


"""simple docstring"""
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
#
########################################################################


SCREAMING_SNAKE_CASE :    Optional[Any]									=				16
SCREAMING_SNAKE_CASE :    Optional[Any]									=				32






def 		lowercase							(     _snake_case						:      Accelerator     ,   _snake_case						:      int = 16     )     ->int:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				AutoTokenizer.from_pretrained('''bert-base-cased'''     )
	__snake_case	:						Any						=				load_dataset('''glue'''     ,   '''mrpc'''     )

	def tokenize_function(_snake_case						:      Dict     ):
		# max_length=None => use the model max length (it's actually the default)
		__snake_case	:						List[Any]						=				tokenizer(examples['''sentence1''']     ,   examples['''sentence2''']     ,   truncation=_snake_case     ,   max_length=_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():
		__snake_case	:						Tuple						=				datasets.map(
		    _snake_case     ,   batched=_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
	__snake_case	:						int						=				tokenized_datasets.rename_column('''label'''     ,   '''labels'''     )

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

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

	# Instantiate dataloaders.
	__snake_case	:						Union[str, Any]						=				DataLoader(
	    tokenized_datasets['''train''']     ,   shuffle=_snake_case     ,   collate_fn=_snake_case     ,   batch_size=_snake_case     )
	__snake_case	:						str						=				DataLoader(
	    tokenized_datasets['''validation''']     ,   shuffle=_snake_case     ,   collate_fn=_snake_case     ,   batch_size=_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

			SCREAMING_SNAKE_CASE :    str									=				mocked_dataloaders  # noqa: F811






def 		lowercase							(     _snake_case						:      Dict     ,   _snake_case						:      List[str]     )     ->Optional[int]:
	"""simple docstring"""





	if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,   _snake_case     ) == "1":
		__snake_case	:						List[str]						=				2
	# New Code #
	__snake_case	:						int						=				int(args.gradient_accumulation_steps     )
	# Initialize accelerator
	__snake_case	:						List[str]						=				Accelerator(
	    cpu=args.cpu     ,   mixed_precision=args.mixed_precision     ,   gradient_accumulation_steps=_snake_case     )
	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
	__snake_case	:						Any						=				config['''lr''']
	__snake_case	:						Optional[int]						=				int(config['''num_epochs''']     )
	__snake_case	:						Union[str, Any]						=				int(config['''seed''']     )
	__snake_case	:						Dict						=				int(config['''batch_size''']     )

	__snake_case	:						Any						=				evaluate.load('''glue'''     ,   '''mrpc'''     )

	set_seed(_snake_case     )
	__snake_case	,							__snake_case	:						Dict						=				get_dataloaders(_snake_case     ,   _snake_case     )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__snake_case	:						Dict						=				AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''     ,   return_dict=_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).
	__snake_case	:						Any						=				model.to(accelerator.device     )

	# Instantiate optimizer
	__snake_case	:						Any						=				AdamW(params=model.parameters()     ,   lr=_snake_case     )

	# Instantiate scheduler
	__snake_case	:						Any						=				get_linear_schedule_with_warmup(
	    optimizer=_snake_case     ,   num_warmup_steps=100     ,   num_training_steps=(len(_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.
	__snake_case	,							__snake_case	,							__snake_case	,							__snake_case	,							__snake_case	:						int						=				accelerator.prepare(
	    _snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     )

	# Now we train the model
	for epoch in range(_snake_case     ):
		model.train()
		for step, batch in enumerate(_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(_snake_case     ):
				__snake_case	:						Union[str, Any]						=				model(**_snake_case     )
				__snake_case	:						Optional[int]						=				output.loss
				accelerator.backward(_snake_case     )
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

		__snake_case	:						Any						=				metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""     ,   _snake_case     )






def 		lowercase							(     )     ->List[Any]:
	"""simple docstring"""





	__snake_case	:						Dict						=				argparse.ArgumentParser(description='''Simple example of training script.'''     )
	parser.add_argument(
	    '''--mixed_precision'''     ,   type=_snake_case     ,   default=_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=_snake_case     ,   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.'''     )
	__snake_case	:						List[str]						=				parser.parse_args()
	__snake_case	:						Union[str, Any]						=				{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
	training_function(_snake_case     ,   _snake_case     )


if __name__ == "__main__":
			main()







from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case								=  logging.get_logger(__name__)




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   List[str]				       =["""pixel_values"""]
	def __init__(			self							:   Dict       ,   UpperCAmelCase_							:   bool = True       ,   UpperCAmelCase_							:   int = 32       ,   UpperCAmelCase_							:   int=PILImageResampling.BILINEAR       ,   UpperCAmelCase_							:   bool = True       ,   **UpperCAmelCase_							:   Any       ,   ):
						SCREAMING_SNAKE_CASE__													= do_resize
						SCREAMING_SNAKE_CASE__													= do_rescale
						SCREAMING_SNAKE_CASE__													= size_divisor
						SCREAMING_SNAKE_CASE__													= resample
						super().__init__(**UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   np.ndarray       ,   UpperCAmelCase_							:   int       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   Optional[ChannelDimension] = None       ,   **UpperCAmelCase_							:   Dict							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= get_image_size(UpperCAmelCase_							)
						# Rounds the height and width down to the closest multiple of size_divisor
						SCREAMING_SNAKE_CASE__													= height // size_divisor * size_divisor
						SCREAMING_SNAKE_CASE__													= width // size_divisor * size_divisor
						SCREAMING_SNAKE_CASE__													= resize(UpperCAmelCase_       ,   (new_h, new_w)       ,   resample=UpperCAmelCase_       ,   data_format=UpperCAmelCase_       ,   **UpperCAmelCase_							)
						return image
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   np.ndarray       ,   UpperCAmelCase_							:   float       ,   UpperCAmelCase_							:   Optional[ChannelDimension] = None       ,   **UpperCAmelCase_							:   List[str]							):
						return rescale(image=UpperCAmelCase_       ,   scale=UpperCAmelCase_       ,   data_format=UpperCAmelCase_       ,   **UpperCAmelCase_							)





	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,   UpperCAmelCase_							:   Optional[bool] = None       ,   UpperCAmelCase_							:   Optional[int] = None       ,   UpperCAmelCase_							:   List[str]=None       ,   UpperCAmelCase_							:   Optional[bool] = None       ,   UpperCAmelCase_							:   Optional[Union[TensorType, str]] = None       ,   UpperCAmelCase_							:   ChannelDimension = ChannelDimension.FIRST       ,   **UpperCAmelCase_							:   List[Any]       ,   ):
						SCREAMING_SNAKE_CASE__													= do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE__													= do_rescale if do_rescale is not None else self.do_rescale
						SCREAMING_SNAKE_CASE__													= size_divisor if size_divisor is not None else self.size_divisor
						SCREAMING_SNAKE_CASE__													= resample if resample is not None else self.resample

						if do_resize and size_divisor is None:
											raise ValueError('size_divisor is required for resizing'							)

						SCREAMING_SNAKE_CASE__													= make_list_of_images(UpperCAmelCase_							)

						if not valid_images(UpperCAmelCase_							):
											raise ValueError('Invalid image(s)'							)

						# All transformations expect numpy arrays.
						SCREAMING_SNAKE_CASE__													= [to_numpy_array(UpperCAmelCase_							) for img in images]

						if do_resize:
											SCREAMING_SNAKE_CASE__													= [self.resize(UpperCAmelCase_       ,   size_divisor=UpperCAmelCase_       ,   resample=UpperCAmelCase_							) for image in images]

						if do_rescale:
											SCREAMING_SNAKE_CASE__													= [self.rescale(UpperCAmelCase_       ,   scale=1 / 255							) for image in images]

						SCREAMING_SNAKE_CASE__													= [to_channel_dimension_format(UpperCAmelCase_       ,   UpperCAmelCase_							) for image in images]

						SCREAMING_SNAKE_CASE__													= {'pixel_values': images}
						return BatchFeature(data=UpperCAmelCase_       ,   tensor_type=UpperCAmelCase_							)





