



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
#
########################################################################

_lowercase		:				str      		=16
_lowercase		:				int      		=32

def      lowerCAmelCase_					(  _lowercase    :       Union[str, Any]						,       _lowercase    :       Tuple = 16)							->	List[Any]:







			"""simple docstring"""
			a__       :						Any							=  AutoTokenizer.from_pretrained("""bert-base-cased""")
			a__       :						str							=  load_dataset("""glue"""						,       """mrpc""")

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

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

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

			# Instantiate dataloaders.
			a__       :						Any							=  DataLoader(
			    tokenized_datasets["""train"""]						,       shuffle=a__						,       collate_fn=a__						,       batch_size=a__)
			a__       :						Optional[int]							=  DataLoader(
			    tokenized_datasets["""validation"""]						,       shuffle=a__						,       collate_fn=a__						,       batch_size=a__)

			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

		_lowercase		:				int      		=mocked_dataloaders  # noqa: F811

def      lowerCAmelCase_					(  _lowercase    :       Dict						,       _lowercase    :       str)							->	int:







			"""simple docstring"""

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

			a__	,						a__       :						Union[str, Any]							=  get_dataloaders(a__						,       a__)
			a__       :						Optional[Any]							=  evaluate.load("""glue"""						,       """mrpc""")

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

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

			# 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__       :						str							=  model.to(accelerator.device)

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

			# Instantiate scheduler
			a__       :						str							=  get_linear_schedule_with_warmup(
			    optimizer=a__						,       num_warmup_steps=100						,       num_training_steps=(len(a__) * 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__       :						Tuple							=  accelerator.prepare(
			    a__						,       a__						,       a__						,       a__						,       a__)

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

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

						model.eval()
						for step, batch in enumerate(a__):
									# 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__       :						Dict							=  model(**a__)
									a__       :						Optional[int]							=  outputs.logits.argmax(dim=-1)
									a__	,						a__       :						Any							=  accelerator.gather_for_metrics((predictions, batch["""labels"""]))
									metric.add_batch(
									    predictions=a__						,       references=a__						,       )

						a__       :						List[Any]							=  metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'''epoch {epoch}:'''						,       a__)

						# 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(a__),
									        """epoch""": epoch,
									    }						,       step=a__						,       )

						# 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_					(  )							->	Any:







			"""simple docstring"""
			a__       :						Any							=  argparse.ArgumentParser(description="""Simple example of training script.""")
			parser.add_argument(
			    """--mixed_precision"""						,       type=a__						,       default=a__						,       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=a__						,       default="""logs"""						,       help="""Location on where to store experiment tracking logs` and relevent project information"""						,       )
			a__       :						List[str]							=  parser.parse_args()
			a__       :						int							=  {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(a__						,       a__)


if __name__ == "__main__":
		main()











_lowercase		:				Optional[int]      		=[
    (1000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]

def      lowerCAmelCase_					(  _lowercase    :       str)							->	int:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
			a__       :						List[Any]							=  0
			a__       :						Dict							=  0
			while place < len(_lowercase):
						if (place + 1 < len(_lowercase)) and (vals[roman[place]] < vals[roman[place + 1]]):
									total += vals[roman[place + 1]] - vals[roman[place]]
									place += 2
						else:
									total += vals[roman[place]]
									place += 1
			return total

def      lowerCAmelCase_					(  _lowercase    :       int)							->	str:







			"""simple docstring"""
			a__       :						Optional[Any]							=  []
			for arabic, roman in ROMAN:
						((a__)	,						(a__))       :						Optional[Any]							=  divmod(_lowercase						,       _lowercase)
						result.append(roman * factor)
						if number == 0:
									break
			return "".join(_lowercase)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




