import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# 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
#
########################################################################

lowerCamelCase_				:		str       						=       32
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase = 16						):
					__a							=   AutoTokenizer.from_pretrained('bert-base-cased'						)
					__a							=   load_dataset('glue'      ,				'mrpc'						)

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

					def collate_fn(__lowerCamelCase						):
										# 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(
										    __lowerCamelCase      ,				padding='longest'      ,				pad_to_multiple_of=__lowerCamelCase      ,				return_tensors='pt'      ,				)

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

	lowerCamelCase_				:		Optional[Any]       						=       mocked_dataloaders  # noqa: F811
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					# For testing only
					if os.environ.get('TESTING_MOCKED_DATALOADERS'      ,				__lowerCamelCase						) == "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']						)

					__a							=   evaluate.load('glue'      ,				'mrpc'						)

					# New Code #
					# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
					# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
					# half each time. From this, we can calculate the number of gradient accumulation steps needed
					# and modify the Accelerator object as a result
					@find_executable_batch_size(starting_batch_size=int(__lowerCamelCase						)						)
					def inner_training_loop(__lowerCamelCase						):
										# Since we need to modify the outside accelerator object, we need to bring it
										# to the local scope
										nonlocal accelerator

										# We can calculate the number of gradient accumulation steps based on the current
										# batch size vs the starting batch size
										__a							=   observed_batch_size // batch_size

										# And then set it in the Accelerator directly:
										__a							=   num_gradient_accumulation_steps

										# Next we need to free all of the stored model references in the Accelerator each time
										accelerator.free_memory()

										# And set the seed so our results are reproducable each reset
										set_seed(__lowerCamelCase						)

										# 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=__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).
										__a							=   model.to(accelerator.device						)

										# Instantiate optimizer
										__a							=   AdamW(params=model.parameters()      ,				lr=__lowerCamelCase						)
										__a					,      __a							=   get_dataloaders(__lowerCamelCase      ,				__lowerCamelCase						)

										# Instantiate scheduler
										__a							=   get_linear_schedule_with_warmup(
										    optimizer=__lowerCamelCase      ,				num_warmup_steps=100      ,				num_training_steps=(len(__lowerCamelCase						) * num_epochs)      ,				)

										# 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(
										    __lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						)

										# Now we train the model
										for epoch in range(__lowerCamelCase						):
															model.train()
															for step, batch in enumerate(__lowerCamelCase						):
																				# And perform gradient accumulation
																				with accelerator.accumulate(__lowerCamelCase						):
																									# We could avoid this line since we set the accelerator with `device_placement=True`.
																									batch.to(accelerator.device						)
																									__a							=   model(**__lowerCamelCase						)
																									__a							=   outputs.loss
																									accelerator.backward(__lowerCamelCase						)
																									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`.
																				batch.to(accelerator.device						)
																				with torch.no_grad():
																									__a							=   model(**__lowerCamelCase						)
																				__a							=   outputs.logits.argmax(dim=-1						)
																				__a					,      __a							=   accelerator.gather_for_metrics((predictions, batch['labels'])						)
																				metric.add_batch(
																				    predictions=__lowerCamelCase      ,				references=__lowerCamelCase      ,				)

															__a							=   metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(f'''epoch {epoch}:'''      ,				__lowerCamelCase						)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
					inner_training_loop()
def 							lowerCAmelCase(	):
					__a							=   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.'						)
					__a							=   parser.parse_args()
					# New Code #
					# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
					__a							=   {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 256}
					training_function(__lowerCamelCase      ,				__lowerCamelCase						)


if __name__ == "__main__":
	main()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_				:		int       						=       {
    """configuration_fsmt""": ["""FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FSMTConfig"""],
    """tokenization_fsmt""": ["""FSMTTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_				:		Dict       						=       ["""FSMTForConditionalGeneration""", """FSMTModel""", """PretrainedFSMTModel"""]


if TYPE_CHECKING:
	from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
	from .tokenization_fsmt import FSMTTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
	import sys

	lowerCamelCase_				:		Dict       						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




