


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,
# specifically showcasing the experiment tracking capability,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################

lowerCamelCase											=       16
lowerCamelCase											=       32




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Accelerator		,			SCREAMING_SNAKE_CASE__					:      int = 16     ):
       '''simple docstring'''





       _lowerCamelCase       :		Dict		=AutoTokenizer.from_pretrained('bert-base-cased'     )
       _lowerCamelCase       :		Optional[Any]		=load_dataset('glue'		,			'mrpc'     )

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

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

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

       # Instantiate dataloaders.
       _lowerCamelCase       :		int		=DataLoader(
           tokenized_datasets['train']		,			shuffle=SCREAMING_SNAKE_CASE__		,			collate_fn=SCREAMING_SNAKE_CASE__		,			batch_size=SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Any		=DataLoader(
           tokenized_datasets['validation']		,			shuffle=SCREAMING_SNAKE_CASE__		,			collate_fn=SCREAMING_SNAKE_CASE__		,			batch_size=SCREAMING_SNAKE_CASE__     )

       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

 lowerCamelCase											=       mocked_dataloaders  # noqa: F811




def 	a_      (    SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      Dict     ):
       '''simple docstring'''





       if os.environ.get('TESTING_MOCKED_DATALOADERS'		,			SCREAMING_SNAKE_CASE__     ) == "1":
              _lowerCamelCase       :		Union[str, Any]		=2
       # Initialize Accelerator

       # New Code #
       # We pass in "all" to `log_with` to grab all available trackers in the environment
       # Note: If using a custom `Tracker` class, should be passed in here such as:
       # >>> log_with = ["all", MyCustomTrackerClassInstance()]
       if args.with_tracking:
              _lowerCamelCase       :		Optional[Any]		=Accelerator(
                  cpu=args.cpu		,			mixed_precision=args.mixed_precision		,			log_with='all'		,			project_dir=args.project_dir     )
       else:
              _lowerCamelCase       :		Dict		=Accelerator(cpu=args.cpu		,			mixed_precision=args.mixed_precision     )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _lowerCamelCase       :		int		=config['lr']
       _lowerCamelCase       :		List[Any]		=int(config['num_epochs']     )
       _lowerCamelCase       :		Optional[Any]		=int(config['seed']     )
       _lowerCamelCase       :		Union[str, Any]		=int(config['batch_size']     )
       set_seed(SCREAMING_SNAKE_CASE__     )

       _lowerCamelCase	,     _lowerCamelCase       :		int		=get_dataloaders(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Tuple		=evaluate.load('glue'		,			'mrpc'     )

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

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

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

       # Instantiate optimizer
       _lowerCamelCase       :		str		=AdamW(params=model.parameters()		,			lr=SCREAMING_SNAKE_CASE__     )

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

       # New Code #
       # We need to initialize the trackers we use. Overall configurations can also be stored
       if args.with_tracking:
              _lowerCamelCase       :		Optional[Any]		=os.path.split(SCREAMING_SNAKE_CASE__     )[-1].split('.'     )[0]
              accelerator.init_trackers(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )

       # Now we train the model
       for epoch in range(SCREAMING_SNAKE_CASE__     ):
              model.train()
              # New Code #
              # For our tracking example, we will log the total loss of each epoch
              if args.with_tracking:
                     _lowerCamelCase       :		Optional[Any]		=0
              for step, batch in enumerate(SCREAMING_SNAKE_CASE__     ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device     )
                     _lowerCamelCase       :		str		=model(**SCREAMING_SNAKE_CASE__     )
                     _lowerCamelCase       :		Union[str, Any]		=outputs.loss
                     # New Code #
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     _lowerCamelCase       :		Union[str, Any]		=loss / gradient_accumulation_steps
                     accelerator.backward(SCREAMING_SNAKE_CASE__     )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

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

              _lowerCamelCase       :		Tuple		=metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'''epoch {epoch}:'''		,			SCREAMING_SNAKE_CASE__     )

              # New Code #
              # To actually log, we call `Accelerator.log`
              # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
              if args.with_tracking:
                     accelerator.log(
                         {
                             'accuracy': eval_metric['accuracy'],
                             'f1': eval_metric['f1'],
                             'train_loss': total_loss.item() / len(SCREAMING_SNAKE_CASE__     ),
                             'epoch': epoch,
                         }		,			step=SCREAMING_SNAKE_CASE__		,			)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
       if args.with_tracking:
              accelerator.end_training()




def 	a_      (    ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[Any]		=argparse.ArgumentParser(description='Simple example of training script.'     )
       parser.add_argument(
           '--mixed_precision'		,			type=SCREAMING_SNAKE_CASE__		,			default=SCREAMING_SNAKE_CASE__		,			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(
           '--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(
           '--project_dir'		,			type=SCREAMING_SNAKE_CASE__		,			default='logs'		,			help='Location on where to store experiment tracking logs` and relevent project information'		,			)
       _lowerCamelCase       :		Optional[Any]		=parser.parse_args()
       _lowerCamelCase       :		Any		={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 main()






# Lint as: python3
import itertools
import os
import re


lowerCamelCase											=       re.compile(R'([A-Z]+)([A-Z][a-z])')
lowerCamelCase											=       re.compile(R'([a-z\d])([A-Z])')

lowerCamelCase											=       re.compile(R'(?<!_)_(?!_)')
lowerCamelCase											=       re.compile(R'(_{2,})')

lowerCamelCase											=       R'^\w+(\.\w+)*$'

lowerCamelCase											=       R'<>:/\|?*'




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Tuple     ):
       '''simple docstring'''





       _lowerCamelCase       :		List[str]		=_uppercase_uppercase_re.sub(r'\1_\2'		,			SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		List[Any]		=_lowercase_uppercase_re.sub(r'\1_\2'		,			SCREAMING_SNAKE_CASE__     )
       return name.lower()




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]     ):
       '''simple docstring'''





       _lowerCamelCase       :		Tuple		=_single_underscore_re.split(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Tuple		=[_multiple_underscores_re.split(SCREAMING_SNAKE_CASE__     ) for n in name]
       return "".join(n.capitalize() for n in itertools.chain.from_iterable(SCREAMING_SNAKE_CASE__     ) if n != ''     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[Any]     ):
       '''simple docstring'''





       if os.path.basename(SCREAMING_SNAKE_CASE__     ) != name:
              raise ValueError(F'''Should be a dataset name, not a path: {name}'''     )
       return camelcase_to_snakecase(SCREAMING_SNAKE_CASE__     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Optional[int]     ):
       '''simple docstring'''





       if os.path.basename(SCREAMING_SNAKE_CASE__     ) != name:
              raise ValueError(F'''Should be a dataset name, not a path: {name}'''     )
       if not re.match(_split_re		,			SCREAMING_SNAKE_CASE__     ):
              raise ValueError(F'''Split name should match \'{_split_re}\'\' but got \'{split}\'.'''     )
       return F'''{filename_prefix_for_name(SCREAMING_SNAKE_CASE__     )}-{split}'''




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[int]		,			SCREAMING_SNAKE_CASE__					:      List[Any]		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]		,			SCREAMING_SNAKE_CASE__					:      int=None     ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		=filename_prefix_for_split(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       if filetype_suffix:
              prefix += F'''.{filetype_suffix}'''
       _lowerCamelCase       :		Optional[Any]		=os.path.join(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       return F'''{filepath}*'''




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Tuple		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      List[str]=None		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]=None     ):
       '''simple docstring'''





       _lowerCamelCase       :		Dict		=filename_prefix_for_split(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Union[str, Any]		=os.path.join(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )

       if shard_lengths:
              _lowerCamelCase       :		Union[str, Any]		=len(SCREAMING_SNAKE_CASE__     )
              _lowerCamelCase       :		Optional[int]		=[F'''{prefix}-{shard_id:05d}-of-{num_shards:05d}''' for shard_id in range(SCREAMING_SNAKE_CASE__     )]
              if filetype_suffix:
                     _lowerCamelCase       :		List[Any]		=[filename + F'''.{filetype_suffix}''' for filename in filenames]
              return filenames
       else:
              _lowerCamelCase       :		Any		=prefix
              if filetype_suffix:
                     filename += F'''.{filetype_suffix}'''
              return [filename]


