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

a_    					=       16
a_    					=       32





def    __lowercase     (						lowerCamelCase					:     Accelerator  ,  lowerCamelCase					:     int = 16	):
 UpperCamelCase_					:						Tuple =					AutoTokenizer.from_pretrained('bert-base-cased'	)
 UpperCamelCase_					:						Tuple =					load_dataset('glue'  ,  'mrpc'	)

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

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

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

 # Instantiate dataloaders.
 UpperCamelCase_					:						List[str] =					DataLoader(
     tokenized_datasets['train']  ,  shuffle=lowerCamelCase  ,  collate_fn=lowerCamelCase  ,  batch_size=lowerCamelCase	)
 UpperCamelCase_					:						Dict =					DataLoader(
     tokenized_datasets['validation']  ,  shuffle=lowerCamelCase  ,  collate_fn=lowerCamelCase  ,  batch_size=lowerCamelCase	)

 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

   a_    					=       mocked_dataloaders  # noqa: F811





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

 UpperCamelCase_, UpperCamelCase_					:						List[str] =					get_dataloaders(lowerCamelCase  ,  lowerCamelCase	)
 UpperCamelCase_					:						Union[str, Any] =					evaluate.load('glue'  ,  'mrpc'	)

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

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 UpperCamelCase_					:						Any =					AutoModelForSequenceClassification.from_pretrained('bert-base-cased'  ,  return_dict=lowerCamelCase	)

 # 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).
 UpperCamelCase_					:						int =					model.to(accelerator.device	)

 # Instantiate optimizer
 UpperCamelCase_					:						List[str] =					AdamW(params=model.parameters()  ,  lr=lowerCamelCase	)

 # Instantiate scheduler
 UpperCamelCase_					:						int =					get_linear_schedule_with_warmup(
     optimizer=lowerCamelCase  ,  num_warmup_steps=100  ,  num_training_steps=(len(lowerCamelCase	) * 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.
 UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_					:						Dict =					accelerator.prepare(
     lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)

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

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

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

  UpperCamelCase_					:						List[str] =					metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"epoch {epoch}:"  ,  lowerCamelCase	)

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

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


if __name__ == "__main__":
   main()




import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)





def    __lowercase     (						lowerCamelCase					:     Dict  ,  lowerCamelCase					:     int=False	):
 try:
  UpperCamelCase_					:						Union[str, Any] =					os.environ[key]
 except KeyError:
  # KEY isn't set, default to `default`.
  UpperCamelCase_					:						List[str] =					default
 else:
  # KEY is set, convert it to True or False.
  try:
   UpperCamelCase_					:						Union[str, Any] =					strtobool(lowerCamelCase	)
  except ValueError:
   # More values are supported, but let's keep the message simple.
   raise ValueError(F"If set, {key} must be yes or no."	)
 return _value


a_    					=       parse_flag_from_env('RUN_SLOW', default=False)





def    __lowercase     (						lowerCamelCase					:     List[Any]	):
 return unittest.skip('Test was skipped'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     int	):
 return unittest.skipUnless(_run_slow_tests  ,  'test is slow'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     str	):
 return unittest.skipUnless(not torch.cuda.is_available()  ,  'test requires only a CPU'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     Optional[Any]	):
 return unittest.skipUnless(torch.cuda.is_available()  ,  'test requires a GPU'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     Any	):
 return unittest.skipUnless(is_xpu_available()  ,  'test requires a XPU'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     Any	):
 return unittest.skipUnless(is_mps_available()  ,  'test requires a `mps` backend support in `torch`'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     str	):
 return unittest.skipUnless(
     is_transformers_available() and is_datasets_available()  ,  'test requires the Hugging Face suite'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     List[str]	):
 return unittest.skipUnless(is_bnb_available()  ,  'test requires the bitsandbytes library'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     str	):
 return unittest.skipUnless(is_tpu_available()  ,  'test requires TPU'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     Tuple	):
 return unittest.skipUnless(torch.cuda.device_count() == 1  ,  'test requires a GPU'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     Tuple	):
 return unittest.skipUnless(torch.xpu.device_count() == 1  ,  'test requires a XPU'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     Optional[Any]	):
 return unittest.skipUnless(torch.cuda.device_count() > 1  ,  'test requires multiple GPUs'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     List[Any]	):
 return unittest.skipUnless(torch.xpu.device_count() > 1  ,  'test requires multiple XPUs'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     int	):
 return unittest.skipUnless(is_safetensors_available()  ,  'test requires safetensors'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     Any	):
 return unittest.skipUnless(is_deepspeed_available()  ,  'test requires DeepSpeed'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     Tuple	):
 return unittest.skipUnless(is_torch_version('>='  ,  '1.12.0'	)  ,  'test requires torch version >= 1.12.0'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     List[Any]=None  ,  lowerCamelCase					:     Optional[int]=None	):
 if test_case is None:
  return partial(lowerCamelCase  ,  version=lowerCamelCase	)
 return unittest.skipUnless(is_torch_version('>='  ,  lowerCamelCase	)  ,  F"test requires torch version >= {version}"	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     int	):
 return unittest.skipUnless(is_tensorboard_available()  ,  'test requires Tensorboard'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     int	):
 return unittest.skipUnless(is_wandb_available()  ,  'test requires wandb'	)(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     Dict	):
 return unittest.skipUnless(is_comet_ml_available()  ,  'test requires comet_ml'	)(lowerCamelCase	)


a_    					=       (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)





def    __lowercase     (						lowerCamelCase					:     Dict	):
 return unittest.skipUnless(
     _atleast_one_tracker_available  ,  'test requires at least one tracker to be available and for `comet_ml` to not be installed'  ,  )(lowerCamelCase	)





class 		_lowercase						(					unittest.TestCase       ):
 lowercase				     =   True

 @classmethod
 def 						SCREAMING_SNAKE_CASE__   ( cls	:							Union[str, Any]			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						str =					tempfile.mkdtemp()

 @classmethod
 def 						SCREAMING_SNAKE_CASE__   ( cls	:							Any			)   ->						Union[str, Any]:

  """simple docstring"""





  if os.path.exists(cls.tmpdir			):
   shutil.rmtree(cls.tmpdir			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						List[str]:

  """simple docstring"""





  if self.clear_on_setup:
   for path in Path(self.tmpdir			).glob('**/*'			):
    if path.is_file():
     path.unlink()
    elif path.is_dir():
     shutil.rmtree(snake_case			)





class 		_lowercase						(					unittest.TestCase       ):

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Any:

  """simple docstring"""





  super().tearDown()
  # Reset the state of the AcceleratorState singleton.
  AcceleratorState._reset_state()
  PartialState._reset_state()





class 		_lowercase						(					unittest.TestCase       ):

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							Union[mock.Mock, List[mock.Mock]]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						str =					mocks if isinstance(snake_case   ,      (tuple, list)			) else [mocks]
  for m in self.mocks:
   m.start()
   self.addCleanup(m.stop			)





def    __lowercase     (						lowerCamelCase					:     Optional[Any]	):
 UpperCamelCase_					:						str =					AcceleratorState()
 UpperCamelCase_					:						str =					tensor[None].clone().to(state.device	)
 UpperCamelCase_					:						List[Any] =					gather(lowerCamelCase	).cpu()
 UpperCamelCase_					:						Tuple =					tensor[0].cpu()
 for i in range(tensors.shape[0]	):
  if not torch.equal(tensors[i]  ,  lowerCamelCase	):
   return False
 return True





class 		_lowercase						:

 def __init__( self	:							Optional[int]   ,      snake_case	:							Any   ,      snake_case	:							List[Any]   ,      snake_case	:							int			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						int =					returncode
  UpperCamelCase_					:						Optional[int] =					stdout
  UpperCamelCase_					:						Optional[int] =					stderr





async def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     Tuple	):
 while True:
  UpperCamelCase_					:						Tuple =					await stream.readline()
  if line:
   callback(lowerCamelCase	)
  else:
   break





async def    __lowercase     (						lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Dict=None  ,  lowerCamelCase					:     Optional[Any]=None  ,  lowerCamelCase					:     List[str]=None  ,  lowerCamelCase					:     Dict=False  ,  lowerCamelCase					:     Tuple=False	):
 if echo:
  print('\nRunning: '  ,  ' '.join(lowerCamelCase	)	)

 UpperCamelCase_					:						Optional[int] =					await asyncio.create_subprocess_exec(
     cmd[0]  ,  *cmd[1:]  ,  stdin=lowerCamelCase  ,  stdout=asyncio.subprocess.PIPE  ,  stderr=asyncio.subprocess.PIPE  ,  env=lowerCamelCase  ,  )

 # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
 # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
 #
 # If it starts hanging, will need to switch to the following code. The problem is that no data
 # will be seen until it's done and if it hangs for example there will be no debug info.
 # out, err = await p.communicate()
 # return _RunOutput(p.returncode, out, err)

 UpperCamelCase_					:						str =					[]
 UpperCamelCase_					:						Union[str, Any] =					[]

 def tee(lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     List[str]=""	):
  UpperCamelCase_					:						int =					line.decode('utf-8'	).rstrip()
  sink.append(lowerCamelCase	)
  if not quiet:
   print(lowerCamelCase  ,  lowerCamelCase  ,  file=lowerCamelCase	)

    # XXX: the timeout doesn't seem to make any difference here
 await asyncio.wait(
     [
         asyncio.create_task(_read_stream(p.stdout  ,  lambda lowerCamelCase	: tee(lowerCamelCase  ,  lowerCamelCase  ,  sys.stdout  ,  label='stdout:'	)	)	),
         asyncio.create_task(_read_stream(p.stderr  ,  lambda lowerCamelCase	: tee(lowerCamelCase  ,  lowerCamelCase  ,  sys.stderr  ,  label='stderr:'	)	)	),
     ]  ,  timeout=lowerCamelCase  ,  )
 return _RunOutput(await p.wait()  ,  lowerCamelCase  ,  lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     Optional[Any]=None  ,  lowerCamelCase					:     int=None  ,  lowerCamelCase					:     Any=180  ,  lowerCamelCase					:     Dict=False  ,  lowerCamelCase					:     Optional[int]=True	):
 UpperCamelCase_					:						str =					asyncio.get_event_loop()
 UpperCamelCase_					:						Union[str, Any] =					loop.run_until_complete(
     _stream_subprocess(lowerCamelCase  ,  env=lowerCamelCase  ,  stdin=lowerCamelCase  ,  timeout=lowerCamelCase  ,  quiet=lowerCamelCase  ,  echo=lowerCamelCase	)	)

 UpperCamelCase_					:						int =					' '.join(lowerCamelCase	)
 if result.returncode > 0:
  UpperCamelCase_					:						Dict =					'\n'.join(result.stderr	)
  raise RuntimeError(
      F"'{cmd_str}' failed with returncode {result.returncode}\n\n"
      F"The combined stderr from workers follows:\n{stderr}"	)

 return result





class 		_lowercase						(					snake_case_       ):
 pass





def    __lowercase     (						lowerCamelCase					:     List[str]  ,  lowerCamelCase					:     Optional[int]=False	):
 try:
  UpperCamelCase_					:						Any =					subprocess.check_output(lowerCamelCase  ,  stderr=subprocess.STDOUT	)
  if return_stdout:
   if hasattr(lowerCamelCase  ,  'decode'	):
    UpperCamelCase_					:						Any =					output.decode('utf-8'	)
   return output
 except subprocess.CalledProcessError as e:
  raise SubprocessCallException(
      F"Command `{' '.join(lowerCamelCase	)}` failed with the following error:\n\n{e.output.decode()}"	) from e



