
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__    (       A_					):






  '''simple docstring'''





  return EnvironmentCommand()

def a__    (       A_					):






  '''simple docstring'''





  return EnvironmentCommand(args.accelerate_config_file					)





class        UpperCAmelCase_			( a__     ):


  '''simple docstring'''







  @staticmethod
  def 							_lowercase							( UpperCamelCase__			: Optional[int]    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       parser.add_parser("""env"""    )
    download_parser.set_defaults(func=__lowerCAmelCase    )
    download_parser.add_argument(
        """--accelerate-config_file"""			,							default=__lowerCAmelCase			,							help="""The accelerate config file to use for the default values in the launching script."""			,							)
    download_parser.set_defaults(func=__lowerCAmelCase    )
  def __init__( self			: int			,							UpperCamelCase__			: Union[str, Any]			,							*UpperCamelCase__			: Optional[Any]    )			->	None:







    """simple docstring"""






    __magic_name__				     =       accelerate_config_file
  def 							_lowercase							( self			: int    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       '''not installed'''
    if is_safetensors_available():
      import safetensors

      __magic_name__				     =       safetensors.__version__
    elif importlib.util.find_spec("""safetensors"""    ) is not None:
      import safetensors

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

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

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

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

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

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

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

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

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

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

    __magic_name__				     =       {
        '''`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(__lowerCAmelCase    )    )

    return info





  @staticmethod
  def 							_lowercase							( UpperCamelCase__			: str    )			->	int:







    """simple docstring"""






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


'''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 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: Optional[Any]          =   16
__a: Any          =   32
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase = 16     ):
 lowercase__				:    Dict  =					AutoTokenizer.from_pretrained('''bert-base-cased'''     )
 lowercase__				:    Optional[int]  =					load_dataset('''glue'''		,     '''mrpc'''     )

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

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

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

 # Instantiate dataloaders.
 lowercase__				:    str  =					DataLoader(
     tokenized_datasets['''train''']		,     shuffle=UpperCAmelCase		,     collate_fn=UpperCAmelCase		,     batch_size=UpperCAmelCase     )
 lowercase__				:    Optional[int]  =					DataLoader(
     tokenized_datasets['''validation''']		,     shuffle=UpperCAmelCase		,     collate_fn=UpperCAmelCase		,     batch_size=UpperCAmelCase     )

 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: Tuple          =   mocked_dataloaders  # noqa: F811
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 # For testing only
 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,     UpperCAmelCase     ) == "1":
  lowercase__				:    Optional[int]  =					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:
  lowercase__				:    Union[str, Any]  =					Accelerator(
      cpu=args.cpu		,     mixed_precision=args.mixed_precision		,     log_with='''all'''		,     project_dir=args.project_dir     )
 else:
  lowercase__				:    Any  =					Accelerator(cpu=args.cpu		,     mixed_precision=args.mixed_precision     )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 lowercase__				:    int  =					config['''lr''']
 lowercase__				:    Optional[int]  =					int(config['''num_epochs''']     )
 lowercase__				:    Optional[Any]  =					int(config['''seed''']     )
 lowercase__				:    int  =					int(config['''batch_size''']     )
 set_seed(UpperCAmelCase     )

 lowercase__ ,							lowercase__				:    str  =					get_dataloaders(UpperCAmelCase		,     UpperCAmelCase     )
 lowercase__				:    Union[str, Any]  =					evaluate.load('''glue'''		,     '''mrpc'''     )

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

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

 # 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).
 lowercase__				:    List[str]  =					model.to(accelerator.device     )

 # Instantiate optimizer
 lowercase__				:    List[Any]  =					AdamW(params=model.parameters()		,     lr=UpperCAmelCase     )

 # Instantiate scheduler
 lowercase__				:    List[str]  =					get_linear_schedule_with_warmup(
     optimizer=UpperCAmelCase		,     num_warmup_steps=100		,     num_training_steps=(len(UpperCAmelCase     ) * 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.
 lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__				:    List[str]  =					accelerator.prepare(
     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )

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

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

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

  lowercase__				:    int  =					metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}:"""		,     UpperCAmelCase     )

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

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


if __name__ == "__main__":
  main()

