





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    __UpperCAmelCase(      lowercase_ ,    lowercase_ = 16      ):
       _lowerCamelCase							:     Tuple	=		AutoTokenizer.from_pretrained('''bert-base-cased'''      )
       _lowerCamelCase							:     Tuple	=		load_dataset('''glue''' ,    '''mrpc'''      )

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

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

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

       # Instantiate dataloaders.
       _lowerCamelCase							:     Union[str, Any]	=		DataLoader(
           tokenized_datasets['''train'''] ,    shuffle=lowercase_ ,    collate_fn=lowercase_ ,    batch_size=lowercase_      )
       _lowerCamelCase							:     Tuple	=		DataLoader(
           tokenized_datasets['''validation'''] ,    shuffle=lowercase_ ,    collate_fn=lowercase_ ,    batch_size=lowercase_      )

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

       _lowerCamelCase,  _lowerCamelCase							:     List[str]	=		get_dataloaders(lowercase_ ,    lowercase_      )
       _lowerCamelCase							:     Dict	=		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							:     str	=		batch_size // MAX_GPU_BATCH_SIZE
              _lowerCamelCase							:     Optional[Any]	=		MAX_GPU_BATCH_SIZE

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

       # 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							:     List[str]	=		AdamW(params=model.parameters() ,    lr=lowercase_      )

       # Instantiate scheduler
       _lowerCamelCase							:     List[Any]	=		get_linear_schedule_with_warmup(
           optimizer=lowercase_ ,    num_warmup_steps=1_00 ,    num_training_steps=(len(lowercase_      ) * 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							:     Tuple	=		accelerator.prepare(
           lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_      )

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

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

              model.eval()
              for step, batch in enumerate(lowercase_      ):
                     # 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							:     Dict	=		model(**lowercase_      )
                     _lowerCamelCase							:     Dict	=		outputs.logits.argmax(dim=-1      )
                     _lowerCamelCase,  _lowerCamelCase							:     int	=		accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
                     metric.add_batch(
                         predictions=lowercase_ ,    references=lowercase_ ,    )

              _lowerCamelCase							:     Union[str, Any]	=		metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:""" ,    lowercase_      )

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

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


if __name__ == "__main__":
       main()








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    __UpperCAmelCase(      lowercase_      ):
       return EnvironmentCommand()




def    __UpperCAmelCase(      lowercase_      ):
       return EnvironmentCommand(args.accelerate_config_file      )







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      @staticmethod
      def 				__snake_case			(		a__):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		parser.add_parser('''env''')
             download_parser.set_defaults(func=a__)
             download_parser.add_argument(
                 '''--accelerate-config_file'''      ,							default=a__      ,							help='''The accelerate config file to use for the default values in the launching script.'''      ,							)
             download_parser.set_defaults(func=a__)


      def __init__(		self      ,							a__      ,							*a__):



             """simple docstring"""





             _lowerCamelCase							:     str	=		accelerate_config_file


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		'''not installed'''
             if is_safetensors_available():
                    import safetensors

                    _lowerCamelCase							:     Optional[Any]	=		safetensors.__version__
             elif importlib.util.find_spec('''safetensors''') is not None:
                    import safetensors

                    _lowerCamelCase							:     Optional[int]	=		F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

             _lowerCamelCase							:     Union[str, Any]	=		'''not installed'''
             _lowerCamelCase							:     Any	=		'''not found'''
             if is_accelerate_available():
                    import accelerate
                    from accelerate.commands.config import default_config_file, load_config_from_file

                    _lowerCamelCase							:     Optional[int]	=		accelerate.__version__
                    # Get the default from the config file.
                    if self._accelerate_config_file is not None or os.path.isfile(a__):
                           _lowerCamelCase							:     Optional[int]	=		load_config_from_file(self._accelerate_config_file).to_dict()

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

             _lowerCamelCase							:     List[Any]	=		'''not installed'''
             _lowerCamelCase							:     Tuple	=		'''NA'''
             if is_torch_available():
                    import torch

                    _lowerCamelCase							:     int	=		torch.__version__
                    _lowerCamelCase							:     List[str]	=		torch.cuda.is_available()

             _lowerCamelCase							:     str	=		'''not installed'''
             _lowerCamelCase							:     Union[str, Any]	=		'''NA'''
             if is_tf_available():
                    import tensorflow as tf

                    _lowerCamelCase							:     List[str]	=		tf.__version__
                    try:
                           # deprecated in v2.1
                           _lowerCamelCase							:     Optional[int]	=		tf.test.is_gpu_available()
                    except AttributeError:
                           # returns list of devices, convert to bool
                           _lowerCamelCase							:     Optional[int]	=		bool(tf.config.list_physical_devices('''GPU'''))

             _lowerCamelCase							:     str	=		'''not installed'''
             _lowerCamelCase							:     List[Any]	=		'''not installed'''
             _lowerCamelCase							:     List[Any]	=		'''not installed'''
             _lowerCamelCase							:     Optional[int]	=		'''NA'''
             if is_flax_available():
                    import flax
                    import jax
                    import jaxlib

                    _lowerCamelCase							:     Any	=		flax.__version__
                    _lowerCamelCase							:     str	=		jax.__version__
                    _lowerCamelCase							:     Any	=		jaxlib.__version__
                    _lowerCamelCase							:     int	=		jax.lib.xla_bridge.get_backend().platform

             _lowerCamelCase							:     int	=		{
                 '''`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(a__))

             return info



      @staticmethod
      def 				__snake_case			(		a__):



             """simple docstring"""





             return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]) + "\n"

