


import argparse

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


A       :		Optional[int]    =   1_6
A       :		Tuple    =   3_2






def  UpperCamelCase					(	__magic_name__     :							Accelerator    ,    __magic_name__     :							int = 16     )   ->  Union[str, Any]:
		"""simple docstring"""




		lowercase__     			=		AutoTokenizer.from_pretrained("""bert-base-cased"""     )
		lowercase__     			=		load_dataset("""glue"""    ,    """mrpc"""     )

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

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

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

		# Instantiate dataloaders.
		lowercase__     			=		DataLoader(
		    tokenized_datasets["""train"""]    ,    shuffle=__lowerCAmelCase    ,    collate_fn=__lowerCAmelCase    ,    batch_size=__lowerCAmelCase    ,    drop_last=__lowerCAmelCase     )
		lowercase__     			=		DataLoader(
		    tokenized_datasets["""validation"""]    ,    shuffle=__lowerCAmelCase    ,    collate_fn=__lowerCAmelCase    ,    batch_size=__lowerCAmelCase    ,    drop_last=(accelerator.mixed_precision == """fp8""")    ,    )

		return train_dataloader, eval_dataloader






def  UpperCamelCase					(	__magic_name__     :							Optional[int]    ,    __magic_name__     :							Union[str, Any]     )   ->  Any:
		"""simple docstring"""




		lowercase__     			=		Accelerator(cpu=args.cpu    ,    mixed_precision=args.mixed_precision     )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowercase__     			=		config["""lr"""]
		lowercase__     			=		int(config["""num_epochs"""]     )
		lowercase__     			=		int(config["""seed"""]     )
		lowercase__     			=		int(config["""batch_size"""]     )

		lowercase__     			=		evaluate.load("""glue"""    ,    """mrpc"""     )

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

		set_seed(__lowerCAmelCase     )
		lowercase__			,	lowercase__     			=		get_dataloaders(__lowerCAmelCase    ,    __lowerCAmelCase     )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowercase__     			=		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).
		lowercase__     			=		model.to(accelerator.device     )
		# Instantiate optimizer
		lowercase__     			=		AdamW(params=model.parameters()    ,    lr=__lowerCAmelCase     )

		# Instantiate scheduler
		lowercase__     			=		get_linear_schedule_with_warmup(
		    optimizer=__lowerCAmelCase    ,    num_warmup_steps=100    ,    num_training_steps=(len(__lowerCAmelCase     ) * 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.

		lowercase__			,	lowercase__			,	lowercase__			,	lowercase__			,	lowercase__     			=		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     ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device     )
						lowercase__     			=		model(**__lowerCAmelCase     )
						lowercase__     			=		outputs.loss
						lowercase__     			=		loss / gradient_accumulation_steps
						accelerator.backward(__lowerCAmelCase     )
						if step % gradient_accumulation_steps == 0:
								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():
								lowercase__     			=		model(**__lowerCAmelCase     )
						lowercase__     			=		outputs.logits.argmax(dim=-1     )
						lowercase__			,	lowercase__     			=		accelerator.gather_for_metrics((predictions, batch["""labels"""])     )
						metric.add_batch(
						    predictions=__lowerCAmelCase    ,    references=__lowerCAmelCase    ,    )

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






def  UpperCamelCase					(	)   ->  Any:
		"""simple docstring"""




		lowercase__     			=		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."""     )
		lowercase__     			=		parser.parse_args()
		lowercase__     			=		{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
		training_function(__lowerCAmelCase    ,    __lowerCAmelCase     )


if __name__ == "__main__":
	main()



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
					from .pipeline_unclip import UnCLIPPipeline
					from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
					from .text_proj import UnCLIPTextProjModel



