





'''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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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						: Tuple		=   16
__lowerCamelCase						: Tuple		=   32


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE = 16   ):


		"""simple docstring"""

		_UpperCamelCase       			=AutoTokenizer.from_pretrained('''bert-base-cased'''   )
		_UpperCamelCase       			=load_dataset('''glue'''  ,      '''mrpc'''   )

		def tokenize_function(__SCREAMING_SNAKE_CASE   ):
				# max_length=None => use the model max length (it's actually the default)
				_UpperCamelCase       			=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():
				_UpperCamelCase       			=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
		_UpperCamelCase       			=tokenized_datasets.rename_column('''label'''  ,      '''labels'''   )

		def collate_fn(__SCREAMING_SNAKE_CASE   ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				_UpperCamelCase       			=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       			=16
				elif accelerator.mixed_precision != "no":
						_UpperCamelCase       			=8
				else:
						_UpperCamelCase       			=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.
		_UpperCamelCase       			=DataLoader(
		    tokenized_datasets['''train''']  ,      shuffle=__SCREAMING_SNAKE_CASE  ,      collate_fn=__SCREAMING_SNAKE_CASE  ,      batch_size=__SCREAMING_SNAKE_CASE   )
		_UpperCamelCase       			=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						: Union[str, Any]		=   mocked_dataloaders  # noqa: F811


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

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

		_UpperCamelCase       			=evaluate.load('''glue'''  ,      '''mrpc'''   )

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

		set_seed(__SCREAMING_SNAKE_CASE   )
		_UpperCamelCase			,     _UpperCamelCase       			=get_dataloaders(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_UpperCamelCase       			=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).
		_UpperCamelCase       			=model.to(accelerator.device   )

		# Instantiate optimizer
		_UpperCamelCase       			=AdamW(params=model.parameters()  ,      lr=__SCREAMING_SNAKE_CASE   )

		# Instantiate scheduler
		_UpperCamelCase       			=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.
		_UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase       			=accelerator.prepare(
		    __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

		# Now we train the model
		for epoch in range(__SCREAMING_SNAKE_CASE   ):
				model.train()
				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   )
						_UpperCamelCase       			=model(**__SCREAMING_SNAKE_CASE   )
						_UpperCamelCase       			=outputs.loss
						_UpperCamelCase       			=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()
				_UpperCamelCase       			=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   )
						with torch.no_grad():
								_UpperCamelCase       			=model(**__SCREAMING_SNAKE_CASE   )
						_UpperCamelCase       			=outputs.logits.argmax(dim=-1   )
						_UpperCamelCase			,     _UpperCamelCase       			=accelerator.gather((predictions, batch['''labels'''])   )
						# New Code #
						# First we check if it's a distributed system
						if accelerator.use_distributed:
								# Then see if we're on the last batch of our eval dataloader
								if step == len(__SCREAMING_SNAKE_CASE   ) - 1:
										# Last batch needs to be truncated on distributed systems as it contains additional samples
										_UpperCamelCase       			=predictions[: len(eval_dataloader.dataset   ) - samples_seen]
										_UpperCamelCase       			=references[: len(eval_dataloader.dataset   ) - samples_seen]
								else:
										# Otherwise we add the number of samples seen
										samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
						metric.add_batch(
						    predictions=__SCREAMING_SNAKE_CASE  ,      references=__SCREAMING_SNAKE_CASE  ,      )

				_UpperCamelCase       			=metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}:'''  ,      __SCREAMING_SNAKE_CASE   )


def 					_a   ():


		"""simple docstring"""

		_UpperCamelCase       			=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.'''   )
		_UpperCamelCase       			=parser.parse_args()
		_UpperCamelCase       			={'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
						main()













'''simple docstring'''






import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  return EnvironmentCommand()


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  return EnvironmentCommand(args.accelerate_config_file   )







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  @staticmethod
  def   UpperCamelCase__      (				UpperCamelCase__    :					ArgumentParser  )     ->						Union[str, Any]:
    _UpperCamelCase       			=parser.add_parser('''env'''  )
    download_parser.set_defaults(func=UpperCamelCase__  )
    download_parser.add_argument(
        '''--accelerate-config_file'''							,	default=UpperCamelCase__							,	help='''The accelerate config file to use for the default values in the launching script.'''							,	)
    download_parser.set_defaults(func=UpperCamelCase__  )


  def __init__(				self    :					Tuple							,	UpperCamelCase__    :					Dict							,	*UpperCamelCase__    :					str  )     ->						None:
    _UpperCamelCase       			=accelerate_config_file


  def   UpperCamelCase__      (				self    :					str  )     ->						int:
    _UpperCamelCase       			='''not installed'''
    if is_safetensors_available():
      import safetensors

      _UpperCamelCase       			=safetensors.__version__
    elif importlib.util.find_spec('''safetensors'''  ) is not None:
      import safetensors

      _UpperCamelCase       			=F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			=_UpperCamelCase       			='''not found'''
    if is_accelerate_available():
      import accelerate
      from accelerate.commands.config import default_config_file, load_config_from_file

      _UpperCamelCase       			=accelerate.__version__
      # Get the default from the config file.
      if self._accelerate_config_file is not None or os.path.isfile(UpperCamelCase__  ):
        _UpperCamelCase       			=load_config_from_file(self._accelerate_config_file  ).to_dict()

      _UpperCamelCase       			=(
          '''\n'''.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]  )
          if isinstance(UpperCamelCase__							,	UpperCamelCase__  )
          else F'''\t{accelerate_config}'''
      )

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''NA'''
    if is_torch_available():
      import torch

      _UpperCamelCase       			=torch.__version__
      _UpperCamelCase       			=torch.cuda.is_available()

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''NA'''
    if is_tf_available():
      import tensorflow as tf

      _UpperCamelCase       			=tf.__version__
      try:
        # deprecated in v2.1
        _UpperCamelCase       			=tf.test.is_gpu_available()
      except AttributeError:
        # returns list of devices, convert to bool
        _UpperCamelCase       			=bool(tf.config.list_physical_devices('''GPU'''  )  )

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''NA'''
    if is_flax_available():
      import flax
      import jax
      import jaxlib

      _UpperCamelCase       			=flax.__version__
      _UpperCamelCase       			=jax.__version__
      _UpperCamelCase       			=jaxlib.__version__
      _UpperCamelCase       			=jax.lib.xla_bridge.get_backend().platform

    _UpperCamelCase       			={
        '''`transformers` version''': version,
        '''Platform''': platform.platform(),
        '''Python version''': platform.python_version(),
        '''Huggingface_hub version''': huggingface_hub.__version__,
        '''Safetensors version''': F'''{safetensors_version}''',
        '''Accelerate version''': F'''{accelerate_version}''',
        '''Accelerate config''': F'''{accelerate_config_str}''',
        '''PyTorch version (GPU?)''': F'''{pt_version} ({pt_cuda_available})''',
        '''Tensorflow version (GPU?)''': F'''{tf_version} ({tf_cuda_available})''',
        '''Flax version (CPU?/GPU?/TPU?)''': F'''{flax_version} ({jax_backend})''',
        '''Jax version''': F'''{jax_version}''',
        '''JaxLib version''': F'''{jaxlib_version}''',
        '''Using GPU in script?''': '''<fill in>''',
        '''Using distributed or parallel set-up in script?''': '''<fill in>''',
    }

    print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''  )
    print(self.format_dict(UpperCamelCase__  )  )

    return info




  @staticmethod
  def   UpperCamelCase__      (				UpperCamelCase__    :					Optional[Any]  )     ->						Dict:
    return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]  ) + "\n"






