

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
# and perform 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)
#
# 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__								=		1_6
lowerCAmelCase__								=		3_2






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Accelerator							,       UpperCAmelCase_							:     int = 1_6	)						->				Union[str, Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[int]			=						AutoTokenizer.from_pretrained('bert-base-cased'	)
	_UpperCamelCase   :		Tuple			=						load_dataset('glue'							,       'mrpc'	)

	def tokenize_function(UpperCAmelCase_							:     Optional[int]	):
		# max_length=None => use the model max length (it's actually the default)
		_UpperCamelCase   :		List[Any]			=						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   :		Any			=						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   :		Union[str, Any]			=						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.
		_UpperCamelCase   :		Any			=						1_2_8 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   :		Union[str, Any]			=						1_6
		elif accelerator.mixed_precision != "no":
			_UpperCamelCase   :		Dict			=						8
		else:
			_UpperCamelCase   :		Optional[Any]			=						None

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

	# Instantiate dataloaders.
	_UpperCamelCase   :		int			=						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

							lowerCAmelCase__								=		mocked_dataloaders  # noqa: F811






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     Union[str, Any]	)						->				str:




	'''simple docstring'''




	if os.environ.get('TESTING_MOCKED_DATALOADERS'							,       UpperCAmelCase_	) == "1":
		_UpperCamelCase   :		int			=						2
	# New Code #
	_UpperCamelCase   :		Any			=						int(args.gradient_accumulation_steps	)
	# Initialize accelerator
	_UpperCamelCase   :		List[Any]			=						Accelerator(
	    cpu=args.cpu							,       mixed_precision=args.mixed_precision							,       gradient_accumulation_steps=UpperCAmelCase_	)
	if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
		raise NotImplementedError(
		    'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'	)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	_UpperCamelCase   :		List[str]			=						config['lr']
	_UpperCamelCase   :		str			=						int(config['num_epochs']	)
	_UpperCamelCase   :		Optional[Any]			=						int(config['seed']	)
	_UpperCamelCase   :		List[Any]			=						int(config['batch_size']	)

	_UpperCamelCase   :		str			=						evaluate.load('glue'							,       'mrpc'	)

	set_seed(UpperCAmelCase_	)
	_UpperCamelCase	,		_UpperCamelCase   :		Optional[int]			=						get_dataloaders(UpperCAmelCase_							,       UpperCAmelCase_	)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	_UpperCamelCase   :		Tuple			=						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   :		Optional[int]			=						model.to(accelerator.device	)

	# Instantiate optimizer
	_UpperCamelCase   :		Optional[Any]			=						AdamW(params=model.parameters()							,       lr=UpperCAmelCase_	)

	# Instantiate scheduler
	_UpperCamelCase   :		Tuple			=						get_linear_schedule_with_warmup(
	    optimizer=UpperCAmelCase_							,       num_warmup_steps=1_0_0							,       num_training_steps=(len(UpperCAmelCase_	) * 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.
	_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		Tuple			=						accelerator.prepare(
	    UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)

	# Now we train the model
	for epoch in range(UpperCAmelCase_	):
		model.train()
		for step, batch in enumerate(UpperCAmelCase_	):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device	)
			# New code #
			# We use the new `accumulate` context manager to perform gradient accumulation
			# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
			with accelerator.accumulate(UpperCAmelCase_	):
				_UpperCamelCase   :		Any			=						model(**UpperCAmelCase_	)
				_UpperCamelCase   :		List[Any]			=						output.loss
				accelerator.backward(UpperCAmelCase_	)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		for step, batch in enumerate(UpperCAmelCase_	):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device	)
			with torch.no_grad():
				_UpperCamelCase   :		Dict			=						model(**UpperCAmelCase_	)
			_UpperCamelCase   :		Tuple			=						outputs.logits.argmax(dim=-1	)
			_UpperCamelCase	,		_UpperCamelCase   :		int			=						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_	)






def 					lowerCamelCase_     (							)						->				Tuple:




	'''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.'							,       )
	# New Code #
	parser.add_argument(
	    '--gradient_accumulation_steps'							,       type=UpperCAmelCase_							,       default=1							,       help='The number of minibatches to be ran before gradients are accumulated.'							,       )
	parser.add_argument('--cpu'							,       action='store_true'							,       help='If passed, will train on the CPU.'	)
	_UpperCamelCase   :		Dict			=						parser.parse_args()
	_UpperCamelCase   :		Any			=						{'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
	training_function(UpperCAmelCase_							,       UpperCAmelCase_	)


if __name__ == "__main__":
							main()







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase__								=		{
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		["""BlipImageProcessor"""]


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """BlipModel""",
							    """BlipPreTrainedModel""",
							    """BlipForConditionalGeneration""",
							    """BlipForQuestionAnswering""",
							    """BlipVisionModel""",
							    """BlipTextModel""",
							    """BlipForImageTextRetrieval""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFBlipModel""",
							    """TFBlipPreTrainedModel""",
							    """TFBlipForConditionalGeneration""",
							    """TFBlipForQuestionAnswering""",
							    """TFBlipVisionModel""",
							    """TFBlipTextModel""",
							    """TFBlipForImageTextRetrieval""",
							]

if TYPE_CHECKING:
							from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
							from .processing_blip import BlipProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_blip import BlipImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blip import (
														    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlipForConditionalGeneration,
														    BlipForImageTextRetrieval,
														    BlipForQuestionAnswering,
														    BlipModel,
														    BlipPreTrainedModel,
														    BlipTextModel,
														    BlipVisionModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blip import (
														    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFBlipForConditionalGeneration,
														    TFBlipForImageTextRetrieval,
														    TFBlipForQuestionAnswering,
														    TFBlipModel,
														    TFBlipPreTrainedModel,
														    TFBlipTextModel,
														    TFBlipVisionModel,
														)

else:
							import sys

							lowerCAmelCase__								=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




