


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

lowercase_								=    16
lowercase_								=    32


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase = 16				)    ->  Optional[int]:
						_a     =			AutoTokenizer.from_pretrained('bert-base-cased'				)
						_a     =			load_dataset('glue'							,     'mrpc'				)

						def tokenize_function(_UpperCAmelCase				):
												# max_length=None => use the model max length (it's actually the default)
												_a     =			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():
												_a     =			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
						_a     =			tokenized_datasets.rename_column('label'							,     'labels'				)

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

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

						# Instantiate dataloaders.
						_a     =			DataLoader(
						    tokenized_datasets['train']							,     shuffle=_UpperCAmelCase							,     collate_fn=_UpperCAmelCase							,     batch_size=_UpperCAmelCase				)
						_a     =			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

		lowercase_								=    mocked_dataloaders  # noqa: F811


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase				)    ->  Union[str, Any]:
						# For testing only
						if os.environ.get('TESTING_MOCKED_DATALOADERS'							,     _UpperCAmelCase				) == "1":
												_a     =			2
						# Initialize accelerator
						_a     =			Accelerator(cpu=args.cpu							,     mixed_precision=args.mixed_precision				)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_a     =			config['lr']
						_a     =			int(config['num_epochs']				)
						_a     =			int(config['seed']				)
						_a     =			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":
																		_a     =			args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		_a     =			int(args.checkpointing_steps				)
												else:
																		raise ValueError(
																		    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""				)
						else:
												_a     =			None

						set_seed(_UpperCAmelCase				)

						_a ,	_a     =			get_dataloaders(_UpperCAmelCase							,     _UpperCAmelCase				)
						_a     =			evaluate.load('glue'							,     'mrpc'				)

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

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

						# Instantiate optimizer
						_a     =			AdamW(params=model.parameters()							,     lr=_UpperCAmelCase				)

						# Instantiate scheduler
						_a     =			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.
						_a ,	_a ,	_a ,	_a ,	_a     =			accelerator.prepare(
						    _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)

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

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

												_a     =			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":
																		_a     =			f"""epoch_{epoch}"""
																		if args.output_dir is not None:
																								_a     =			os.path.join(args.output_dir							,     _UpperCAmelCase				)
																		accelerator.save_state(_UpperCAmelCase				)


def 							SCREAMING_SNAKE_CASE					(  )    ->  List[Any]:
						_a     =			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.'							,     )
						_a     =			parser.parse_args()
						_a     =			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(_UpperCAmelCase							,     _UpperCAmelCase				)


if __name__ == "__main__":
		main()






from typing import TYPE_CHECKING

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


lowercase_								=    {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_								=    ['CodeGenTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_								=    [
		    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'CodeGenForCausalLM',
		    'CodeGenModel',
		    'CodeGenPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
		from .tokenization_codegen import CodeGenTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_codegen_fast import CodeGenTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_codegen import (
				    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CodeGenForCausalLM,
				    CodeGenModel,
				    CodeGenPreTrainedModel,
				)

else:
		import sys

		lowercase_								=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


