


"""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 the experiment tracking capability,
# 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
#
########################################################################

__UpperCamelCase						:				Optional[Any]             =      16
__UpperCamelCase						:				Dict             =      32







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ = 16	)      ->		Union[str, Any]:







		"""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

	__UpperCamelCase						:				Union[str, Any]             =      mocked_dataloaders  # noqa: F811







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		List[str]:







		"""simple docstring"""





		if os.environ.get('TESTING_MOCKED_DATALOADERS' ,			UpperCamelCase__	) == "1":
				A					       = 2
		# Initialize Accelerator

		# New Code #
		# We pass in "all" to `log_with` to grab all available trackers in the environment
		# Note: If using a custom `Tracker` class, should be passed in here such as:
		# >>> log_with = ["all", MyCustomTrackerClassInstance()]
		if args.with_tracking:
				A					       = Accelerator(
				    cpu=args.cpu ,			mixed_precision=args.mixed_precision ,			log_with='all' ,			project_dir=args.project_dir	)
		else:
				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']	)
		set_seed(UpperCamelCase__	)

		A       , A					       = get_dataloaders(UpperCamelCase__ ,			UpperCamelCase__	)
		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

		# 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__	)

		# New Code #
		# We need to initialize the trackers we use. Overall configurations can also be stored
		if args.with_tracking:
				A					       = os.path.split(UpperCamelCase__	)[-1].split('.'	)[0]
				accelerator.init_trackers(UpperCamelCase__ ,			UpperCamelCase__	)

		# Now we train the model
		for epoch in range(UpperCamelCase__	):
				model.train()
				# New Code #
				# For our tracking example, we will log the total loss of each epoch
				if args.with_tracking:
						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	)
						A					       = model(**UpperCamelCase__	)
						A					       = outputs.loss
						# New Code #
						if args.with_tracking:
								total_loss += loss.detach().float()
						A					       = loss / gradient_accumulation_steps
						accelerator.backward(UpperCamelCase__	)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				for step, batch in enumerate(UpperCamelCase__	):
						# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
						batch.to(accelerator.device	)
						with torch.no_grad():
								A					       = model(**UpperCamelCase__	)
						A					       = outputs.logits.argmax(dim=-1	)
						A       , A					       = 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__	)

				# New Code #
				# To actually log, we call `Accelerator.log`
				# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
				if args.with_tracking:
						accelerator.log(
						    {
						        'accuracy': eval_metric['accuracy'],
						        'f1': eval_metric['f1'],
						        'train_loss': total_loss.item() / len(UpperCamelCase__	),
						        'epoch': epoch,
						    } ,			step=UpperCamelCase__ ,			)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
		if args.with_tracking:
				accelerator.end_training()







def  __snake_case       (   )      ->		int:







		"""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.'	)
		parser.add_argument(
		    '--with_tracking' ,			action='store_true' ,			help='Whether to load in all available experiment trackers from the environment and use them for logging.' ,			)
		parser.add_argument(
		    '--project_dir' ,			type=UpperCamelCase__ ,			default='logs' ,			help='Location on where to store experiment tracking logs` and relevent project information' ,			)
		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"""


import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
	from PIL import Image
else:



	class 			lowerCamelCase__						:







							@staticmethod
							def __a						(							*_lowercase   :    int			,			**_lowercase   :    Optional[int]		):
									pass



@is_pipeline_test
@require_vision
@require_torch
class 			lowerCamelCase__						(   unittest.TestCase			):
						lowerCAmelCase       =     MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING







						def __a						(							self   :    int			,			_lowercase   :    Optional[Any]			,			_lowercase   :    int			,			_lowercase   :    Union[str, Any]		):
								A					       = pipeline(
								    'zero-shot-object-detection'			,			model='hf-internal-testing/tiny-random-owlvit-object-detection'		)

								A					       = [
								    {
								        'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
								        'candidate_labels': ['cat', 'remote', 'couch'],
								    }
								]
								return object_detector, examples







						def __a						(							self   :    Tuple			,			_lowercase   :    int			,			_lowercase   :    Tuple		):
								A					       = object_detector(examples[0]			,			threshold=0.0		)

								A					       = len(_lowercase		)
								self.assertGreater(_lowercase			,			0		)
								self.assertEqual(
								    _lowercase			,			[
								        {
								            'score': ANY(_lowercase		),
								            'label': ANY(_lowercase		),
								            'box': {'xmin': ANY(_lowercase		), 'ymin': ANY(_lowercase		), 'xmax': ANY(_lowercase		), 'ymax': ANY(_lowercase		)},
								        }
								        for i in range(_lowercase		)
								    ]			,			)







						@require_tf
						@unittest.skip('Zero Shot Object Detection not implemented in TF'		)
						def __a						(							self   :    Optional[int]		):
								pass







						@require_torch
						def __a						(							self   :    Optional[Any]		):
								A					       = pipeline(
								    'zero-shot-object-detection'			,			model='hf-internal-testing/tiny-random-owlvit-object-detection'		)

								A					       = object_detector(
								    './tests/fixtures/tests_samples/COCO/000000039769.png'			,			candidate_labels=['cat', 'remote', 'couch']			,			threshold=0.6_4			,			)

								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        {'score': 0.7_2_3_5, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								        {'score': 0.7_2_1_8, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								        {'score': 0.7_1_8_4, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								        {'score': 0.6_7_4_8, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								        {'score': 0.6_6_5_6, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								        {'score': 0.6_6_1_4, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								        {'score': 0.6_4_5_6, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
								        {'score': 0.6_4_2, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
								        {'score': 0.6_4_1_9, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
								    ]			,			)

								A					       = object_detector(
								    [
								        {
								            'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
								            'candidate_labels': ['cat', 'remote', 'couch'],
								        }
								    ]			,			threshold=0.6_4			,			)

								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        [
								            {'score': 0.7_2_3_5, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								            {'score': 0.7_2_1_8, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								            {'score': 0.7_1_8_4, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								            {'score': 0.6_7_4_8, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								            {'score': 0.6_6_5_6, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								            {'score': 0.6_6_1_4, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								            {'score': 0.6_4_5_6, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
								            {'score': 0.6_4_2, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
								            {'score': 0.6_4_1_9, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
								        ]
								    ]			,			)







						@require_torch
						@slow
						def __a						(							self   :    List[str]		):
								A					       = pipeline('zero-shot-object-detection'		)

								A					       = object_detector(
								    'http://images.cocodataset.org/val2017/000000039769.jpg'			,			candidate_labels=['cat', 'remote', 'couch']			,			)
								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
								        {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
								        {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
								        {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
								        {'score': 0.1_2_0_8, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
								    ]			,			)

								A					       = object_detector(
								    [
								        {
								            'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
								            'candidate_labels': ['cat', 'remote', 'couch'],
								        },
								        {
								            'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
								            'candidate_labels': ['cat', 'remote', 'couch'],
								        },
								    ]			,			)
								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        [
								            {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
								            {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
								            {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
								            {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
								            {'score': 0.1_2_0_8, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
								        ],
								        [
								            {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
								            {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
								            {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
								            {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
								            {'score': 0.1_2_0_8, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
								        ],
								    ]			,			)







						@require_tf
						@unittest.skip('Zero Shot Object Detection not implemented in TF'		)
						def __a						(							self   :    Optional[int]		):
								pass







						@require_torch
						@slow
						def __a						(							self   :    Optional[Any]		):
								A					       = 0.2
								A					       = pipeline('zero-shot-object-detection'		)

								A					       = object_detector(
								    'http://images.cocodataset.org/val2017/000000039769.jpg'			,			candidate_labels=['cat', 'remote', 'couch']			,			threshold=_lowercase			,			)
								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
								        {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
								        {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
								    ]			,			)







						@require_torch
						@slow
						def __a						(							self   :    Optional[int]		):
								A					       = 2
								A					       = pipeline('zero-shot-object-detection'		)

								A					       = object_detector(
								    'http://images.cocodataset.org/val2017/000000039769.jpg'			,			candidate_labels=['cat', 'remote', 'couch']			,			top_k=_lowercase			,			)
								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
								        {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
								    ]			,			)





