






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 checkpointing 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
#
########################################################################

SCREAMING_SNAKE_CASE__		:       int   							=							16
SCREAMING_SNAKE_CASE__		:       List[Any]   							=							32





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase = 16					)   -> Tuple:

					'''simple docstring'''
					UpperCAmelCase__			:     Any										= AutoTokenizer.from_pretrained("""bert-base-cased"""					)
					UpperCAmelCase__			:     List[str]										= load_dataset("""glue"""   ,       """mrpc"""					)

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

					def collate_fn(__lowerCamelCase					):
										# 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__			:     List[Any]										= 16
										elif accelerator.mixed_precision != "no":
															UpperCAmelCase__			:     Dict										= 8
										else:
															UpperCAmelCase__			:     List[Any]										= None

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

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

					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

						SCREAMING_SNAKE_CASE__		:       List[Any]   							=							mocked_dataloaders  # noqa: F811





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Any:

					'''simple docstring'''
					# For testing only
					if os.environ.get("""TESTING_MOCKED_DATALOADERS"""   ,       UpperCamelCase__					) == "1":
										UpperCAmelCase__			:     Union[str, Any]										= 2
					# Initialize accelerator
					UpperCAmelCase__			:     Dict										= Accelerator(cpu=args.cpu   ,       mixed_precision=args.mixed_precision					)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					UpperCAmelCase__			:     List[str]										= config["lr"]
					UpperCAmelCase__			:     int										= int(config["""num_epochs"""]					)
					UpperCAmelCase__			:     str										= int(config["""seed"""]					)
					UpperCAmelCase__			:     Dict										= int(config["""batch_size"""]					)

					# New Code #
					# Parse out whether we are saving every epoch or after a certain number of batches
					if hasattr(args.checkpointing_steps   ,       """isdigit"""					):
										if args.checkpointing_steps == "epoch":
															UpperCAmelCase__			:     int										= args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															UpperCAmelCase__			:     List[Any]										= int(args.checkpointing_steps					)
										else:
															raise ValueError(
															    F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."					)
					else:
										UpperCAmelCase__			:     List[str]										= None

					set_seed(UpperCamelCase__					)

					UpperCAmelCase__			:     Tuple										= get_dataloaders(UpperCamelCase__   ,       UpperCamelCase__					)
					UpperCAmelCase__			:     Any										= evaluate.load("""glue"""   ,       """mrpc"""					)

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

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCAmelCase__			:     Any										= AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""   ,       return_dict=UpperCamelCase__					)

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

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

					# Instantiate scheduler
					UpperCAmelCase__			:     List[str]										= get_linear_schedule_with_warmup(
					    optimizer=UpperCamelCase__   ,       num_warmup_steps=100   ,       num_training_steps=(len(UpperCamelCase__					) * 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.
					UpperCAmelCase__			:     Union[str, Any]										= accelerator.prepare(
					    UpperCamelCase__   ,       UpperCamelCase__   ,       UpperCamelCase__   ,       UpperCamelCase__   ,       UpperCamelCase__					)

					# New Code #
					# We need to keep track of how many total steps we have iterated over
					UpperCAmelCase__			:     Tuple										= 0
					# We also need to keep track of the stating epoch so files are named properly
					UpperCAmelCase__			:     Optional[Any]										= 0

					# We need to load the checkpoint back in before training here with `load_state`
					# The total number of epochs is adjusted based on where the state is being loaded from,
					# as we assume continuation of the same training script
					if args.resume_from_checkpoint:
										if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
															accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}"					)
															accelerator.load_state(args.resume_from_checkpoint					)
															UpperCAmelCase__			:     int										= os.path.basename(args.resume_from_checkpoint					)
										else:
															# Get the most recent checkpoint
															UpperCAmelCase__			:     List[str]										= [f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
															dirs.sort(key=os.path.getctime					)
															UpperCAmelCase__			:     str										= dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										UpperCAmelCase__			:     Optional[int]										= os.path.splitext(UpperCamelCase__					)[0]

										if "epoch" in training_difference:
															UpperCAmelCase__			:     List[Any]										= int(training_difference.replace("""epoch_"""   ,       """"""					)					) + 1
															UpperCAmelCase__			:     Dict										= None
										else:
															UpperCAmelCase__			:     Tuple										= int(training_difference.replace("""step_"""   ,       """"""					)					)
															UpperCAmelCase__			:     Any										= resume_step // len(UpperCamelCase__					)
															resume_step -= starting_epoch * len(UpperCamelCase__					)

    # Now we train the model
					for epoch in range(UpperCamelCase__   ,       UpperCamelCase__					):
										model.train()
										# New Code #
										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
															# We need to skip steps until we reach the resumed step
															UpperCAmelCase__			:     int										= accelerator.skip_first_batches(UpperCamelCase__   ,       UpperCamelCase__					)
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															UpperCAmelCase__			:     Any										= train_dataloader
										for step, batch in enumerate(UpperCamelCase__					):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device					)
															UpperCAmelCase__			:     int										= model(**UpperCamelCase__					)
															UpperCAmelCase__			:     Union[str, Any]										= outputs.loss
															UpperCAmelCase__			:     Union[str, Any]										= loss / gradient_accumulation_steps
															accelerator.backward(UpperCamelCase__					)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
															# New Code #
															overall_step += 1

															# New Code #
															# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
															# These are saved to folders named `step_{overall_step}`
															# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
															# If mixed precision was used, will also save a "scalar.bin" file
															if isinstance(UpperCamelCase__   ,       UpperCamelCase__					):
																				UpperCAmelCase__			:     Optional[Any]										= F"step_{overall_step}"
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														UpperCAmelCase__			:     Optional[Any]										= os.path.join(args.output_dir   ,       UpperCamelCase__					)
																									accelerator.save_state(UpperCamelCase__					)

										model.eval()
										for step, batch in enumerate(UpperCamelCase__					):
															# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
															batch.to(accelerator.device					)
															with torch.no_grad():
																				UpperCAmelCase__			:     int										= model(**UpperCamelCase__					)
															UpperCAmelCase__			:     Dict										= outputs.logits.argmax(dim=-1					)
															UpperCAmelCase__			:     Dict										= accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
															metric.add_batch(
															    predictions=UpperCamelCase__   ,       references=UpperCamelCase__   ,       )

										UpperCAmelCase__			:     int										= metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"epoch {epoch}:"   ,       UpperCamelCase__					)

										# New Code #
										# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
										# These are saved to folders named `epoch_{epoch}`
										# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
										# If mixed precision was used, will also save a "scalar.bin" file
										if checkpointing_steps == "epoch":
															UpperCAmelCase__			:     Any										= F"epoch_{epoch}"
															if args.output_dir is not None:
																				UpperCAmelCase__			:     Tuple										= os.path.join(args.output_dir   ,       UpperCamelCase__					)
															accelerator.save_state(UpperCamelCase__					)





def    _lowerCamelCase	(	)   -> Dict:

					'''simple docstring'''
					UpperCAmelCase__			:     Dict										= argparse.ArgumentParser(description="""Simple example of training script."""					)
					parser.add_argument(
					    """--mixed_precision"""   ,       type=UpperCamelCase__   ,       default=UpperCamelCase__   ,       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(
					    """--checkpointing_steps"""   ,       type=UpperCamelCase__   ,       default=UpperCamelCase__   ,       help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""   ,       )
					parser.add_argument(
					    """--output_dir"""   ,       type=UpperCamelCase__   ,       default="""."""   ,       help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""   ,       )
					parser.add_argument(
					    """--resume_from_checkpoint"""   ,       type=UpperCamelCase__   ,       default=UpperCamelCase__   ,       help="""If the training should continue from a checkpoint folder."""   ,       )
					UpperCAmelCase__			:     Optional[Any]										= parser.parse_args()
					UpperCAmelCase__			:     Optional[int]										= {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(UpperCamelCase__   ,       UpperCamelCase__					)


if __name__ == "__main__":
						main()














from .stack import Stack





def    _lowerCamelCase	(	__lowerCamelCase					)   -> bool:

     '''simple docstring'''
     UpperCAmelCase__			:     Stack[str]										= Stack()
     UpperCAmelCase__			:     str										= {"""(""": """)""", """[""": """]""", """{""": """}"""}
     for bracket in parentheses:
          if bracket in bracket_pairs:
               stack.push(__lowerCamelCase					)
          elif bracket in (")", "]", "}"):
               if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                    return False
     return stack.is_empty()


if __name__ == "__main__":
      from doctest import testmod

      testmod()

      SCREAMING_SNAKE_CASE__		:       Any   							=							["""((()))""", """((())""", """(()))"""]
      print("""Balanced parentheses demonstration:\n""")
      for example in examples:
            SCREAMING_SNAKE_CASE__		:       List[str]   							=							"""""" if balanced_parentheses(example) else """not """
            print(f'''{example} is {not_str}balanced''')






