



"""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: Tuple										=     1_6
_UpperCamelCase: List[Any]										=     3_2


def      lowercase__   (	_UpperCAmelCase		,					_UpperCAmelCase = 16 )   ->	int:






      '''simple docstring'''


      lowercase     :					Any 		=      AutoTokenizer.from_pretrained('bert-base-cased' )
      lowercase     :					Optional[Any] 		=      load_dataset('glue'		,					'mrpc' )

      def tokenize_function(_UpperCAmelCase ):
            # max_length=None => use the model max length (it's actually the default)
            lowercase     :					Any 		=      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():
            lowercase     :					Optional[Any] 		=      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
      lowercase     :					Any 		=      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     :					Optional[Any] 		=      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":
                  lowercase     :					Tuple 		=      16
            elif accelerator.mixed_precision != "no":
                  lowercase     :					Any 		=      8
            else:
                  lowercase     :					int 		=      None

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

      # Instantiate dataloaders.
      lowercase     :					Any 		=      DataLoader(
          tokenized_datasets['train']		,					shuffle=lowerCamelCase__		,					collate_fn=lowerCamelCase__		,					batch_size=lowerCamelCase__ )
      lowercase     :					List[str] 		=      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

     _UpperCamelCase: Optional[int]										=     mocked_dataloaders  # noqa: F811


def      lowercase__   (	_UpperCAmelCase		,					_UpperCAmelCase )   ->	List[str]:






      '''simple docstring'''


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

      lowercase		,			lowercase     :					List[str] 		=      get_dataloaders(lowerCamelCase__		,					lowerCamelCase__ )
      lowercase     :					str 		=      evaluate.load('glue'		,					'mrpc' )

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

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

      # Instantiate optimizer
      lowercase     :					Union[str, Any] 		=      AdamW(params=model.parameters()		,					lr=lowerCamelCase__ )

      # Instantiate scheduler
      lowercase     :					Union[str, Any] 		=      get_linear_schedule_with_warmup(
          optimizer=lowerCamelCase__		,					num_warmup_steps=1_00		,					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.
      lowercase		,			lowercase		,			lowercase		,			lowercase		,			lowercase     :					str 		=      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:
            lowercase     :					Optional[int] 		=      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:
                  lowercase     :					Dict 		=      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 )
                  lowercase     :					str 		=      model(**lowerCamelCase__ )
                  lowercase     :					Tuple 		=      outputs.loss
                  # New Code #
                  if args.with_tracking:
                        total_loss += loss.detach().float()
                  lowercase     :					Union[str, Any] 		=      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():
                        lowercase     :					Tuple 		=      model(**lowerCamelCase__ )
                  lowercase     :					List[Any] 		=      outputs.logits.argmax(dim=-1 )
                  lowercase		,			lowercase     :					Tuple 		=      accelerator.gather_for_metrics((predictions, batch['labels']) )
                  metric.add_batch(
                      predictions=lowerCamelCase__		,					references=lowerCamelCase__		,					)

            lowercase     :					Optional[int] 		=      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__   (	)   ->	List[Any]:






      '''simple docstring'''


      lowercase     :					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'		,					)
      lowercase     :					Union[str, Any] 		=      parser.parse_args()
      lowercase     :					Union[str, Any] 		=      {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(lowerCamelCase__		,					lowerCamelCase__ )


if __name__ == "__main__":
     main()


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A				={'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''XGLMTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''XGLMTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XGLMForCausalLM''',
			    '''XGLMModel''',
			    '''XGLMPreTrainedModel''',
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''FlaxXGLMForCausalLM''',
			    '''FlaxXGLMModel''',
			    '''FlaxXGLMPreTrainedModel''',
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXGLMForCausalLM''',
			    '''TFXGLMModel''',
			    '''TFXGLMPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm import XGLMTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm_fast import XGLMTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						    TFXGLMPreTrainedModel,
						)


else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure)


