


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   :    Union[str, Any]     =		16
UpperCAmelCase   :    Tuple     =		32






def 	__lowerCamelCase						(							lowerCamelCase__		:						Accelerator  ,							lowerCamelCase__		:						int = 16      ):





			'''simple docstring'''




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

			def tokenize_function(lowerCamelCase__		:						List[str]      ):
						# max_length=None => use the model max length (it's actually the default)
						lowerCamelCase    		=  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():
						lowerCamelCase    		=  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
			lowerCamelCase    		=  tokenized_datasets.rename_column("""label"""  ,							"""labels"""      )

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

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

			# Instantiate dataloaders.
			lowerCamelCase    		=  DataLoader(
			    tokenized_datasets["""train"""]  ,							shuffle=lowerCamelCase__  ,							collate_fn=lowerCamelCase__  ,							batch_size=lowerCamelCase__      )
			lowerCamelCase    		=  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 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						Optional[int]      ):





			'''simple docstring'''




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

			lowerCamelCase		,		lowerCamelCase    		=  get_dataloaders(lowerCamelCase__  ,							lowerCamelCase__      )
			lowerCamelCase    		=  evaluate.load("""glue"""  ,							"""mrpc"""      )

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

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

			# Instantiate optimizer
			lowerCamelCase    		=  AdamW(params=model.parameters()  ,							lr=lowerCamelCase__      )

			# Instantiate scheduler
			lowerCamelCase    		=  get_linear_schedule_with_warmup(
			    optimizer=lowerCamelCase__  ,							num_warmup_steps=100  ,							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.
			lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase    		=  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:
						lowerCamelCase    		=  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:
									lowerCamelCase    		=  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      )
									lowerCamelCase    		=  model(**lowerCamelCase__      )
									lowerCamelCase    		=  outputs.loss
									# New Code #
									if args.with_tracking:
												total_loss += loss.detach().float()
									lowerCamelCase    		=  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():
												lowerCamelCase    		=  model(**lowerCamelCase__      )
									lowerCamelCase    		=  outputs.logits.argmax(dim=-1      )
									lowerCamelCase		,		lowerCamelCase    		=  accelerator.gather_for_metrics((predictions, batch["""labels"""])      )
									metric.add_batch(
									    predictions=lowerCamelCase__  ,							references=lowerCamelCase__  ,							)

						lowerCamelCase    		=  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 	__lowerCamelCase						(							):





			'''simple docstring'''




			lowerCamelCase    		=  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"""  ,							)
			lowerCamelCase    		=  parser.parse_args()
			lowerCamelCase    		=  {"""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_tokenizers_available, is_torch_available


UpperCAmelCase   :    Any     =		{
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    Union[str, Any]     =		[
		    "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RoCBertForCausalLM",
		    "RoCBertForMaskedLM",
		    "RoCBertForMultipleChoice",
		    "RoCBertForPreTraining",
		    "RoCBertForQuestionAnswering",
		    "RoCBertForSequenceClassification",
		    "RoCBertForTokenClassification",
		    "RoCBertLayer",
		    "RoCBertModel",
		    "RoCBertPreTrainedModel",
		    "load_tf_weights_in_roc_bert",
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		UpperCAmelCase   :    Tuple     =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

