
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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


UpperCAmelCase       =					16
UpperCAmelCase       =					32






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE = 16				):
	lowercase	    =       AutoTokenizer.from_pretrained('bert-base-cased'				)
	lowercase	    =       load_dataset('glue'					,				'mrpc'				)

	def tokenize_function(__SCREAMING_SNAKE_CASE				):
		# max_length=None => use the model max length (it's actually the default)
		lowercase	    =       tokenizer(examples['sentence1']					,				examples['sentence2']					,				truncation=__SCREAMING_SNAKE_CASE					,				max_length=__SCREAMING_SNAKE_CASE				)
		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(
		    __SCREAMING_SNAKE_CASE					,				batched=__SCREAMING_SNAKE_CASE					,				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(__SCREAMING_SNAKE_CASE				):
		# 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(
		    __SCREAMING_SNAKE_CASE					,				padding='longest'					,				max_length=__SCREAMING_SNAKE_CASE					,				pad_to_multiple_of=__SCREAMING_SNAKE_CASE					,				return_tensors='pt'					,				)

	# Instantiate dataloaders.
	lowercase	    =       DataLoader(
	    tokenized_datasets['train']					,				shuffle=__SCREAMING_SNAKE_CASE					,				collate_fn=__SCREAMING_SNAKE_CASE					,				batch_size=__SCREAMING_SNAKE_CASE				)
	lowercase	    =       DataLoader(
	    tokenized_datasets['validation']					,				shuffle=__SCREAMING_SNAKE_CASE					,				collate_fn=__SCREAMING_SNAKE_CASE					,				batch_size=__SCREAMING_SNAKE_CASE				)

	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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	# For testing only
	if os.environ.get('TESTING_MOCKED_DATALOADERS'					,				__SCREAMING_SNAKE_CASE				) == "1":
		lowercase	    =       2
	# New Code #
	lowercase	    =       int(args.gradient_accumulation_steps				)
	lowercase	    =       int(args.local_sgd_steps				)
	# Initialize accelerator
	lowercase	    =       Accelerator(
	    cpu=args.cpu					,				mixed_precision=args.mixed_precision					,				gradient_accumulation_steps=__SCREAMING_SNAKE_CASE				)
	if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
		raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'				)
	# 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'				)

	set_seed(__SCREAMING_SNAKE_CASE				)
	lowercase    ,   lowercase	    =       get_dataloaders(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
	# 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=__SCREAMING_SNAKE_CASE				)

	# 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=__SCREAMING_SNAKE_CASE				)

	# Instantiate scheduler
	lowercase	    =       get_linear_schedule_with_warmup(
	    optimizer=__SCREAMING_SNAKE_CASE					,				num_warmup_steps=100					,				num_training_steps=(len(__SCREAMING_SNAKE_CASE				) * 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.
	lowercase    ,   lowercase    ,   lowercase    ,   lowercase    ,   lowercase	    =       accelerator.prepare(
	    __SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)

	# Now we train the model
	for epoch in range(__SCREAMING_SNAKE_CASE				):
		model.train()
		with LocalSGD(
		    accelerator=__SCREAMING_SNAKE_CASE					,				model=__SCREAMING_SNAKE_CASE					,				local_sgd_steps=__SCREAMING_SNAKE_CASE					,				enabled=local_sgd_steps is not None				) as local_sgd:
			for step, batch in enumerate(__SCREAMING_SNAKE_CASE				):
				# 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(__SCREAMING_SNAKE_CASE				):
					lowercase	    =       model(**__SCREAMING_SNAKE_CASE				)
					lowercase	    =       output.loss
					accelerator.backward(__SCREAMING_SNAKE_CASE				)
					optimizer.step()
					lr_scheduler.step()
					optimizer.zero_grad()
					# LocalSGD-specific line
					local_sgd.step()

		model.eval()
		for step, batch in enumerate(__SCREAMING_SNAKE_CASE				):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device				)
			with torch.no_grad():
				lowercase	    =       model(**__SCREAMING_SNAKE_CASE				)
			lowercase	    =       outputs.logits.argmax(dim=-1				)
			lowercase    ,   lowercase	    =       accelerator.gather_for_metrics((predictions, batch['labels'])				)
			metric.add_batch(
			    predictions=__SCREAMING_SNAKE_CASE					,				references=__SCREAMING_SNAKE_CASE					,				)

		lowercase	    =       metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'''epoch {epoch}:'''					,				__SCREAMING_SNAKE_CASE				)






def 				UpperCAmelCase_						(				):
	lowercase	    =       argparse.ArgumentParser(description='Simple example of training script.'				)
	parser.add_argument(
	    '--mixed_precision'					,				type=__SCREAMING_SNAKE_CASE					,				default=__SCREAMING_SNAKE_CASE					,				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=__SCREAMING_SNAKE_CASE					,				default=1					,				help='The number of minibatches to be ran before gradients are accumulated.'					,				)
	parser.add_argument(
	    '--local_sgd_steps'					,				type=__SCREAMING_SNAKE_CASE					,				default=8					,				help='Number of local SGD steps or None to disable local SGD'				)
	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(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
	main()







'''simple docstring'''
from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
							from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
							from ..tf_utils import stable_softmax

_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase				)
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					def __init__(	self     :						int      ,       **_lowercase     :						List[Any])    ->							Union[str, Any]:
								super().__init__(**_lowercase)

								requires_backends(self      ,       'vision')
								self.check_model_type(
								    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
								    if self.framework == 'tf'
								    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING)



					def __call__(	self     :						Optional[Any]      ,       _lowercase     :						Union[str, List[str], "Image", List["Image"]]      ,       **_lowercase     :						List[Any])    ->							Any:
								return super().__call__(_lowercase      ,       **_lowercase)



					def     __snake_case						(	self     :						int      ,       **_lowercase     :						Union[str, Any])    ->							Any:
								A_									= {}
								if "candidate_labels" in kwargs:
											A_									= kwargs['candidate_labels']
								if "hypothesis_template" in kwargs:
											A_									= kwargs['hypothesis_template']

								return preprocess_params, {}, {}



					def     __snake_case						(	self     :						List[str]      ,       _lowercase     :						Tuple      ,       _lowercase     :						Any=None      ,       _lowercase     :						Optional[int]="This is a photo of {}.")    ->							Union[str, Any]:
								A_									= load_image(_lowercase)
								A_									= self.image_processor(images=[image]      ,       return_tensors=self.framework)
								A_									= candidate_labels
								A_									= [hypothesis_template.format(_lowercase) for x in candidate_labels]
								A_									= self.tokenizer(_lowercase      ,       return_tensors=self.framework      ,       padding=_lowercase)
								A_									= [text_inputs]
								return inputs



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Tuple)    ->							Optional[int]:
								A_									= model_inputs.pop('candidate_labels')
								A_									= model_inputs.pop('text_inputs')
								if isinstance(text_inputs[0]      ,       _lowercase):
											A_									= text_inputs[0]
								else:
											# Batching case.
											A_									= text_inputs[0][0]

								A_									= self.model(**_lowercase      ,       **_lowercase)

								A_									= {
								    'candidate_labels': candidate_labels,
								    'logits': outputs.logits_per_image,
								}
								return model_outputs



					def     __snake_case						(	self     :						List[str]      ,       _lowercase     :						int)    ->							Optional[int]:
								A_									= model_outputs.pop('candidate_labels')
								A_									= model_outputs['logits'][0]
								if self.framework == "pt":
											A_									= logits.softmax(dim=-1).squeeze(-1)
											A_									= probs.tolist()
											if not isinstance(_lowercase      ,       _lowercase):
														A_									= [scores]
								elif self.framework == "tf":
											A_									= stable_softmax(_lowercase      ,       axis=-1)
											A_									= probs.numpy().tolist()
								else:
											raise ValueError(F'Unsupported framework: {self.framework}')

								A_									= [
								    {'score': score, 'label': candidate_label}
								    for score, candidate_label in sorted(zip(_lowercase      ,       _lowercase)      ,       key=lambda _lowercase: -x[0])
								]
								return result




