"""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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################


__lowerCamelCase											=  16
__lowerCamelCase											=  32






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__ = 16				):






							"""simple docstring"""





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

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

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

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

							# Instantiate dataloaders.
							A__	     =							DataLoader(
							    tokenized_datasets['train']       ,    shuffle=UpperCamelCase__       ,    collate_fn=UpperCamelCase__       ,    batch_size=UpperCamelCase__				)
							A__	     =							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   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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

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

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

							set_seed(UpperCamelCase__				)
							A__     ,  A__	     =							get_dataloaders(UpperCamelCase__       ,    UpperCamelCase__				)
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							A__	     =							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).
							A__	     =							model.to(accelerator.device				)

							# Instantiate optimizer
							A__	     =							AdamW(params=model.parameters()       ,    lr=UpperCamelCase__				)

							# Instantiate scheduler
							A__	     =							get_linear_schedule_with_warmup(
							    optimizer=UpperCamelCase__       ,    num_warmup_steps=100       ,    num_training_steps=(len(UpperCamelCase__				) * 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.
							A__     ,  A__     ,  A__     ,  A__     ,  A__	     =							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				)
																					A__	     =							model(**UpperCamelCase__				)
																					A__	     =							outputs.loss
																					A__	     =							loss / gradient_accumulation_steps
																					accelerator.backward(UpperCamelCase__				)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

														model.eval()
														A__	     =							0
														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():
																												A__	     =							model(**UpperCamelCase__				)
																					A__	     =							outputs.logits.argmax(dim=-1				)
																					A__     ,  A__	     =							accelerator.gather((predictions, batch['labels'])				)
																					# New Code #
																					# First we check if it's a distributed system
																					if accelerator.use_distributed:
																												# Then see if we're on the last batch of our eval dataloader
																												if step == len(UpperCamelCase__				) - 1:
																																			# Last batch needs to be truncated on distributed systems as it contains additional samples
																																			A__	     =							predictions[: len(eval_dataloader.dataset				) - samples_seen]
																																			A__	     =							references[: len(eval_dataloader.dataset				) - samples_seen]
																												else:
																																			# Otherwise we add the number of samples seen
																																			samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
																					metric.add_batch(
																					    predictions=UpperCamelCase__       ,    references=UpperCamelCase__       ,    )

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






def   UpperCAmelCase					(	):






							"""simple docstring"""





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


if __name__ == "__main__":
				main()






"""simple docstring"""




from typing import Dict, Optional

import numpy as np

import datasets


__lowerCamelCase											=  "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n"

__lowerCamelCase											=  "\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n"

__lowerCamelCase											=  "\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = False       ,    ):






							"""simple docstring"""





							if label_map is not None:
														for old_id, new_id in label_map.items():
																					A__	     =							new_id

    # turn into Numpy arrays
							A__	     =							np.array(UpperCamelCase__				)
							A__	     =							np.array(UpperCamelCase__				)

							if reduce_labels:
														A__	     =							255
														A__	     =							label - 1
														A__	     =							255

							A__	     =							label != ignore_index
							A__	     =							np.not_equal(UpperCamelCase__       ,    UpperCamelCase__				)
							A__	     =							pred_label[mask]
							A__	     =							np.array(UpperCamelCase__				)[mask]

							A__	     =							pred_label[pred_label == label]

							A__	     =							np.histogram(UpperCamelCase__       ,    bins=UpperCamelCase__       ,    range=(0, num_labels - 1)				)[0]
							A__	     =							np.histogram(UpperCamelCase__       ,    bins=UpperCamelCase__       ,    range=(0, num_labels - 1)				)[0]
							A__	     =							np.histogram(UpperCamelCase__       ,    bins=UpperCamelCase__       ,    range=(0, num_labels - 1)				)[0]

							A__	     =							area_pred_label + area_label - area_intersect

							return area_intersect, area_union, area_pred_label, area_label






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = False       ,    ):






							"""simple docstring"""





							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							for result, gt_seg_map in zip(UpperCamelCase__       ,    UpperCamelCase__				):
														A__     ,  A__     ,  A__     ,  A__	     =							intersect_and_union(
														    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				)
														total_area_intersect += area_intersect
														total_area_union += area_union
														total_area_pred_label += area_pred_label
														total_area_label += area_label
							return total_area_intersect, total_area_union, total_area_pred_label, total_area_label






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = False       ,    ):






							"""simple docstring"""





							A__     ,  A__     ,  A__     ,  A__	     =							total_intersect_and_union(
							    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				)

							# compute metrics
							A__	     =							{}

							A__	     =							total_area_intersect.sum() / total_area_label.sum()
							A__	     =							total_area_intersect / total_area_union
							A__	     =							total_area_intersect / total_area_label

							A__	     =							np.nanmean(UpperCamelCase__				)
							A__	     =							np.nanmean(UpperCamelCase__				)
							A__	     =							all_acc
							A__	     =							iou
							A__	     =							acc

							if nan_to_num is not None:
														A__	     =							{metric: np.nan_to_num(UpperCamelCase__       ,    nan=UpperCamelCase__				) for metric, metric_value in metrics.items()}

							return metrics


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,						_KWARGS_DESCRIPTION )
class     UpperCamelCase__(			datasets.Metric ):





						def 				snake_case__	(       self     )					->				str:
													return datasets.MetricInfo(
													    description=_DESCRIPTION		,citation=_CITATION		,inputs_description=_KWARGS_DESCRIPTION		,features=datasets.Features(
													        # 1st Seq - height dim, 2nd - width dim
													        {
													            'predictions': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'     )     )     ),
													            'references': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'     )     )     ),
													        }     )		,reference_urls=[
													        'https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'
													    ]		,)





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = False		,)					->				Tuple:
													A__	     =							mean_iou(
													    results=__UpperCAmelCase		,gt_seg_maps=__UpperCAmelCase		,num_labels=__UpperCAmelCase		,ignore_index=__UpperCAmelCase		,nan_to_num=__UpperCAmelCase		,label_map=__UpperCAmelCase		,reduce_labels=__UpperCAmelCase		,)
													return iou_result





