



"""simple docstring"""






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      :      Union[str, Any]   					=					logging.getLogger()
__UpperCAmelCase      :      Optional[int]   					=					torch.cuda.is_available()
__UpperCAmelCase      :      Union[str, Any]   					=					{
    '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      A      (			_A,     _A							):




					"""simple docstring"""
					snake_case_						:Any								=						"\n".join(_A							)
					Path(_A							).open("w"							).writelines(_A							)


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


__UpperCAmelCase      :      Any   					=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
def      A      (			_A							):




					"""simple docstring"""
					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 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					@classmethod
					def 		_a (      cls   ):




										"""simple docstring"""





										logging.disable(logging.CRITICAL   )  # remove noisy download output from tracebacks
										return cls







					@slow
					@require_torch_gpu
					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Optional[Any]								=						list_models()
										snake_case_						:List[str]								=						"sshleifer"
										snake_case_						:Optional[Any]								=						[x.modelId for x in model_list if x.modelId.startswith(a   )]
										snake_case_						:Tuple								=						["sshleifer/blenderbot-3B", "sshleifer/blenderbot-90M"]
										snake_case_						:List[Any]								=						[]
										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 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:int								=						{"student_encoder_layers": 2, "student_decoder_layers": 1, "no_teacher": True}
										self._test_distiller_cli(a   )







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:int								=						{
										    "student_encoder_layers": 2,
										    "student_decoder_layers": 1,
										    "max_epochs": 4,
										    "val_check_interval": 0.25,
										    "alpha_hid": 2.0,
										    "model_name_or_path": "IGNORE_THIS_IT_DOESNT_GET_USED",
										}
										snake_case_						:Optional[int]								=						self._test_distiller_cli(a    ,       check_contents=a   )

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

										snake_case_						: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 		_a (      self   ):




										"""simple docstring"""





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

										snake_case_						:List[Any]								=						model(a    ,       attention_mask=a    ,       decoder_input_ids=a    ,       use_cache=a   ).logits

										snake_case_						:List[str]								=						nn.functional.log_softmax(a    ,       dim=-1   )
										snake_case_	,  snake_case_						: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 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Optional[Any]								=						{
										    "student_encoder_layers": 2,
										    "student_decoder_layers": 1,
										    "num_train_epochs": 4,
										    "val_check_interval": 0.25,
										    "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",
										}
										snake_case_						:Tuple								=						self._test_distiller_cli(a    ,       check_contents=a   )
										assert model.model.config.model_type == "mbart"

										snake_case_						:Any								=						list(Path(model.output_dir   ).glob("*.ckpt"   )   )
										self.assertEqual(1    ,       len(a   )   )
										snake_case_						:Union[str, Any]								=						list(Path(model.output_dir   ).glob("**/*.bin"   )   )
										snake_case_						:Union[str, Any]								=						list(Path(model.output_dir   ).glob("best_tfmr/*"   )   )
										assert len(a   ) > 2
										self.assertEqual(len(a   )    ,       2   )







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Tuple								=						{
										    "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 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Optional[Any]								=						{
										    "teacher": T5_TINY,
										    "student": T5_TINIER,
										    "model_name_or_path": T5_TINIER,
										    "tokenizer_name": T5_TINIER,
										}
										self._test_distiller_cli(a   )







					def 		_a (      self    ,       a    ,       a=True   ):




										"""simple docstring"""





										snake_case_						:Union[str, Any]								=						{
										    "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   )
										snake_case_						:dict								=						CHEAP_ARGS.copy()
										snake_case_						:str								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
										snake_case_						:Optional[Any]								=						self.get_auto_remove_tmp_dir()

										args_d.update(data_dir=a    ,       output_dir=a    ,       **a   )
										snake_case_						:Optional[Any]								=						distill_main(argparse.Namespace(**a   )   )
										if not check_contents:
															return model
										snake_case_						:Union[str, Any]								=						os.listdir(a   )
										snake_case_						:List[str]								=						{os.path.basename(a   ) for p in contents}
										snake_case_						:Dict								=						[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   )

										snake_case_						:Dict								=						load_json(model.metrics_save_path   )
										snake_case_						:Optional[int]								=						metrics["val"][-1]
										self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]    ,       0.01   )
										self.assertGreaterEqual(1.0    ,       last_step_stats["val_avg_gen_time"]   )
										self.assertIsInstance(last_step_stats[F'''val_avg_{model.val_metric}''']    ,       a   )
										snake_case_						:List[Any]								=						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 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					@parameterized.expand(
					    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]    ,       )
					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										snake_case_						:dict								=						CHEAP_ARGS.copy()
										snake_case_						:Optional[int]								=						"translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization"
										snake_case_						:Tuple								=						0.1 if task == "translation" else 0

										snake_case_						:str								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
										snake_case_						:Tuple								=						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
										snake_case_						:List[str]								=						argparse.Namespace(**a   )
										snake_case_						:Optional[int]								=						main(a   )

										snake_case_						:Union[str, Any]								=						module.model.get_input_embeddings()
										assert not input_embeds.weight.requires_grad
										if model == T5_TINY:
															snake_case_						:Union[str, 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:
															snake_case_						:List[str]								=						module.model.model
															snake_case_						:Union[str, Any]								=						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:
															snake_case_						:Any								=						module.model.model
															snake_case_						:Any								=						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

										snake_case_						:int								=						load_json(module.output_dir / "text_batch.json"   )
										assert isinstance(a    ,       a   )
										assert len(a   ) >= 4







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:dict								=						CHEAP_ARGS.copy()

										snake_case_						:Union[str, Any]								=						"summarization"
										snake_case_						:Tuple								=						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
										snake_case_						:Any								=						BART_TINY
										snake_case_						:str								=						self.get_auto_remove_tmp_dir()
										snake_case_						:List[Any]								=						args_d.copy()
										args_da.update(
										    model_name_or_path=a    ,       output_dir=a    ,       )
										snake_case_						:List[str]								=						("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
										for p in extra_model_params:
															snake_case_						:Any								=						0.5
										snake_case_						:Any								=						argparse.Namespace(**a   )
										snake_case_						:List[str]								=						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
										snake_case_						:int								=						T5_TINY
										snake_case_						:Dict								=						self.get_auto_remove_tmp_dir()
										snake_case_						:Dict								=						args_d.copy()
										args_da.update(
										    model_name_or_path=a    ,       output_dir=a    ,       )
										snake_case_						:Optional[Any]								=						"encoder_layerdrop"
										snake_case_						:Tuple								=						0.5
										snake_case_						:List[Any]								=						argparse.Namespace(**a   )
										with pytest.raises(a   ) as excinfo:
															snake_case_						:Any								=						main(a   )
										assert str(excinfo.value   ) == F'''model config doesn\'t have a `{unsupported_param}` attribute'''







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:dict								=						CHEAP_ARGS.copy()

										snake_case_						:Tuple								=						"summarization"
										snake_case_						:Union[str, Any]								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )

										snake_case_						:Optional[int]								=						BART_TINY
										snake_case_						:int								=						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
										snake_case_						:str								=						argparse.ArgumentParser()
										snake_case_						:Union[str, Any]								=						pl.Trainer.add_argparse_args(a   )
										snake_case_						:str								=						SummarizationModule.add_model_specific_args(a    ,       os.getcwd()   )
										snake_case_						:List[Any]								=						{"--help": True}

										# --help test
										with pytest.raises(a   ) as excinfo:
															with CaptureStdout() as cs:
																				snake_case_						:Any								=						parser.parse_args(a   )
															assert False, "--help is expected to sys.exit"
										assert excinfo.type == SystemExit
										snake_case_						: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
										snake_case_						:Any								=						"non_existing_scheduler"
										snake_case_						:int								=						{F'''--lr_scheduler={unsupported_param}'''}
										with pytest.raises(a   ) as excinfo:
															with CaptureStderr() as cs:
																				snake_case_						:Dict								=						parser.parse_args(a   )
															assert False, "invalid argument is expected to sys.exit"
										assert excinfo.type == SystemExit
										snake_case_						: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
										snake_case_						:Dict								=						"cosine"
										snake_case_						:Optional[int]								=						args_d.copy()
										snake_case_						:Union[str, Any]								=						supported_param
										snake_case_						:Optional[Any]								=						argparse.Namespace(**a   )
										snake_case_						:str								=						main(a   )
										assert (
										    getattr(model.hparams    ,       "lr_scheduler"   ) == supported_param
										), F'''lr_scheduler={supported_param} shouldn\'t fail'''






"""simple docstring"""






import argparse
import os

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, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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      :      Dict   					=					16
__UpperCAmelCase      :      List[Any]   					=					32
def      A      (			_A,     _A							):




					"""simple docstring"""
					# Initialize accelerator
					if args.with_tracking:
										snake_case_						:Union[str, Any]								=						Accelerator(
										    cpu=args.cpu,     mixed_precision=args.mixed_precision,     log_with="all",     project_dir=args.project_dir							)
					else:
										snake_case_						:Tuple								=						Accelerator(cpu=args.cpu,     mixed_precision=args.mixed_precision							)

					if hasattr(args.checkpointing_steps,     "isdigit"							):
										if args.checkpointing_steps == "epoch":
															snake_case_						:Tuple								=						args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															snake_case_						:int								=						int(args.checkpointing_steps							)
										else:
															raise ValueError(
															    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''							)
					else:
										snake_case_						:Tuple								=						None
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					snake_case_						:Union[str, Any]								=						config["lr"]
					snake_case_						:Any								=						int(config["num_epochs"]							)
					snake_case_						:Optional[int]								=						int(config["seed"]							)
					snake_case_						:List[Any]								=						int(config["batch_size"]							)

					# We need to initialize the trackers we use, and also store our configuration
					if args.with_tracking:
										snake_case_						:Union[str, Any]								=						os.path.split(_A							)[-1].split("."							)[0]
										accelerator.init_trackers(_A,     _A							)

					snake_case_						:Any								=						AutoTokenizer.from_pretrained("bert-base-cased"							)
					snake_case_						:Any								=						load_dataset("glue",     "mrpc"							)
					snake_case_						:Optional[Any]								=						evaluate.load("glue",     "mrpc"							)

					def tokenize_function(_A							):
										# max_length=None => use the model max length (it's actually the default)
										snake_case_						:Any								=						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():
										snake_case_						: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
					snake_case_						:Any								=						tokenized_datasets.rename_column("label",     "labels"							)

					# If the batch size is too big we use gradient accumulation
					snake_case_						:List[Any]								=						1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										snake_case_						:Optional[int]								=						batch_size // MAX_GPU_BATCH_SIZE
										snake_case_						:List[str]								=						MAX_GPU_BATCH_SIZE

					def collate_fn(_A							):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										snake_case_						:Optional[int]								=						128 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															snake_case_						:Union[str, Any]								=						16
										elif accelerator.mixed_precision != "no":
															snake_case_						:List[Any]								=						8
										else:
															snake_case_						:Any								=						None

										return tokenizer.pad(
										    _A,     padding="longest",     max_length=_A,     pad_to_multiple_of=_A,     return_tensors="pt",     )

					# Instantiate dataloaders.
					snake_case_						:str								=						DataLoader(
					    tokenized_datasets["train"],     shuffle=_A,     collate_fn=_A,     batch_size=_A							)
					snake_case_						:Any								=						DataLoader(
					    tokenized_datasets["validation"],     shuffle=_A,     collate_fn=_A,     batch_size=_A							)

					set_seed(_A							)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					snake_case_						:Optional[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).
					snake_case_						:str								=						model.to(accelerator.device							)

					# Instantiate optimizer
					snake_case_						:List[Any]								=						AdamW(params=model.parameters(),     lr=_A							)

					# Instantiate scheduler
					snake_case_						:Tuple								=						get_linear_schedule_with_warmup(
					    optimizer=_A,     num_warmup_steps=100,     num_training_steps=(len(_A							) * num_epochs) // gradient_accumulation_steps,     )

					# 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.
					snake_case_	,  snake_case_	,  snake_case_	,  snake_case_	,  snake_case_						:Optional[Any]								=						accelerator.prepare(
					    _A,     _A,     _A,     _A,     _A							)

					# We need to keep track of how many total steps we have iterated over
					snake_case_						:Optional[int]								=						0
					# We also need to keep track of the stating epoch so files are named properly
					snake_case_						:int								=						0

					# Potentially 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							)
															snake_case_						:str								=						os.path.basename(args.resume_from_checkpoint							)
										else:
															# Get the most recent checkpoint
															snake_case_						:int								=						[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
															dirs.sort(key=os.path.getctime							)
															snake_case_						:Tuple								=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										snake_case_						:Optional[int]								=						os.path.splitext(_A							)[0]

										if "epoch" in training_difference:
															snake_case_						:Union[str, Any]								=						int(training_difference.replace("epoch_",     ""							)							) + 1
															snake_case_						:List[str]								=						None
										else:
															snake_case_						:Tuple								=						int(training_difference.replace("step_",     ""							)							)
															snake_case_						:Optional[int]								=						resume_step // len(_A							)
															resume_step -= starting_epoch * len(_A							)

    # Now we train the model
					for epoch in range(_A,     _A							):
										model.train()
										if args.with_tracking:
															snake_case_						:Optional[Any]								=						0
										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
															# We need to skip steps until we reach the resumed step
															snake_case_						:Dict								=						accelerator.skip_first_batches(_A,     _A							)
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															snake_case_						:Optional[Any]								=						train_dataloader
										for step, batch in enumerate(_A							):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device							)
															snake_case_						:Union[str, Any]								=						model(**_A							)
															snake_case_						:str								=						outputs.loss
															snake_case_						:int								=						loss / gradient_accumulation_steps
															# We keep track of the loss at each epoch
															if args.with_tracking:
																				total_loss += loss.detach().float()
															accelerator.backward(_A							)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1

															if isinstance(_A,     _A							):
																				snake_case_						:List[str]								=						F'''step_{overall_step}'''
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														snake_case_						:Tuple								=						os.path.join(args.output_dir,     _A							)
																									accelerator.save_state(_A							)

										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():
																				snake_case_						:str								=						model(**_A							)
															snake_case_						:str								=						outputs.logits.argmax(dim=-1							)
															snake_case_	,  snake_case_						:List[str]								=						accelerator.gather_for_metrics((predictions, batch["labels"])							)
															metric.add_batch(
															    predictions=_A,     references=_A,     )

										snake_case_						:int								=						metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F'''epoch {epoch}:''',     _A							)
										if args.with_tracking:
															accelerator.log(
															    {
															        "accuracy": eval_metric["accuracy"],
															        "f1": eval_metric["f1"],
															        "train_loss": total_loss.item() / len(_A							),
															        "epoch": epoch,
															    },     step=_A,     )

										if checkpointing_steps == "epoch":
															snake_case_						:str								=						F'''epoch_{epoch}'''
															if args.output_dir is not None:
																				snake_case_						:Any								=						os.path.join(args.output_dir,     _A							)
															accelerator.save_state(_A							)

					if args.with_tracking:
										accelerator.end_training()
def      A      (			):




					"""simple docstring"""
					snake_case_						:str								=						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."							)
					parser.add_argument(
					    "--checkpointing_steps",     type=_A,     default=_A,     help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",     )
					parser.add_argument(
					    "--resume_from_checkpoint",     type=_A,     default=_A,     help="If the training should continue from a checkpoint folder.",     )
					parser.add_argument(
					    "--with_tracking",     action="store_true",     help="Whether to load in all available experiment trackers from the environment and use them for logging.",     )
					parser.add_argument(
					    "--output_dir",     type=_A,     default=".",     help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",     )
					parser.add_argument(
					    "--project_dir",     type=_A,     default="logs",     help="Location on where to store experiment tracking logs` and relevent project information",     )
					snake_case_						:Optional[int]								=						parser.parse_args()
					snake_case_						:Optional[Any]								=						{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(_A,     _A							)


if __name__ == "__main__":
				main()

