


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
#
########################################################################

UpperCAmelCase_				:			Union[str, Any]   =	16
UpperCAmelCase_				:			str   =	32







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Accelerator  ,   __A			:      int = 16    )     ->		Union[str, Any]:

       """simple docstring"""

       a_ :		str							=  AutoTokenizer.from_pretrained('bert-base-cased'    )
       a_ :		List[str]							=  load_dataset('glue'  ,   'mrpc'    )

       def tokenize_function(__A			:      Any    ):
              # max_length=None => use the model max length (it's actually the default)
              a_ :		List[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():
              a_ :		Union[str, Any]							=  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'    )

       def collate_fn(__A			:      Optional[int]    ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              a_ :		Optional[int]							=  1_28 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_ :		Tuple							=  16
              elif accelerator.mixed_precision != "no":
                     a_ :		int							=  8
              else:
                     a_ :		List[str]							=  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_ :		List[str]							=  DataLoader(
           tokenized_datasets['validation']  ,   shuffle=__A  ,   collate_fn=__A  ,   batch_size=__A    )

       return train_dataloader, eval_dataloader


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

    UpperCAmelCase_				:			str   =	mocked_dataloaders  # noqa: F811







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      List[str]    )     ->		int:

       """simple docstring"""

       if os.environ.get('TESTING_MOCKED_DATALOADERS'  ,   __A    ) == "1":
              a_ :		Dict							=  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:
              a_ :		Optional[int]							=  Accelerator(
                  cpu=args.cpu  ,   mixed_precision=args.mixed_precision  ,   log_with='all'  ,   project_dir=args.project_dir    )
       else:
              a_ :		Tuple							=  Accelerator(cpu=args.cpu  ,   mixed_precision=args.mixed_precision    )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       a_ :		List[str]							=  config['lr']
       a_ :		Dict							=  int(config['num_epochs']    )
       a_ :		Union[str, Any]							=  int(config['seed']    )
       a_ :		Dict							=  int(config['batch_size']    )
       set_seed(__A    )

       a_ :		List[Any]							=  get_dataloaders(__A  ,   __A    )
       a_ :		Optional[Any]							=  evaluate.load('glue'  ,   'mrpc'    )

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

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       a_ :		List[str]							=  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_ :		Any							=  model.to(accelerator.device    )

       # Instantiate optimizer
       a_ :		str							=  AdamW(params=model.parameters()  ,   lr=__A    )

       # Instantiate scheduler
       a_ :		Tuple							=  get_linear_schedule_with_warmup(
           optimizer=__A  ,   num_warmup_steps=1_00  ,   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_ :		Optional[int]							=  accelerator.prepare(
           __A  ,   __A  ,   __A  ,   __A  ,   __A    )

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

       # Now we train the model
       for epoch in range(__A    ):
              model.train()
              # New Code #
              # For our tracking example, we will log the total loss of each epoch
              if args.with_tracking:
                     a_ :		Union[str, Any]							=  0
              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_ :		Union[str, Any]							=  model(**__A    )
                     a_ :		Optional[int]							=  outputs.loss
                     # New Code #
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     a_ :		int							=  loss / gradient_accumulation_steps
                     accelerator.backward(__A    )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

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

              a_ :		Any							=  metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:"""  ,   __A    )

              # 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(__A    ),
                             'epoch': epoch,
                         }  ,   step=__A  ,   )

    # 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 	SCREAMING_SNAKE_CASE_						(      )     ->		List[Any]:

       """simple docstring"""

       a_ :		Optional[int]							=  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(
           '--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=__A  ,   default='logs'  ,   help='Location on where to store experiment tracking logs` and relevent project information'  ,   )
       a_ :		Optional[Any]							=  parser.parse_args()
       a_ :		str							=  {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(__A  ,   __A    )


if __name__ == "__main__":
    main()






def 	SCREAMING_SNAKE_CASE_						(      __A			:      list    )     ->		list:

       """simple docstring"""

       a_ :		int							=  len(__A    )
       for _ in range(__A    ):
              for i in range(_ % 2  ,   arr_size - 1  ,   2    ):
                     if arr[i + 1] < arr[i]:
                            a_							,		a_ :		int							=  arr[i + 1], arr[i]

       return arr


if __name__ == "__main__":
   UpperCAmelCase_				:			int   =	list(range(10, 0, -1))
   print(F'Original: {arr}. Sorted: {odd_even_transposition(arr)}')


