





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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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
#
########################################################################


_a 						=      16
_a 						=      32





def lowerCAmelCase__(__snake_case       ,__snake_case = 16							)				->      List[str]:







	'''simple docstring'''







	lowerCamelCase__								=		AutoTokenizer.from_pretrained('''bert-base-cased'''							)
	lowerCamelCase__								=		load_dataset('''glue'''       ,'''mrpc'''							)

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

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

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

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

			_a 						=      mocked_dataloaders  # noqa: F811





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Dict:







	'''simple docstring'''







	if os.environ.get('''TESTING_MOCKED_DATALOADERS'''       ,snake_case__							) == "1":
		lowerCamelCase__								=		2
	# Initialize accelerator
	lowerCamelCase__								=		Accelerator(cpu=args.cpu       ,mixed_precision=args.mixed_precision							)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	lowerCamelCase__								=		config['''lr''']
	lowerCamelCase__								=		int(config['''num_epochs''']							)
	lowerCamelCase__								=		int(config['''seed''']							)
	lowerCamelCase__								=		int(config['''batch_size''']							)

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

	# New Code #
	# We now can define an inner training loop function. It should take a batch size as the only parameter,
	# and build the dataloaders in there.
	# It also gets our decorator
	@find_executable_batch_size(starting_batch_size=snake_case__							)
	def inner_training_loop(__snake_case							):
		# And now just move everything below under this function
		# We need to bring in the Accelerator object from earlier
		nonlocal accelerator
		# And reset all of its attributes that could hold onto any memory:
		accelerator.free_memory()

		# Then we can declare the model, optimizer, and everything else:
		set_seed(snake_case__							)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCamelCase__								=		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).
		lowerCamelCase__								=		model.to(accelerator.device							)

		# Instantiate optimizer
		lowerCamelCase__								=		AdamW(params=model.parameters()       ,lr=snake_case__							)
		lowerCamelCase__				,	lowerCamelCase__								=		get_dataloaders(snake_case__       ,snake_case__							)

		# Instantiate scheduler
		lowerCamelCase__								=		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.
		lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__								=		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							)
				lowerCamelCase__								=		model(**snake_case__							)
				lowerCamelCase__								=		outputs.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():
					lowerCamelCase__								=		model(**snake_case__							)
				lowerCamelCase__								=		outputs.logits.argmax(dim=-1							)
				lowerCamelCase__				,	lowerCamelCase__								=		accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
				metric.add_batch(
				    predictions=snake_case__       ,references=snake_case__       ,)

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

    # 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__()				->      List[str]:







	'''simple docstring'''







	lowerCamelCase__								=		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.'''       ,)
	parser.add_argument('''--cpu'''       ,action='''store_true'''       ,help='''If passed, will train on the CPU.'''							)
	lowerCamelCase__								=		parser.parse_args()
	lowerCamelCase__								=		{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
	training_function(snake_case__       ,snake_case__							)


if __name__ == "__main__":
			main()









"""simple docstring"""




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
					import torch


lowerCAmelCase							:			int     							=							logging.get_logger(__name__)






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						["pixel_values"]





				def __init__(       self		, _a = True		, _a = None		, _a = PILImageResampling.BILINEAR		, _a = True		, _a = None		, _a = True		, _a = 1 / 255		, _a = True		, _a = None		, _a = None		, **_a		, ):





							"""simple docstring"""
							super().__init__(**_a )
							lowerCamelCase			      =					size if size is not None else {"""shortest_edge""": 256}
							lowerCamelCase			      =					get_size_dict(_a		, default_to_square=_a )
							lowerCamelCase			      =					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
							lowerCamelCase			      =					get_size_dict(_a		, param_name="""crop_size""" )
							lowerCamelCase			      =					do_resize
							lowerCamelCase			      =					size
							lowerCamelCase			      =					resample
							lowerCamelCase			      =					do_center_crop
							lowerCamelCase			      =					crop_size
							lowerCamelCase			      =					do_rescale
							lowerCamelCase			      =					rescale_factor
							lowerCamelCase			      =					do_normalize
							lowerCamelCase			      =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowerCamelCase			      =					image_std if image_std is not None else IMAGENET_STANDARD_STD





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = PILImageResampling.BICUBIC		, _a = None		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					get_size_dict(_a		, default_to_square=_a )
							if "shortest_edge" not in size:
										raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
							lowerCamelCase			      =					get_resize_output_image_size(_a		, size=size["""shortest_edge"""]		, default_to_square=_a )
							return resize(_a		, size=_a		, resample=_a		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = None		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					get_size_dict(_a )
							if "height" not in size or "width" not in size:
										raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}' )
							return center_crop(_a		, size=(size["""height"""], size["""width"""])		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = None		, **_a ):





							"""simple docstring"""
							return rescale(_a		, scale=_a		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a = None		, **_a		, ):





							"""simple docstring"""
							return normalize(_a		, mean=_a		, std=_a		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = ChannelDimension.FIRST		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					do_resize if do_resize is not None else self.do_resize
							lowerCamelCase			      =					size if size is not None else self.size
							lowerCamelCase			      =					get_size_dict(_a		, default_to_square=_a )
							lowerCamelCase			      =					resample if resample is not None else self.resample
							lowerCamelCase			      =					do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCamelCase			      =					crop_size if crop_size is not None else self.crop_size
							lowerCamelCase			      =					get_size_dict(_a		, param_name="""crop_size""" )
							lowerCamelCase			      =					do_rescale if do_rescale is not None else self.do_rescale
							lowerCamelCase			      =					rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCamelCase			      =					do_normalize if do_normalize is not None else self.do_normalize
							lowerCamelCase			      =					image_mean if image_mean is not None else self.image_mean
							lowerCamelCase			      =					image_std if image_std is not None else self.image_std

							lowerCamelCase			      =					make_list_of_images(_a )

							if not valid_images(_a ):
										raise ValueError(
										    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
										    """torch.Tensor, tf.Tensor or jax.ndarray.""" )

							if do_resize and size is None:
										raise ValueError("""Size must be specified if do_resize is True.""" )

							if do_center_crop and crop_size is None:
										raise ValueError("""Crop size must be specified if do_center_crop is True.""" )

							if do_rescale and rescale_factor is None:
										raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )

							if do_normalize and (image_mean is None or image_std is None):
										raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )

							# All transformations expect numpy arrays.
							lowerCamelCase			      =					[to_numpy_array(_a ) for image in images]

							if do_resize:
										lowerCamelCase			      =					[self.resize(image=_a		, size=_a		, resample=_a ) for image in images]

							if do_center_crop:
										lowerCamelCase			      =					[self.center_crop(image=_a		, size=_a ) for image in images]

							if do_rescale:
										lowerCamelCase			      =					[self.rescale(image=_a		, scale=_a ) for image in images]

							if do_normalize:
										lowerCamelCase			      =					[self.normalize(image=_a		, mean=_a		, std=_a ) for image in images]

							lowerCamelCase			      =					[to_channel_dimension_format(_a		, _a ) for image in images]

							lowerCamelCase			      =					{"""pixel_values""": images}
							return BatchFeature(data=_a		, tensor_type=_a )





				def    _lowerCAmelCase				(       self		, _a		, _a = None ):





							"""simple docstring"""
							lowerCamelCase			      =					outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
										if len(_a ) != len(_a ):
													raise ValueError(
													    """Make sure that you pass in as many target sizes as the batch dimension of the logits""" )

										if is_torch_tensor(_a ):
													lowerCamelCase			      =					target_sizes.numpy()

										lowerCamelCase			      =					[]

										for idx in range(len(_a ) ):
													lowerCamelCase			      =					torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0 )		, size=target_sizes[idx]		, mode="""bilinear"""		, align_corners=_a )
													lowerCamelCase			      =					resized_logits[0].argmax(dim=0 )
													semantic_segmentation.append(_a )
							else:
										lowerCamelCase			      =					logits.argmax(dim=1 )
										lowerCamelCase			      =					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

							return semantic_segmentation



