					)
									__A     :						Dict          =    {'--help': True}

									# --help test
									with pytest.raises(_A					) as excinfo:
														with CaptureStdout() as cs:
																			__A     :						Optional[int]          =    parser.parse_args(_A					)
														assert False, "--help is expected to sys.exit"
									assert excinfo.type == SystemExit
									__A     :						int          =    lightning_base.arg_to_scheduler_metavar
									assert expected in cs.out, "--help is expected to list the supported schedulers"

									# --lr_scheduler=non_existing_scheduler test
									__A     :						List[Any]          =    'non_existing_scheduler'
									__A     :						List[Any]          =    {F"""--lr_scheduler={unsupported_param}"""}
									with pytest.raises(_A					) as excinfo:
														with CaptureStderr() as cs:
																			__A     :						Tuple          =    parser.parse_args(_A					)
														assert False, "invalid argument is expected to sys.exit"
									assert excinfo.type == SystemExit
									__A     :						Union[str, Any]          =    F"""invalid choice: '{unsupported_param}'"""
									assert expected in cs.err, F"""should have bailed on invalid choice of scheduler {unsupported_param}"""

									# --lr_scheduler=existing_scheduler test
									__A     :						List[str]          =    'cosine'
									__A     :						str          =    args_d.copy()
									__A     :						Optional[Any]          =    supported_param
									__A     :						Tuple          =    argparse.Namespace(**_A					)
									__A     :						List[Any]          =    main(_A					)
									assert (
									    getattr(model.hparams					, 'lr_scheduler'					) == supported_param
									), F"""lr_scheduler={supported_param} shouldn't fail"""











import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

UpperCAmelCase						:       List[Any]        =					32







def 			_SCREAMING_SNAKE_CASE    (       a ,						a = 16  )				->				List[Any]:
					__A     :						List[str]          =    AutoTokenizer.from_pretrained('bert-base-cased'  )
					__A     :						int          =    load_dataset('glue' ,						'mrpc'  )

					def tokenize_function(a  ):
										# max_length=None => use the model max length (it's actually the default)
										__A     :						str          =    tokenizer(examples['sentence1'] ,						examples['sentence2'] ,						truncation=a ,						max_length=a  )
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					# starting with the main process first:
					with accelerator.main_process_first():
										__A     :						Optional[int]          =    datasets.map(
										    a ,						batched=a ,						remove_columns=['idx', 'sentence1', 'sentence2'] ,						)

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					__A     :						Tuple          =    tokenized_datasets.rename_column('label' ,						'labels'  )

					def collate_fn(a  ):
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															__A     :						Union[str, Any]          =    16
										elif accelerator.mixed_precision != "no":
															__A     :						Any          =    8
										else:
															__A     :						Union[str, Any]          =    None

										return tokenizer.pad(
										    a ,						padding='longest' ,						pad_to_multiple_of=a ,						return_tensors='pt' ,						)

					# Instantiate dataloaders.
					__A     :						Any          =    DataLoader(
					    tokenized_datasets['train'] ,						shuffle=a ,						collate_fn=a ,						batch_size=a  )
					__A     :						List[Any]          =    DataLoader(
					    tokenized_datasets['validation'] ,						shuffle=a ,						collate_fn=a ,						batch_size=a  )

					return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
				from accelerate.test_utils.training import mocked_dataloaders

				UpperCAmelCase						:       Tuple        =					mocked_dataloaders  # noqa: F811







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				Optional[Any]:
					# For testing only
					if os.environ.get('TESTING_MOCKED_DATALOADERS' ,						a  ) == "1":
										__A     :						Dict          =    2
					# Initialize accelerator
					__A     :						int          =    Accelerator(cpu=args.cpu ,						mixed_precision=args.mixed_precision  )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__A     :						Dict          =    config['lr']
					__A     :						Optional[Any]          =    int(config['num_epochs']  )
					__A     :						int          =    int(config['seed']  )
					__A     :						int          =    int(config['batch_size']  )

					__A     :						Tuple          =    evaluate.load('glue' ,						'mrpc'  )

					# New Code #
					# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
					# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
					# half each time. From this, we can calculate the number of gradient accumulation steps needed
					# and modify the Accelerator object as a result
					@find_executable_batch_size(starting_batch_size=int(a  )  )
					def inner_training_loop(a  ):
										# Since we need to modify the outside accelerator object, we need to bring it
										# to the local scope
										nonlocal accelerator

										# We can calculate the number of gradient accumulation steps based on the current
										# batch size vs the starting batch size
										__A     :						Optional[Any]          =    observed_batch_size // batch_size

										# And then set it in the Accelerator directly:
										__A     :						List[str]          =    num_gradient_accumulation_steps

										# Next we need to free all of the stored model references in the Accelerator each time
										accelerator.free_memory()

										# And set the seed so our results are reproducable each reset
										set_seed(a  )

										# Instantiate the model (we build the model here so that the seed also control new weights initialization)
										__A     :						List[Any]          =    AutoModelForSequenceClassification.from_pretrained('bert-base-cased' ,						return_dict=a  )

										# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
										# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
										# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
										__A     :						Dict          =    model.to(accelerator.device  )

										# Instantiate optimizer
										__A     :						Optional[Any]          =    AdamW(params=model.parameters() ,						lr=a  )
										__A   ,    __A     :						Dict          =    get_dataloaders(a ,						a  )

										# Instantiate scheduler
										__A     :						List[Any]          =    get_linear_schedule_with_warmup(
										    optimizer=a ,						num_warmup_steps=1_00 ,						num_training_steps=(len(a  ) * num_epochs) ,						)

										# Prepare everything
										# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
										# prepare method.
										__A   ,    __A   ,    __A   ,    __A   ,    __A     :						Tuple          =    accelerator.prepare(
										    a ,						a ,						a ,						a ,						a  )

										# Now we train the model
										for epoch in range(a  ):
															model.train()
															for step, batch in enumerate(a  ):
																				# And perform gradient accumulation
																				with accelerator.accumulate(a  ):
																									# We could avoid this line since we set the accelerator with `device_placement=True`.
																									batch.to(accelerator.device  )
																									__A     :						List[str]          =    model(**a  )
																									__A     :						List[str]          =    outputs.loss
																									accelerator.backward(a  )
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()

															model.eval()
															for step, batch in enumerate(a  ):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device  )
																				with torch.no_grad():
																									__A     :						List[Any]          =    model(**a  )
																				__A     :						List[Any]          =    outputs.logits.argmax(dim=-1  )
																				__A   ,    __A     :						Tuple          =    accelerator.gather_for_metrics((predictions, batch['labels'])  )
																				metric.add_batch(
																				    predictions=a ,						references=a ,						)

															__A     :						Optional[int]          =    metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(F"""epoch {epoch}:""" ,						a  )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
					inner_training_loop()







def 			_SCREAMING_SNAKE_CASE    (       )				->				Union[str, Any]:
					__A     :						List[Any]          =    argparse.ArgumentParser(description='Simple example of training script.'  )
					parser.add_argument(
					    '--mixed_precision' ,						type=a ,						default=a ,						choices=['no', 'fp16', 'bf16', 'fp8'] ,						help='Whether to use mixed precision. Choose'
					    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
					    'and an Nvidia Ampere GPU.' ,						)
					parser.add_argument('--cpu' ,						action='store_true' ,						help='If passed, will train on the CPU.'  )
					__A     :						int          =    parser.parse_args()
					# New Code #
					# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
					__A     :						Optional[int]          =    {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 2_56}
					training_function(a ,						a  )


if __name__ == "__main__":
				main()




