






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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 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								=						16
__UpperCAmelCase								=						32




def      lowercase__      (     __snake_case    :							Accelerator     ,       __snake_case    :							int = 16  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Union[str, Any]            =						AutoTokenizer.from_pretrained('bert-base-cased'  )
					UpperCAmelCase_    :      Any            =						load_dataset('glue'     ,       'mrpc'  )

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

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

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

					# Instantiate dataloaders.
					UpperCAmelCase_    :      int            =						DataLoader(
					    tokenized_datasets['train']     ,       shuffle=__snake_case     ,       collate_fn=__snake_case     ,       batch_size=__snake_case  )
					UpperCAmelCase_    :      Tuple            =						DataLoader(
					    tokenized_datasets['validation']     ,       shuffle=__snake_case     ,       collate_fn=__snake_case     ,       batch_size=__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

		__UpperCAmelCase								=						mocked_dataloaders  # noqa: F811




def      lowercase__      (     __snake_case    :							List[str]     ,       __snake_case    :							Optional[int]  ):


					'''simple docstring'''


					if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,       __snake_case  ) == "1":
										UpperCAmelCase_    :      Optional[int]            =						2
					# New Code #
					UpperCAmelCase_    :      int            =						int(args.gradient_accumulation_steps  )
					UpperCAmelCase_    :      List[Any]            =						int(args.local_sgd_steps  )
					# Initialize accelerator
					UpperCAmelCase_    :      Dict            =						Accelerator(
					    cpu=args.cpu     ,       mixed_precision=args.mixed_precision     ,       gradient_accumulation_steps=__snake_case  )
					if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
										raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'  )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					UpperCAmelCase_    :      List[str]            =						config['lr']
					UpperCAmelCase_    :      str            =						int(config['num_epochs']  )
					UpperCAmelCase_    :      str            =						int(config['seed']  )
					UpperCAmelCase_    :      str            =						int(config['batch_size']  )

					UpperCAmelCase_    :      List[str]            =						evaluate.load('glue'     ,       'mrpc'  )

					set_seed(__snake_case  )
					UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						get_dataloaders(__snake_case     ,       __snake_case  )
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCAmelCase_    :      Optional[Any]            =						AutoModelForSequenceClassification.from_pretrained('bert-base-cased'     ,       return_dict=__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_    :      Any            =						model.to(accelerator.device  )

					# Instantiate optimizer
					UpperCAmelCase_    :      Optional[Any]            =						AdamW(params=model.parameters()     ,       lr=__snake_case  )

					# Instantiate scheduler
					UpperCAmelCase_    :      Tuple            =						get_linear_schedule_with_warmup(
					    optimizer=__snake_case     ,       num_warmup_steps=100     ,       num_training_steps=(len(__snake_case  ) * num_epochs)     ,       )

					# 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_    :      List[str]            =						accelerator.prepare(
					    __snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case  )

					# Now we train the model
					for epoch in range(__snake_case  ):
										model.train()
										with LocalSGD(
										    accelerator=__snake_case     ,       model=__snake_case     ,       local_sgd_steps=__snake_case     ,       enabled=local_sgd_steps is not None  ) as local_sgd:
															for step, batch in enumerate(__snake_case  ):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device  )
																				# New code #
																				# We use the new `accumulate` context manager to perform gradient accumulation
																				# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																				with accelerator.accumulate(__snake_case  ):
																									UpperCAmelCase_    :      str            =						model(**__snake_case  )
																									UpperCAmelCase_    :      List[str]            =						output.loss
																									accelerator.backward(__snake_case  )
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()
																									# LocalSGD-specific line
																									local_sgd.step()

										model.eval()
										for step, batch in enumerate(__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_    :      Any            =						model(**__snake_case  )
															UpperCAmelCase_    :      Union[str, Any]            =						outputs.logits.argmax(dim=-1  )
															UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						accelerator.gather_for_metrics((predictions, batch['labels'])  )
															metric.add_batch(
															    predictions=__snake_case     ,       references=__snake_case     ,       )

										UpperCAmelCase_    :      Tuple            =						metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"epoch {epoch}:"     ,       __snake_case  )




def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      Union[str, Any]            =						argparse.ArgumentParser(description='Simple example of training script.'  )
					parser.add_argument(
					    '--mixed_precision'     ,       type=__snake_case     ,       default=__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.'     ,       )
					# New Code #
					parser.add_argument(
					    '--gradient_accumulation_steps'     ,       type=__snake_case     ,       default=1     ,       help='The number of minibatches to be ran before gradients are accumulated.'     ,       )
					parser.add_argument(
					    '--local_sgd_steps'     ,       type=__snake_case     ,       default=8     ,       help='Number of local SGD steps or None to disable local SGD'  )
					parser.add_argument('--cpu'     ,       action='store_true'     ,       help='If passed, will train on the CPU.'  )
					UpperCAmelCase_    :      Dict            =						parser.parse_args()
					UpperCAmelCase_    :      List[Any]            =						{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
					training_function(__snake_case     ,       __snake_case  )


if __name__ == "__main__":
		main()











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_:str             =						{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Union[str, Any]             =						[
         """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """AdaptiveEmbedding""",
         """TransfoXLForSequenceClassification""",
         """TransfoXLLMHeadModel""",
         """TransfoXLModel""",
         """TransfoXLPreTrainedModel""",
         """load_tf_weights_in_transfo_xl""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Any             =						[
         """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFAdaptiveEmbedding""",
         """TFTransfoXLForSequenceClassification""",
         """TFTransfoXLLMHeadModel""",
         """TFTransfoXLMainLayer""",
         """TFTransfoXLModel""",
         """TFTransfoXLPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
     from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_transfo_xl import (
              TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
              AdaptiveEmbedding,
              TransfoXLForSequenceClassification,
              TransfoXLLMHeadModel,
              TransfoXLModel,
              TransfoXLPreTrainedModel,
              load_tf_weights_in_transfo_xl,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_transfo_xl import (
              TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFAdaptiveEmbedding,
              TFTransfoXLForSequenceClassification,
              TFTransfoXLLMHeadModel,
              TFTransfoXLMainLayer,
              TFTransfoXLModel,
              TFTransfoXLPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_:Dict             =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


