



'''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__    :     List[str]			  =   logging.getLogger()
UpperCamelCase__    :     Tuple			  =   torch.cuda.is_available()
UpperCamelCase__    :     List[str]			  =   {
    '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     UpperCAmelCase     (    a_      ,		a_		)   ->       Optional[Any]:



   """simple docstring"""






   A_  :		Dict						=					"""\n""".join(a_		)
   Path(a_		).open("""w"""		).writelines(a_		)


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



   """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	(  __A							):


 """simple docstring"""

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







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







 def       UpperCAmelCase_       (      self  )    ->    int:
    A_  :		Optional[Any]						=					{"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
    self._test_distiller_cli(_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    A_  :		str						=					{
        """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""",
    }
    A_  :		Tuple						=					self._test_distiller_cli(_lowerCamelCase					,    check_contents=_lowerCamelCase  )

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

    A_  :		List[Any]						=					self.get_auto_remove_tmp_dir()
    convert_pl_to_hf(ckpts[0]					,    transformer_ckpts[0].parent					,    _lowerCamelCase  )
    assert os.path.exists(os.path.join(_lowerCamelCase					,    """pytorch_model.bin"""  )  )







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

    A_  :		List[Any]						=					model(_lowerCamelCase					,    attention_mask=_lowerCamelCase					,    decoder_input_ids=_lowerCamelCase					,    use_cache=_lowerCamelCase  ).logits

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







 def       UpperCAmelCase_       (      self  )    ->    int:
    A_  :		Union[str, 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""",
    }
    A_  :		Tuple						=					self._test_distiller_cli(_lowerCamelCase					,    check_contents=_lowerCamelCase  )
    assert model.model.config.model_type == "mbart"

    A_  :		List[Any]						=					list(Path(model.output_dir  ).glob("""*.ckpt"""  )  )
    self.assertEqual(1					,    len(_lowerCamelCase  )  )
    A_  :		Tuple						=					list(Path(model.output_dir  ).glob("""**/*.bin"""  )  )
    A_  :		Optional[int]						=					list(Path(model.output_dir  ).glob("""best_tfmr/*"""  )  )
    assert len(_lowerCamelCase  ) > 2
    self.assertEqual(len(_lowerCamelCase  )					,    2  )







 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    A_  :		Dict						=					{
        """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(_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    A_  :		int						=					{
        """teacher""": T5_TINY,
        """student""": T5_TINIER,
        """model_name_or_path""": T5_TINIER,
        """tokenizer_name""": T5_TINIER,
    }
    self._test_distiller_cli(_lowerCamelCase  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase=True  )    ->    List[Any]:
    A_  :		Optional[int]						=					{
        """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(_lowerCamelCase  )
    A_  :		dict						=					CHEAP_ARGS.copy()
    A_  :		Tuple						=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
    A_  :		Dict						=					self.get_auto_remove_tmp_dir()

    args_d.update(data_dir=_lowerCamelCase					,    output_dir=_lowerCamelCase					,    **_lowerCamelCase  )
    A_  :		List[str]						=					distill_main(argparse.Namespace(**_lowerCamelCase  )  )
    if not check_contents:
       return model
    A_  :		Optional[Any]						=					os.listdir(_lowerCamelCase  )
    A_  :		Optional[int]						=					{os.path.basename(_lowerCamelCase  ) for p in contents}
    A_  :		Optional[Any]						=					[p for p in contents if p.endswith("""ckpt"""  )]
    assert len(_lowerCamelCase  ) > 0

    self.assertIn("""test_generations.txt"""					,    _lowerCamelCase  )
    self.assertIn("""test_results.txt"""					,    _lowerCamelCase  )

    A_  :		Tuple						=					load_json(model.metrics_save_path  )
    A_  :		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}"]					,    _lowerCamelCase  )
    A_  :		Union[str, Any]						=					int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1  )
    self.assertEqual(len(metrics["""val"""]  )					,    _lowerCamelCase  )
    self.assertEqual(len(metrics["""test"""]  )					,    1  )
    return model


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 @parameterized.expand(
     [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]					,    )
 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Union[str, Any]:
    A_  :		dict						=					CHEAP_ARGS.copy()
    A_  :		Union[str, Any]						=					"""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_  :		int						=					self.get_auto_remove_tmp_dir()
    args_d.update(
        data_dir=_lowerCamelCase					,    model_name_or_path=_lowerCamelCase					,    tokenizer_name=_lowerCamelCase					,    train_batch_size=2					,    eval_batch_size=2					,    output_dir=_lowerCamelCase					,    do_predict=_lowerCamelCase					,    task=_lowerCamelCase					,    src_lang="""en_XX"""					,    tgt_lang="""ro_RO"""					,    freeze_encoder=_lowerCamelCase					,    freeze_embeds=_lowerCamelCase					,    )
    assert "n_train" in args_d
    A_  :		Any						=					argparse.Namespace(**_lowerCamelCase  )
    A_  :		str						=					main(_lowerCamelCase  )

    A_  :		List[str]						=					module.model.get_input_embeddings()
    assert not input_embeds.weight.requires_grad
    if model == T5_TINY:
       A_  :		List[str]						=					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_  :		Tuple						=					module.model.model
       A_  :		int						=					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_  :		Union[str, 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_  :		List[Any]						=					load_json(module.output_dir / """text_batch.json"""  )
    assert isinstance(_lowerCamelCase					,    _lowerCamelCase  )
    assert len(_lowerCamelCase  ) >= 4







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    A_  :		dict						=					CHEAP_ARGS.copy()

    A_  :		Optional[int]						=					"""summarization"""
    A_  :		Optional[int]						=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )

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

    # test models whose config includes the extra_model_args
    A_  :		str						=					BART_TINY
    A_  :		int						=					self.get_auto_remove_tmp_dir()
    A_  :		str						=					args_d.copy()
    args_da.update(
        model_name_or_path=_lowerCamelCase					,    output_dir=_lowerCamelCase					,    )
    A_  :		List[str]						=					("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
    for p in extra_model_params:
       A_  :		Optional[int]						=					0.5
    A_  :		Optional[int]						=					argparse.Namespace(**_lowerCamelCase  )
    A_  :		Tuple						=					main(_lowerCamelCase  )
    for p in extra_model_params:
       assert getattr(model.config					,    _lowerCamelCase  ) == 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_  :		Tuple						=					self.get_auto_remove_tmp_dir()
    A_  :		Tuple						=					args_d.copy()
    args_da.update(
        model_name_or_path=_lowerCamelCase					,    output_dir=_lowerCamelCase					,    )
    A_  :		int						=					"""encoder_layerdrop"""
    A_  :		Any						=					0.5
    A_  :		str						=					argparse.Namespace(**_lowerCamelCase  )
    with pytest.raises(_lowerCamelCase  ) as excinfo:
       A_  :		List[str]						=					main(_lowerCamelCase  )
    assert str(excinfo.value  ) == F"model config doesn't have a `{unsupported_param}` attribute"







 def       UpperCAmelCase_       (      self  )    ->    str:
    A_  :		dict						=					CHEAP_ARGS.copy()

    A_  :		str						=					"""summarization"""
    A_  :		Dict						=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )

    A_  :		List[Any]						=					BART_TINY
    A_  :		str						=					self.get_auto_remove_tmp_dir()

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

    # emulate finetune.py
    A_  :		Any						=					argparse.ArgumentParser()
    A_  :		Optional[Any]						=					pl.Trainer.add_argparse_args(_lowerCamelCase  )
    A_  :		Optional[int]						=					SummarizationModule.add_model_specific_args(_lowerCamelCase					,    os.getcwd()  )
    A_  :		Dict						=					{"""--help""": True}

    # --help test
    with pytest.raises(_lowerCamelCase  ) as excinfo:
       with CaptureStdout() as cs:
          A_  :		Dict						=					parser.parse_args(_lowerCamelCase  )
       assert False, "--help is expected to sys.exit"
    assert excinfo.type == SystemExit
    A_  :		List[Any]						=					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_  :		Dict						=					"""non_existing_scheduler"""
    A_  :		List[str]						=					{F"--lr_scheduler={unsupported_param}"}
    with pytest.raises(_lowerCamelCase  ) as excinfo:
       with CaptureStderr() as cs:
          A_  :		str						=					parser.parse_args(_lowerCamelCase  )
       assert False, "invalid argument is expected to sys.exit"
    assert excinfo.type == SystemExit
    A_  :		str						=					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_  :		str						=					"""cosine"""
    A_  :		int						=					args_d.copy()
    A_  :		Optional[int]						=					supported_param
    A_  :		List[str]						=					argparse.Namespace(**_lowerCamelCase  )
    A_  :		Tuple						=					main(_lowerCamelCase  )
    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__    :     Union[str, Any]			  =   16
UpperCamelCase__    :     Optional[int]			  =   32



def     UpperCAmelCase     (    a_      ,		a_		)   ->       List[Any]:



   """simple docstring"""






   if args.with_tracking:
      A_  :		Union[str, Any]						=					Accelerator(
          cpu=args.cpu      ,		mixed_precision=args.mixed_precision      ,		log_with="""all"""      ,		project_dir=args.project_dir		)
   else:
      A_  :		Optional[Any]						=					Accelerator(cpu=args.cpu      ,		mixed_precision=args.mixed_precision		)

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

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

   A_  :		Union[str, Any]						=					AutoTokenizer.from_pretrained("""bert-base-cased"""		)
   A_  :		Optional[int]						=					load_dataset("""glue"""      ,		"""mrpc"""		)
   A_  :		List[str]						=					evaluate.load("""glue"""      ,		"""mrpc"""		)

   def tokenize_function(a_		):
      # max_length=None => use the model max length (it's actually the default)
      A_  :		Tuple						=					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_  :		Dict						=					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_  :		int						=					tokenized_datasets.rename_column("""label"""      ,		"""labels"""		)

   # If the batch size is too big we use gradient accumulation
   A_  :		Optional[Any]						=					1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      A_  :		List[str]						=					batch_size // MAX_GPU_BATCH_SIZE
      A_  :		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.
      A_  :		List[Any]						=					1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         A_  :		Any						=					1_6
      elif accelerator.mixed_precision != "no":
         A_  :		Optional[int]						=					8
      else:
         A_  :		Optional[Any]						=					None

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

   # Instantiate dataloaders.
   A_  :		str						=					DataLoader(
       tokenized_datasets["""train"""]      ,		shuffle=a_      ,		collate_fn=a_      ,		batch_size=a_		)
   A_  :		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)
   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_  :		Union[str, Any]						=					model.to(accelerator.device		)

   # Instantiate optimizer
   A_  :		Any						=					AdamW(params=model.parameters()      ,		lr=a_		)

   # Instantiate scheduler
   A_  :		Dict						=					get_linear_schedule_with_warmup(
       optimizer=a_      ,		num_warmup_steps=1_0_0      ,		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.
   A_						,  A_						,  A_						,  A_						,  A_  :		List[str]						=					accelerator.prepare(
       a_      ,		a_      ,		a_      ,		a_      ,		a_		)

   # We need to keep track of how many total steps we have iterated over
   A_  :		Optional[int]						=					0
   # We also need to keep track of the stating epoch so files are named properly
   A_  :		Union[str, Any]						=					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		)
         A_  :		int						=					os.path.basename(args.resume_from_checkpoint		)
      else:
         # Get the most recent checkpoint
         A_  :		int						=					[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
         dirs.sort(key=os.path.getctime		)
         A_  :		Optional[Any]						=					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      A_  :		Optional[Any]						=					os.path.splitext(a_		)[0]

      if "epoch" in training_difference:
         A_  :		List[Any]						=					int(training_difference.replace("""epoch_"""      ,		""""""		)		) + 1
         A_  :		List[str]						=					None
      else:
         A_  :		Optional[int]						=					int(training_difference.replace("""step_"""      ,		""""""		)		)
         A_  :		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:
         A_  :		List[str]						=					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
         A_  :		List[Any]						=					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
         A_  :		Tuple						=					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		)
         A_  :		List[Any]						=					model(**a_		)
         A_  :		str						=					outputs.loss
         A_  :		Tuple						=					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_		):
            A_  :		str						=					F"step_{overall_step}"
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  A_  :		Dict						=					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():
            A_  :		str						=					model(**a_		)
         A_  :		Any						=					outputs.logits.argmax(dim=-1		)
         A_						,  A_  :		Dict						=					accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
         metric.add_batch(
             predictions=a_      ,		references=a_      ,		)

      A_  :		Union[str, Any]						=					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":
         A_  :		str						=					F"epoch_{epoch}"
         if args.output_dir is not None:
            A_  :		Dict						=					os.path.join(args.output_dir      ,		a_		)
         accelerator.save_state(a_		)

   if args.with_tracking:
      accelerator.end_training()



def     UpperCAmelCase     (    )   ->       str:



   """simple docstring"""






   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."""		)
   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"""      ,		)
   A_  :		Tuple						=					parser.parse_args()
   A_  :		Union[str, Any]						=					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
   training_function(a_      ,		a_		)


if __name__ == "__main__":
  main()
