




import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


lowerCamelCase							:						Any					       =  logging.getLogger(__name__)







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		'''summarization'''
 UpperCamelCase		  =		['''loss''']
 UpperCamelCase		  =		ROUGE_KEYS
 UpperCamelCase		  =		'''rouge2'''

 def __init__(							self   :    List[Any]		,				A_   :    int		,				**A_   :    Dict    )  ->      Tuple:

       """simple docstring"""






       if hparams.sortish_sampler and hparams.gpus > 1:
             lowerCamelCase_							=       False
       elif hparams.max_tokens_per_batch is not None:
             if hparams.gpus > 1:
                   raise NotImplementedError('Dynamic Batch size does not work for multi-gpu training'    )
             if hparams.sortish_sampler:
                   raise ValueError('--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'    )

       super().__init__(A_		,				num_labels=A_		,				mode=self.mode		,				**A_    )
       use_task_specific_params(self.model		,				'summarization'    )
       save_git_info(self.hparams.output_dir    )
       lowerCamelCase_							=       Path(self.output_dir    ) / 'metrics.json'
       lowerCamelCase_							=       Path(self.output_dir    ) / 'hparams.pkl'
       pickle_save(self.hparams		,				self.hparams_save_path    )
       lowerCamelCase_							=       0
       lowerCamelCase_							=       defaultdict(A_    )
       lowerCamelCase_							=       self.config.model_type
       lowerCamelCase_							=       self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

       lowerCamelCase_							=       {
           "data_dir": self.hparams.data_dir,
           "max_source_length": self.hparams.max_source_length,
           "prefix": self.model.config.prefix or "",
       }
       lowerCamelCase_							=       {
           'train': self.hparams.n_train,
           'val': self.hparams.n_val,
           'test': self.hparams.n_test,
       }
       lowerCamelCase_							=       {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

       lowerCamelCase_							=       {
           'train': self.hparams.max_target_length,
           'val': self.hparams.val_max_target_length,
           'test': self.hparams.test_max_target_length,
       }
       assert self.target_lens["train"] <= self.target_lens["val"], f"""target_lens: {self.target_lens}"""
       assert self.target_lens["train"] <= self.target_lens["test"], f"""target_lens: {self.target_lens}"""
       if self.hparams.freeze_embeds:
             freeze_embeds(self.model    )
       if self.hparams.freeze_encoder:
             freeze_params(self.model.get_encoder()    )
             assert_all_frozen(self.model.get_encoder()    )

       lowerCamelCase_							=       get_git_info()['repo_sha']
       lowerCamelCase_							=       hparams.num_workers
       lowerCamelCase_							=       None  # default to config
       if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		,				A_    ):
             lowerCamelCase_							=       self.tokenizer.lang_code_to_id[hparams.tgt_lang]
             lowerCamelCase_							=       self.decoder_start_token_id
       lowerCamelCase_							=       (
           SeqaSeqDataset if hasattr(self.tokenizer		,				'prepare_seq2seq_batch'    ) else LegacySeqaSeqDataset
       )
       lowerCamelCase_							=       False
       lowerCamelCase_							=       self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
       if self.hparams.eval_max_gen_length is not None:
             lowerCamelCase_							=       self.hparams.eval_max_gen_length
       else:
             lowerCamelCase_							=       self.model.config.max_length
       lowerCamelCase_							=       self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric

 def 							a__					(							self   :    Dict		,				A_   :    Dict[str, torch.Tensor]    )  ->      Dict[str, List[str]]:

       """simple docstring"""






       lowerCamelCase_							=       {
           k: self.tokenizer.batch_decode(v.tolist()    ) if 'mask' not in k else v.shape for k, v in batch.items()
       }
       save_json(A_		,				Path(self.output_dir    ) / 'text_batch.json'    )
       save_json({k: v.tolist() for k, v in batch.items()}		,				Path(self.output_dir    ) / 'tok_batch.json'    )

       lowerCamelCase_							=       True
       return readable_batch

 def 							a__					(							self   :    Optional[int]		,				A_   :    str		,				**A_   :    str    )  ->      Optional[int]:

       """simple docstring"""






       return self.model(A_		,				**A_    )

 def 							a__					(							self   :    Optional[Any]		,				A_   :    List[int]    )  ->      Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=       self.tokenizer.batch_decode(
           A_		,				skip_special_tokens=A_		,				clean_up_tokenization_spaces=A_    )
       return lmap(str.strip		,				A_    )

 def 							a__					(							self   :    str		,				A_   :    dict    )  ->      Tuple:

       """simple docstring"""






       lowerCamelCase_							=       self.tokenizer.pad_token_id
       lowerCamelCase_	,							lowerCamelCase_							=       batch['input_ids'], batch['attention_mask']
       lowerCamelCase_							=       batch['labels']
       if isinstance(self.model		,				A_    ):
             lowerCamelCase_							=       self.model._shift_right(A_    )
       else:
             lowerCamelCase_							=       shift_tokens_right(A_		,				A_    )
       if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
             lowerCamelCase_							=       decoder_input_ids
             self.save_readable_batch(A_    )

       lowerCamelCase_							=       self(A_		,				attention_mask=A_		,				decoder_input_ids=A_		,				use_cache=A_    )
       lowerCamelCase_							=       outputs['logits']
       if self.hparams.label_smoothing == 0:
             # Same behavior as modeling_bart.py, besides ignoring pad_token_id
             lowerCamelCase_							=       nn.CrossEntropyLoss(ignore_index=A_    )

             assert lm_logits.shape[-1] == self.vocab_size
             lowerCamelCase_							=       ce_loss_fct(lm_logits.view(-1		,				lm_logits.shape[-1]    )		,				tgt_ids.view(-1    )    )
       else:
             lowerCamelCase_							=       nn.functional.log_softmax(A_		,				dim=-1    )
             lowerCamelCase_	,							lowerCamelCase_							=       label_smoothed_nll_loss(
                 A_		,				A_		,				self.hparams.label_smoothing		,				ignore_index=A_    )
       return (loss,)

 @property
 def 							a__					(							self   :    Dict    )  ->      int:

       """simple docstring"""






       return self.tokenizer.pad_token_id

 def 							a__					(							self   :    Optional[int]		,				A_   :    Union[str, Any]		,				A_   :    List[str]    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       self._step(A_    )

       lowerCamelCase_							=       dict(zip(self.loss_names		,				A_    )    )
       # tokens per batch
       lowerCamelCase_							=       batch['input_ids'].ne(self.pad    ).sum() + batch['labels'].ne(self.pad    ).sum()
       lowerCamelCase_							=       batch['input_ids'].shape[0]
       lowerCamelCase_							=       batch['input_ids'].eq(self.pad    ).sum()
       lowerCamelCase_							=       batch['input_ids'].eq(self.pad    ).float().mean()
       # TODO(SS): make a wandb summary metric for this
       return {"loss": loss_tensors[0], "log": logs}

 def 							a__					(							self   :    str		,				A_   :    str		,				A_   :    List[Any]    )  ->      Dict:

       """simple docstring"""






       return self._generative_step(A_    )

 def 							a__					(							self   :    str		,				A_   :    Union[str, Any]		,				A_   :    Union[str, Any]="val"    )  ->      Dict:

       """simple docstring"""






       self.step_count += 1
       lowerCamelCase_							=       {k: torch.stack([x[k] for x in outputs]    ).mean() for k in self.loss_names}
       lowerCamelCase_							=       losses['loss']
       lowerCamelCase_							=       {
           k: np.array([x[k] for x in outputs]    ).mean() for k in self.metric_names + ['gen_time', 'gen_len']
       }
       lowerCamelCase_							=       (
           generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
       )
       lowerCamelCase_							=       torch.tensor(A_    ).type_as(A_    )
       generative_metrics.update({k: v.item() for k, v in losses.items()}    )
       losses.update(A_    )
       lowerCamelCase_							=       {f"""{prefix}_avg_{k}""": x for k, x in losses.items()}
       lowerCamelCase_							=       self.step_count
       self.metrics[prefix].append(A_    )  # callback writes this to self.metrics_save_path
       lowerCamelCase_							=       flatten_list([x['preds'] for x in outputs]    )
       return {
           "log": all_metrics,
           "preds": preds,
           f"""{prefix}_loss""": loss,
           f"""{prefix}_{self.val_metric}""": metric_tensor,
       }

 def 							a__					(							self   :    Optional[int]		,				A_   :    Any		,				A_   :    Tuple    )  ->      Dict:

       """simple docstring"""






       return calculate_rouge(A_		,				A_    )

 def 							a__					(							self   :    Union[str, Any]		,				A_   :    dict    )  ->      dict:

       """simple docstring"""






       lowerCamelCase_							=       time.time()

       # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
       lowerCamelCase_							=       self.model.generate(
           batch['input_ids']		,				attention_mask=batch['attention_mask']		,				use_cache=A_		,				decoder_start_token_id=self.decoder_start_token_id		,				num_beams=self.eval_beams		,				max_length=self.eval_max_length		,				)
       lowerCamelCase_							=       (time.time() - ta) / batch['input_ids'].shape[0]
       lowerCamelCase_							=       self.ids_to_clean_text(A_    )
       lowerCamelCase_							=       self.ids_to_clean_text(batch['labels']    )
       lowerCamelCase_							=       self._step(A_    )
       lowerCamelCase_							=       dict(zip(self.loss_names		,				A_    )    )
       lowerCamelCase_							=       self.calc_generative_metrics(A_		,				A_    )
       lowerCamelCase_							=       np.mean(lmap(A_		,				A_    )    )
       base_metrics.update(gen_time=A_		,				gen_len=A_		,				preds=A_		,				target=A_		,				**A_    )
       return base_metrics

 def 							a__					(							self   :    str		,				A_   :    List[str]		,				A_   :    str    )  ->      List[Any]:

       """simple docstring"""






       return self._generative_step(A_    )

 def 							a__					(							self   :    Optional[int]		,				A_   :    Union[str, Any]    )  ->      Dict:

       """simple docstring"""






       return self.validation_epoch_end(A_		,				prefix='test'    )

 def 							a__					(							self   :    Dict		,				A_   :    Any    )  ->      SeqaSeqDataset:

       """simple docstring"""






       lowerCamelCase_							=       self.n_obs[type_path]
       lowerCamelCase_							=       self.target_lens[type_path]
       lowerCamelCase_							=       self.dataset_class(
           self.tokenizer		,				type_path=A_		,				n_obs=A_		,				max_target_length=A_		,				**self.dataset_kwargs		,				)
       return dataset

 def 							a__					(							self   :    List[str]		,				A_   :    str		,				A_   :    int		,				A_   :    bool = False    )  ->      DataLoader:

       """simple docstring"""






       lowerCamelCase_							=       self.get_dataset(A_    )

       if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
             lowerCamelCase_							=       dataset.make_sortish_sampler(A_		,				distributed=self.hparams.gpus > 1    )
             return DataLoader(
                 A_		,				batch_size=A_		,				collate_fn=dataset.collate_fn		,				shuffle=A_		,				num_workers=self.num_workers		,				sampler=A_		,				)

       elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
             lowerCamelCase_							=       dataset.make_dynamic_sampler(
                 self.hparams.max_tokens_per_batch		,				distributed=self.hparams.gpus > 1    )
             return DataLoader(
                 A_		,				batch_sampler=A_		,				collate_fn=dataset.collate_fn		,				num_workers=self.num_workers		,				)
       else:
             return DataLoader(
                 A_		,				batch_size=A_		,				collate_fn=dataset.collate_fn		,				shuffle=A_		,				num_workers=self.num_workers		,				sampler=A_		,				)

 def 							a__					(							self   :    str    )  ->      DataLoader:

       """simple docstring"""






       lowerCamelCase_							=       self.get_dataloader('train'		,				batch_size=self.hparams.train_batch_size		,				shuffle=A_    )
       return dataloader

 def 							a__					(							self   :    List[str]    )  ->      DataLoader:

       """simple docstring"""






       return self.get_dataloader('val'		,				batch_size=self.hparams.eval_batch_size    )

 def 							a__					(							self   :    List[str]    )  ->      DataLoader:

       """simple docstring"""






       return self.get_dataloader('test'		,				batch_size=self.hparams.eval_batch_size    )





 @staticmethod
 def 							a__					(							A_   :    Tuple		,				A_   :    List[Any]    )  ->      Optional[int]:

       """simple docstring"""






       BaseTransformer.add_model_specific_args(A_		,				A_    )
       add_generic_args(A_		,				A_    )
       parser.add_argument(
           '--max_source_length'		,				default=1024		,				type=A_		,				help=(
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )		,				)
       parser.add_argument(
           '--max_target_length'		,				default=56		,				type=A_		,				help=(
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )		,				)
       parser.add_argument(
           '--val_max_target_length'		,				default=142		,				type=A_		,				help=(
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )		,				)
       parser.add_argument(
           '--test_max_target_length'		,				default=142		,				type=A_		,				help=(
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )		,				)
       parser.add_argument('--freeze_encoder'		,				action='store_true'    )
       parser.add_argument('--freeze_embeds'		,				action='store_true'    )
       parser.add_argument('--sortish_sampler'		,				action='store_true'		,				default=A_    )
       parser.add_argument('--overwrite_output_dir'		,				action='store_true'		,				default=A_    )
       parser.add_argument('--max_tokens_per_batch'		,				type=A_		,				default=A_    )
       parser.add_argument('--logger_name'		,				type=A_		,				choices=['default', 'wandb', 'wandb_shared']		,				default='default'    )
       parser.add_argument('--n_train'		,				type=A_		,				default=-1		,				required=A_		,				help='# examples. -1 means use all.'    )
       parser.add_argument('--n_val'		,				type=A_		,				default=500		,				required=A_		,				help='# examples. -1 means use all.'    )
       parser.add_argument('--n_test'		,				type=A_		,				default=-1		,				required=A_		,				help='# examples. -1 means use all.'    )
       parser.add_argument(
           '--task'		,				type=A_		,				default='summarization'		,				required=A_		,				help='# examples. -1 means use all.'    )
       parser.add_argument('--label_smoothing'		,				type=A_		,				default=0.0		,				required=A_    )
       parser.add_argument('--src_lang'		,				type=A_		,				default=''		,				required=A_    )
       parser.add_argument('--tgt_lang'		,				type=A_		,				default=''		,				required=A_    )
       parser.add_argument('--eval_beams'		,				type=A_		,				default=A_		,				required=A_    )
       parser.add_argument(
           '--val_metric'		,				type=A_		,				default=A_		,				required=A_		,				choices=['bleu', 'rouge2', 'loss', None]    )
       parser.add_argument('--eval_max_gen_length'		,				type=A_		,				default=A_		,				help='never generate more than n tokens'    )
       parser.add_argument('--save_top_k'		,				type=A_		,				default=1		,				required=A_		,				help='How many checkpoints to save'    )
       parser.add_argument(
           '--early_stopping_patience'		,				type=A_		,				default=-1		,				required=A_		,				help=(
               '-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'
               ' val_check_interval will effect it.'
           )		,				)
       return parser







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		'''translation'''
 UpperCamelCase		  =		['''loss''']
 UpperCamelCase		  =		['''bleu''']
 UpperCamelCase		  =		'''bleu'''

 def __init__(							self   :    Optional[int]		,				A_   :    int		,				**A_   :    Tuple    )  ->      Optional[int]:

       """simple docstring"""






       super().__init__(A_		,				**A_    )
       lowerCamelCase_							=       hparams.src_lang
       lowerCamelCase_							=       hparams.tgt_lang





 def 							a__					(							self   :    Optional[int]		,				A_   :    Union[str, Any]		,				A_   :    Union[str, Any]    )  ->      dict:

       """simple docstring"""






       return calculate_bleu(A_		,				A_    )

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Tuple      ,				lowercase      :			Any=None	):


      '''simple docstring'''



      Path(args.output_dir	).mkdir(exist_ok=lowercase	)
      check_output_dir(lowercase      ,				expected_items=3	)

      if model is None:
            if "summarization" in args.task:
                  lowerCamelCase_							=       SummarizationModule(lowercase	)
            else:
                  lowerCamelCase_							=       TranslationModule(lowercase	)
      lowerCamelCase_							=       Path(args.data_dir	).name
      if (
          args.logger_name == "default"
          or args.fast_dev_run
          or str(args.output_dir	).startswith('/tmp'	)
          or str(args.output_dir	).startswith('/var'	)
      ):
            lowerCamelCase_							=       True  # don't pollute wandb logs unnecessarily
      elif args.logger_name == "wandb":
            from pytorch_lightning.loggers import WandbLogger

            lowerCamelCase_							=       os.environ.get('WANDB_PROJECT'      ,				lowercase	)
            lowerCamelCase_							=       WandbLogger(name=model.output_dir.name      ,				project=lowercase	)

      elif args.logger_name == "wandb_shared":
            from pytorch_lightning.loggers import WandbLogger

            lowerCamelCase_							=       WandbLogger(name=model.output_dir.name      ,				project=f"""hf_{dataset}"""	)

      if args.early_stopping_patience >= 0:
            lowerCamelCase_							=       get_early_stopping_callback(model.val_metric      ,				args.early_stopping_patience	)
      else:
            lowerCamelCase_							=       False

      lowerCamelCase_							=       args.val_metric == 'loss'
      lowerCamelCase_							=       generic_train(
          lowercase      ,				lowercase      ,				logging_callback=SeqaSeqLoggingCallback()      ,				checkpoint_callback=get_checkpoint_callback(
              args.output_dir      ,				model.val_metric      ,				args.save_top_k      ,				lowercase	)      ,				early_stopping_callback=lowercase      ,				logger=lowercase      ,				)
      pickle_save(model.hparams      ,				model.output_dir / 'hparams.pkl'	)
      if not args.do_predict:
            return model

      lowerCamelCase_							=       ''
      lowerCamelCase_							=       sorted(glob.glob(os.path.join(args.output_dir      ,				'*.ckpt'	)      ,				recursive=lowercase	)	)
      if checkpoints:
            lowerCamelCase_							=       checkpoints[-1]
            lowerCamelCase_							=       checkpoints[-1]
      trainer.logger.log_hyperparams(model.hparams	)

      # test() without a model tests using the best checkpoint automatically
      trainer.test()
      return model


if __name__ == "__main__":
 lowerCamelCase							:						Optional[int]					       =  argparse.ArgumentParser()
 lowerCamelCase							:						List[str]					       =  pl.Trainer.add_argparse_args(parser)
 lowerCamelCase							:						List[Any]					       =  SummarizationModule.add_model_specific_args(parser, os.getcwd())

 lowerCamelCase							:						List[Any]					       =  parser.parse_args()

 main(args)











'''simple docstring'''
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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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__			=      16
UpperCamelCase__			=      32




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase = 16				):


   """simple docstring"""




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

   def tokenize_function(_UpperCamelCase				):
      # max_length=None => use the model max length (it's actually the default)
      lowercase_						:     Any							     =  tokenizer(examples["sentence1"]    ,		examples["sentence2"]    ,		truncation=_UpperCamelCase    ,		max_length=_UpperCamelCase				)
      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():
      lowercase_						:     str							     =  datasets.map(
          _UpperCamelCase    ,		batched=_UpperCamelCase    ,		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
   lowercase_						:     str							     =  tokenized_datasets.rename_column("label"    ,		"labels"				)

   def collate_fn(_UpperCamelCase				):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      lowercase_						:     List[str]							     =  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":
         lowercase_						:     List[str]							     =  16
      elif accelerator.mixed_precision != "no":
         lowercase_						:     Tuple							     =  8
      else:
         lowercase_						:     Dict							     =  None

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

   # Instantiate dataloaders.
   lowercase_						:     Optional[int]							     =  DataLoader(
       tokenized_datasets["train"]    ,		shuffle=_UpperCamelCase    ,		collate_fn=_UpperCamelCase    ,		batch_size=_UpperCamelCase				)
   lowercase_						:     str							     =  DataLoader(
       tokenized_datasets["validation"]    ,		shuffle=_UpperCamelCase    ,		collate_fn=_UpperCamelCase    ,		batch_size=_UpperCamelCase				)

   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__			=      mocked_dataloaders  # noqa: F811




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,		_UpperCamelCase				) == "1":
      lowercase_						:     Tuple							     =  2
   # Initialize accelerator
   lowercase_						:     Tuple							     =  Accelerator(cpu=args.cpu    ,		mixed_precision=args.mixed_precision				)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowercase_						:     str							     =  config["lr"]
   lowercase_						:     Optional[int]							     =  int(config["num_epochs"]				)
   lowercase_						:     List[str]							     =  int(config["seed"]				)
   lowercase_						:     List[Any]							     =  int(config["batch_size"]				)

   lowercase_						:     List[Any]							     =  evaluate.load("glue"    ,		"mrpc"				)

   # New Code #
   # We now can define an inner training loop function. It should take a batch size as the only parameter,
   # and build the dataloaders in there.
   # It also gets our decorator
   @find_executable_batch_size(starting_batch_size=_UpperCamelCase				)
   def inner_training_loop(_UpperCamelCase				):
      # And now just move everything below under this function
      # We need to bring in the Accelerator object from earlier
      nonlocal accelerator
      # And reset all of its attributes that could hold onto any memory:
      accelerator.free_memory()

      # Then we can declare the model, optimizer, and everything else:
      set_seed(_UpperCamelCase				)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowercase_						:     List[str]							     =  AutoModelForSequenceClassification.from_pretrained("bert-base-cased"    ,		return_dict=_UpperCamelCase				)

      # 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).
      lowercase_						:     List[Any]							     =  model.to(accelerator.device				)

      # Instantiate optimizer
      lowercase_						:     Tuple							     =  AdamW(params=model.parameters()    ,		lr=_UpperCamelCase				)
      lowercase_     ,		lowercase_						:     Any							     =  get_dataloaders(_UpperCamelCase    ,		_UpperCamelCase				)

      # Instantiate scheduler
      lowercase_						:     Union[str, Any]							     =  get_linear_schedule_with_warmup(
          optimizer=_UpperCamelCase    ,		num_warmup_steps=100    ,		num_training_steps=(len(_UpperCamelCase				) * 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.
      lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Optional[int]							     =  accelerator.prepare(
          _UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)

      # Now we train the model
      for epoch in range(_UpperCamelCase				):
         model.train()
         for step, batch in enumerate(_UpperCamelCase				):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device				)
            lowercase_						:     Optional[Any]							     =  model(**_UpperCamelCase				)
            lowercase_						:     List[Any]							     =  outputs.loss
            accelerator.backward(_UpperCamelCase				)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

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

         lowercase_						:     int							     =  metric.compute()
         # Use accelerator.print to print only on the main process.
         accelerator.print(F"""epoch {epoch}:"""    ,		_UpperCamelCase				)

    # 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       (	):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  argparse.ArgumentParser(description="Simple example of training script."				)
   parser.add_argument(
       "--mixed_precision"    ,		type=_UpperCamelCase    ,		default=_UpperCamelCase    ,		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."				)
   lowercase_						:     int							     =  parser.parse_args()
   lowercase_						:     Optional[Any]							     =  {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(_UpperCamelCase    ,		_UpperCamelCase				)


if __name__ == "__main__":
 main()




