



'''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
#
########################################################################

UpperCamelCase_       :  str					=  16
UpperCamelCase_       :  Optional[int]					=  32






def 		_lowerCAmelCase				(_lowercase   ,					_lowercase = 16			):

       """simple docstring"""

       a__								= AutoTokenizer.from_pretrained("bert-base-cased"			)
       a__								= load_dataset("glue"   ,					"mrpc"			)

       def tokenize_function(_lowercase			):
              # max_length=None => use the model max length (it's actually the default)
              a__								= 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():
              a__								= 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
       a__								= 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.
              a__								= 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__								= 16
              elif accelerator.mixed_precision != "no":
                     a__								= 8
              else:
                     a__								= None

              return tokenizer.pad(
                  lowercase__   ,					padding="longest"   ,					max_length=lowercase__   ,					pad_to_multiple_of=lowercase__   ,					return_tensors="pt"   ,					)

       # Instantiate dataloaders.
       a__								= DataLoader(
           tokenized_datasets["train"]   ,					shuffle=lowercase__   ,					collate_fn=lowercase__   ,					batch_size=lowercase__			)
       a__								= 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

    UpperCamelCase_       :  Tuple					=  mocked_dataloaders  # noqa: F811






def 		_lowerCAmelCase				(_lowercase   ,					_lowercase			):

       """simple docstring"""

       if os.environ.get("TESTING_MOCKED_DATALOADERS"   ,					lowercase__			) == "1":
              a__								= 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__								= Accelerator(
                  cpu=args.cpu   ,					mixed_precision=args.mixed_precision   ,					log_with="all"   ,					project_dir=args.project_dir			)
       else:
              a__								= Accelerator(cpu=args.cpu   ,					mixed_precision=args.mixed_precision			)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       a__								= config["lr"]
       a__								= int(config["num_epochs"]			)
       a__								= int(config["seed"]			)
       a__								= int(config["batch_size"]			)
       set_seed(lowercase__			)

       a__						,					a__								= get_dataloaders(lowercase__   ,					lowercase__			)
       a__								= evaluate.load("glue"   ,					"mrpc"			)

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

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       a__								= 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).
       a__								= model.to(accelerator.device			)

       # Instantiate optimizer
       a__								= AdamW(params=model.parameters()   ,					lr=lowercase__			)

       # Instantiate scheduler
       a__								= 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.
       a__						,					a__						,					a__						,					a__						,					a__								= 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:
              a__								= 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:
                     a__								= 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			)
                     a__								= model(**lowercase__			)
                     a__								= outputs.loss
                     # New Code #
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     a__								= 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():
                            a__								= model(**lowercase__			)
                     a__								= outputs.logits.argmax(dim=-1			)
                     a__						,					a__								= accelerator.gather_for_metrics((predictions, batch["labels"])			)
                     metric.add_batch(
                         predictions=lowercase__   ,					references=lowercase__   ,					)

              a__								= 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 		_lowerCAmelCase				():

       """simple docstring"""

       a__								= 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"   ,					)
       a__								= parser.parse_args()
       a__								= {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
       training_function(lowercase__   ,					lowercase__			)


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 					__UpperCamelCase				(						lowercase__    :	Union[str, Any]			):






     '''simple docstring'''





     return EnvironmentCommand()

def 					__UpperCamelCase				(						lowercase__    :	Any			):






     '''simple docstring'''





     return EnvironmentCommand(args.accelerate_config_file			)







class 	lowerCAmelCase  (   A ):







       @staticmethod
       def       snake_case (				__lowercase      :			ArgumentParser   ):







            """simple docstring"""







            __lowercase      		=parser.add_parser('env'   )
            download_parser.set_defaults(func=__lowercase   )
            download_parser.add_argument(
                '--accelerate-config_file'    ,   default=__lowercase    ,   help='The accelerate config file to use for the default values in the launching script.'    ,   )
            download_parser.set_defaults(func=__lowercase   )







       def __init__(				self      :			int    ,   __lowercase      :			Any    ,   *__lowercase      :			str   ):







            """simple docstring"""







            __lowercase      		=accelerate_config_file







       def       snake_case (				self      :			Optional[int]   ):







            """simple docstring"""







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

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

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

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

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

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

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

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

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

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

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

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

            __lowercase      		={
                '`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(__lowercase   )   )

            return info







       @staticmethod
       def       snake_case (				__lowercase      :			Optional[Any]   ):







            """simple docstring"""







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


