



'''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 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
#
########################################################################

_UpperCamelCase  						=			16
_UpperCamelCase  						=			32
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Accelerator					, UpperCAmelCase_      :	int = 16						) ->							Optional[int]:
							lowerCAmelCase__  =   AutoTokenizer.from_pretrained("""bert-base-cased"""						)
							lowerCAmelCase__  =   load_dataset("""glue"""					, """mrpc"""						)

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

							def collate_fn(UpperCAmelCase_      :	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(
														    UpperCAmelCase_					, padding="""longest"""					, max_length=UpperCAmelCase_					, pad_to_multiple_of=UpperCAmelCase_					, return_tensors="""pt"""					, )

							# Instantiate dataloaders.
							lowerCAmelCase__  =   DataLoader(
							    tokenized_datasets["""train"""]					, shuffle=UpperCAmelCase_					, collate_fn=UpperCAmelCase_					, batch_size=UpperCAmelCase_						)
							lowerCAmelCase__  =   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

		_UpperCamelCase  						=			mocked_dataloaders  # noqa: F811
def 			_lowerCAmelCase(      UpperCAmelCase_      :	List[str]					, UpperCAmelCase_      :	int						) ->							Dict:
							# For testing only
							if os.environ.get("""TESTING_MOCKED_DATALOADERS"""					, UpperCAmelCase_						) == "1":
														lowerCAmelCase__  =   2
							# Initialize accelerator
							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"""]						)

							# 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":
																					lowerCAmelCase__  =   args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					lowerCAmelCase__  =   int(args.checkpointing_steps						)
														else:
																					raise ValueError(
																					    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''						)
							else:
														lowerCAmelCase__  =   None

							set_seed(UpperCAmelCase_						)

							lowerCAmelCase__  ,lowerCAmelCase__  =   get_dataloaders(UpperCAmelCase_					, UpperCAmelCase_						)
							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=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).
							lowerCAmelCase__  =   model.to(accelerator.device						)

							# Instantiate optimizer
							lowerCAmelCase__  =   AdamW(params=model.parameters()					, lr=UpperCAmelCase_						)

							# Instantiate scheduler
							lowerCAmelCase__  =   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.
							lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  =   accelerator.prepare(
							    UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_						)

							# New Code #
							# We need to keep track of how many total steps we have iterated over
							lowerCAmelCase__  =   0
							# We also need to keep track of the stating epoch so files are named properly
							lowerCAmelCase__  =   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						)
																					lowerCAmelCase__  =   os.path.basename(args.resume_from_checkpoint						)
														else:
																					# Get the most recent checkpoint
																					lowerCAmelCase__  =   [f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
																					dirs.sort(key=os.path.getctime						)
																					lowerCAmelCase__  =   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														lowerCAmelCase__  =   os.path.splitext(UpperCAmelCase_						)[0]

														if "epoch" in training_difference:
																					lowerCAmelCase__  =   int(training_difference.replace("""epoch_"""					, """"""						)						) + 1
																					lowerCAmelCase__  =   None
														else:
																					lowerCAmelCase__  =   int(training_difference.replace("""step_"""					, """"""						)						)
																					lowerCAmelCase__  =   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
																					lowerCAmelCase__  =   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
																					lowerCAmelCase__  =   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						)
																					lowerCAmelCase__  =   model(**UpperCAmelCase_						)
																					lowerCAmelCase__  =   outputs.loss
																					lowerCAmelCase__  =   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_						):
																												lowerCAmelCase__  =   F'''step_{overall_step}'''
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										lowerCAmelCase__  =   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():
																												lowerCAmelCase__  =   model(**UpperCAmelCase_						)
																					lowerCAmelCase__  =   outputs.logits.argmax(dim=-1						)
																					lowerCAmelCase__  ,lowerCAmelCase__  =   accelerator.gather_for_metrics((predictions, batch["""labels"""])						)
																					metric.add_batch(
																					    predictions=UpperCAmelCase_					, references=UpperCAmelCase_					, )

														lowerCAmelCase__  =   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":
																					lowerCAmelCase__  =   F'''epoch_{epoch}'''
																					if args.output_dir is not None:
																												lowerCAmelCase__  =   os.path.join(args.output_dir					, UpperCAmelCase_						)
																					accelerator.save_state(UpperCAmelCase_						)
def 			_lowerCAmelCase(      ) ->							Any:
							lowerCAmelCase__  =   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."""					, )
							lowerCAmelCase__  =   parser.parse_args()
							lowerCAmelCase__  =   {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(UpperCAmelCase_					, UpperCAmelCase_						)


if __name__ == "__main__":
		main()










'''simple docstring'''







def 			_lowerCAmelCase(      UpperCAmelCase_      :	list					, UpperCAmelCase_      :	int					, UpperCAmelCase_      :	int					, UpperCAmelCase_      :	int						) ->							int:
							lowerCAmelCase__  =   int((lower_bound + upper_bound) // 2						)
							if array[r] == value:
														return r
							if lower_bound >= upper_bound:
														return -1
							if array[r] < value:
														return binary_search(UpperCAmelCase_					, r + 1					, UpperCAmelCase_					, UpperCAmelCase_						)
							else:
														return binary_search(UpperCAmelCase_					, UpperCAmelCase_					, r - 1					, UpperCAmelCase_						)
def 			_lowerCAmelCase(      UpperCAmelCase_      :	int					, UpperCAmelCase_      :	list						) ->							list:
							lowerCAmelCase__  =   0
							if matrix[index][0] == value:
														return [index, 0]
							while index < len(UpperCAmelCase_						) and matrix[index][0] < value:
														lowerCAmelCase__  =   binary_search(matrix[index]					, 0					, len(matrix[index]						) - 1					, UpperCAmelCase_						)
														if r != -1:
																					return [index, r]
														index += 1
							return [-1, -1]


if __name__ == "__main__":
		import doctest

		doctest.testmod()





