import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__lowercase											=      logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-classification/requirements.txt''')

__lowercase											=      list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__lowercase											=      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    with open(SCREAMING_SNAKE_CASE						,    '''rb'''			) as f:
        __UpperCamelCase  :Dict    						=	Image.open(SCREAMING_SNAKE_CASE			)
        return im.convert('''RGB'''			)


@dataclass
class 			lowerCamelCase_  :






    '''simple docstring'''






    a__			:		Optional[str]      			=   field(
        default=UpperCAmelCase_		,			metadata={
            """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
        }		,			)
    a__			:		Optional[str]      			=   field(
        default=UpperCAmelCase_		,			metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}				)
    a__			:		Optional[str]      			=   field(default=UpperCAmelCase_		,			metadata={"""help""": """A folder containing the training data."""}				)
    a__			:		Optional[str]      			=   field(default=UpperCAmelCase_		,			metadata={"""help""": """A folder containing the validation data."""}				)
    a__			:		Optional[float]      			=   field(
        default=0.1_5		,			metadata={"""help""": """Percent to split off of train for validation."""}				)
    a__			:		Optional[int]      			=   field(
        default=UpperCAmelCase_		,			metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of training examples to this """
                """value if set."""
            )
        }		,			)
    a__			:		Optional[int]      			=   field(
        default=UpperCAmelCase_		,			metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                """value if set."""
            )
        }		,			)







    def     UpperCamelCase__     (			self) ->			Any:
        if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
            raise ValueError(
                '''You must specify either a dataset name from the hub or a train and/or validation directory.''')


@dataclass
class 			lowerCamelCase_  :






    '''simple docstring'''






    a__			:		str      			=   field(
        default="""google/vit-base-patch16-224-in21k"""		,			metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		,			)
    a__			:		Optional[str]      			=   field(
        default=UpperCAmelCase_		,			metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(UpperCAmelCase_				)}		,			)
    a__			:		Optional[str]      			=   field(
        default=UpperCAmelCase_		,			metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
    a__			:		Optional[str]      			=   field(
        default=UpperCAmelCase_		,			metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}				)
    a__			:		str      			=   field(
        default="""main"""		,			metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}		,			)
    a__			:		str      			=   field(default=UpperCAmelCase_		,			metadata={"""help""": """Name or path of preprocessor config."""}				)
    a__			:		bool      			=   field(
        default=UpperCAmelCase_		,			metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }		,			)
    a__			:		bool      			=   field(
        default=UpperCAmelCase_		,			metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}		,			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Any    						=	torch.stack([example['''pixel_values'''] for example in examples]			)
    __UpperCamelCase  :Dict    						=	torch.tensor([example['''labels'''] for example in examples]			)
    return {"pixel_values": pixel_values, "labels": labels}



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
    if len(sys.argv			) == 2 and sys.argv[1].endswith('''.json'''			):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        __UpperCamelCase  :Optional[int]    						=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
    else:
        __UpperCamelCase  :Optional[Any]    						=	parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry('''run_image_classification'''						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    # Setup logging
    logging.basicConfig(
        format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''						,    datefmt='''%m/%d/%Y %H:%M:%S'''						,    handlers=[logging.StreamHandler(sys.stdout			)]						,    )

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    __UpperCamelCase  :Optional[int]    						=	training_args.get_process_log_level()
    logger.setLevel(SCREAMING_SNAKE_CASE			)
    transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE			)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
        + f"""distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}"""			)
    logger.info(f"""Training/evaluation parameters {training_args}"""			)

    # Detecting last checkpoint.
    __UpperCamelCase  :int    						=	None
    if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
        __UpperCamelCase  :Dict    						=	get_last_checkpoint(training_args.output_dir			)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
            raise ValueError(
                f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                '''Use --overwrite_output_dir to overcome.'''			)
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''			)

    # Set seed before initializing model.
    set_seed(training_args.seed			)

    # Initialize our dataset and prepare it for the 'image-classification' task.
    if data_args.dataset_name is not None:
        __UpperCamelCase  :Optional[Any]    						=	load_dataset(
            data_args.dataset_name						,    data_args.dataset_config_name						,    cache_dir=model_args.cache_dir						,    task='''image-classification'''						,    use_auth_token=True if model_args.use_auth_token else None						,    )
    else:
        __UpperCamelCase  :Any    						=	{}
        if data_args.train_dir is not None:
            __UpperCamelCase  :int    						=	os.path.join(data_args.train_dir						,    '''**'''			)
        if data_args.validation_dir is not None:
            __UpperCamelCase  :Any    						=	os.path.join(data_args.validation_dir						,    '''**'''			)
        __UpperCamelCase  :str    						=	load_dataset(
            '''imagefolder'''						,    data_files=SCREAMING_SNAKE_CASE						,    cache_dir=model_args.cache_dir						,    task='''image-classification'''						,    )

    # If we don't have a validation split, split off a percentage of train as validation.
    __UpperCamelCase  :Optional[int]    						=	None if '''validation''' in dataset.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split						,    SCREAMING_SNAKE_CASE			) and data_args.train_val_split > 0.0:
        __UpperCamelCase  :Optional[int]    						=	dataset['''train'''].train_test_split(data_args.train_val_split			)
        __UpperCamelCase  :int    						=	split['''train''']
        __UpperCamelCase  :List[Any]    						=	split['''test''']

    # Prepare label mappings.
    # We'll include these in the model's config to get human readable labels in the Inference API.
    __UpperCamelCase  :Tuple    						=	dataset['''train'''].features['''labels'''].names
    __UpperCamelCase  :Optional[Any]    						=	{}, {}
    for i, label in enumerate(SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :Optional[int]    						=	str(SCREAMING_SNAKE_CASE			)
        __UpperCamelCase  :Optional[Any]    						=	label

    # Load the accuracy metric from the datasets package
    __UpperCamelCase  :Dict    						=	evaluate.load('''accuracy'''			)

    # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
    def compute_metrics(SCREAMING_SNAKE_CASE			):
        return metric.compute(predictions=np.argmax(p.predictions						,    axis=1			)						,    references=p.label_ids			)

    __UpperCamelCase  :Tuple    						=	AutoConfig.from_pretrained(
        model_args.config_name or model_args.model_name_or_path						,    num_labels=len(SCREAMING_SNAKE_CASE			)						,    labelaid=SCREAMING_SNAKE_CASE						,    idalabel=SCREAMING_SNAKE_CASE						,    finetuning_task='''image-classification'''						,    cache_dir=model_args.cache_dir						,    revision=model_args.model_revision						,    use_auth_token=True if model_args.use_auth_token else None						,    )
    __UpperCamelCase  :List[Any]    						=	AutoModelForImageClassification.from_pretrained(
        model_args.model_name_or_path						,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path			)						,    config=SCREAMING_SNAKE_CASE						,    cache_dir=model_args.cache_dir						,    revision=model_args.model_revision						,    use_auth_token=True if model_args.use_auth_token else None						,    ignore_mismatched_sizes=model_args.ignore_mismatched_sizes						,    )
    __UpperCamelCase  :str    						=	AutoImageProcessor.from_pretrained(
        model_args.image_processor_name or model_args.model_name_or_path						,    cache_dir=model_args.cache_dir						,    revision=model_args.model_revision						,    use_auth_token=True if model_args.use_auth_token else None						,    )

    # Define torchvision transforms to be applied to each image.
    if "shortest_edge" in image_processor.size:
        __UpperCamelCase  :str    						=	image_processor.size['''shortest_edge''']
    else:
        __UpperCamelCase  :Any    						=	(image_processor.size['''height'''], image_processor.size['''width'''])
    __UpperCamelCase  :Any    						=	Normalize(mean=image_processor.image_mean						,    std=image_processor.image_std			)
    __UpperCamelCase  :Optional[Any]    						=	Compose(
        [
            RandomResizedCrop(SCREAMING_SNAKE_CASE			),
            RandomHorizontalFlip(),
            ToTensor(),
            normalize,
        ]			)
    __UpperCamelCase  :Optional[int]    						=	Compose(
        [
            Resize(SCREAMING_SNAKE_CASE			),
            CenterCrop(SCREAMING_SNAKE_CASE			),
            ToTensor(),
            normalize,
        ]			)

    def train_transforms(SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :Optional[Any]    						=	[
            _train_transforms(pil_img.convert('''RGB'''			)			) for pil_img in example_batch['''image''']
        ]
        return example_batch

    def val_transforms(SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :Tuple    						=	[_val_transforms(pil_img.convert('''RGB'''			)			) for pil_img in example_batch['''image''']]
        return example_batch

    if training_args.do_train:
        if "train" not in dataset:
            raise ValueError('''--do_train requires a train dataset'''			)
        if data_args.max_train_samples is not None:
            __UpperCamelCase  :List[Any]    						=	(
                dataset['''train'''].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
            )
        # Set the training transforms
        dataset["train"].set_transform(SCREAMING_SNAKE_CASE			)

    if training_args.do_eval:
        if "validation" not in dataset:
            raise ValueError('''--do_eval requires a validation dataset'''			)
        if data_args.max_eval_samples is not None:
            __UpperCamelCase  :int    						=	(
                dataset['''validation'''].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
            )
        # Set the validation transforms
        dataset["validation"].set_transform(SCREAMING_SNAKE_CASE			)

    # Initalize our trainer
    __UpperCamelCase  :Optional[Any]    						=	Trainer(
        model=SCREAMING_SNAKE_CASE						,    args=SCREAMING_SNAKE_CASE						,    train_dataset=dataset['''train'''] if training_args.do_train else None						,    eval_dataset=dataset['''validation'''] if training_args.do_eval else None						,    compute_metrics=SCREAMING_SNAKE_CASE						,    tokenizer=SCREAMING_SNAKE_CASE						,    data_collator=SCREAMING_SNAKE_CASE						,    )

    # Training
    if training_args.do_train:
        __UpperCamelCase  :List[str]    						=	None
        if training_args.resume_from_checkpoint is not None:
            __UpperCamelCase  :Dict    						=	training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            __UpperCamelCase  :str    						=	last_checkpoint
        __UpperCamelCase  :Dict    						=	trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE			)
        trainer.save_model()
        trainer.log_metrics('''train'''						,    train_result.metrics			)
        trainer.save_metrics('''train'''						,    train_result.metrics			)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        __UpperCamelCase  :Union[str, Any]    						=	trainer.evaluate()
        trainer.log_metrics('''eval'''						,    SCREAMING_SNAKE_CASE			)
        trainer.save_metrics('''eval'''						,    SCREAMING_SNAKE_CASE			)

    # Write model card and (optionally) push to hub
    __UpperCamelCase  :str    						=	{
        '''finetuned_from''': model_args.model_name_or_path,
        '''tasks''': '''image-classification''',
        '''dataset''': data_args.dataset_name,
        '''tags''': ['''image-classification''', '''vision'''],
    }
    if training_args.push_to_hub:
        trainer.push_to_hub(**SCREAMING_SNAKE_CASE			)
    else:
        trainer.create_model_card(**SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
     main()






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_  :






        '''simple docstring'''






        @staticmethod
        def     UpperCamelCase__     (			*__lowercase				,				**__lowercase) ->			Dict:
            pass


@is_pipeline_test
@require_vision
@require_torch
class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    a__			:		str      			=   MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase) ->			str:
        __UpperCamelCase  :Union[str, Any]    						=	pipeline(
            '''zero-shot-object-detection'''				,				model='''hf-internal-testing/tiny-random-owlvit-object-detection''')

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







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase) ->			Optional[Any]:
        __UpperCamelCase  :Optional[Any]    						=	object_detector(examples[0]				,				threshold=0.0)

        __UpperCamelCase  :Dict    						=	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     UpperCamelCase__     (			self) ->			int:
        pass







    @require_torch
    def     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :Tuple    						=	pipeline(
            '''zero-shot-object-detection'''				,				model='''hf-internal-testing/tiny-random-owlvit-object-detection''')

        __UpperCamelCase  :str    						=	object_detector(
            '''./tests/fixtures/tests_samples/COCO/000000039769.png'''				,				candidate_labels=['''cat''', '''remote''', '''couch''']				,				threshold=0.64				,				)

        self.assertEqual(
            nested_simplify(__lowercase				,				decimals=4)				,				[
                {'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                {'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
                {'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
            ]				,				)

        __UpperCamelCase  :str    						=	object_detector(
            [
                {
                    '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                    '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                }
            ]				,				threshold=0.64				,				)

        self.assertEqual(
            nested_simplify(__lowercase				,				decimals=4)				,				[
                [
                    {'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                    {'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                    {'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                    {'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                    {'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                    {'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                    {'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                    {'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
                    {'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                ]
            ]				,				)







    @require_torch
    @slow
    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :List[str]    						=	pipeline('''zero-shot-object-detection''')

        __UpperCamelCase  :List[str]    						=	object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg'''				,				candidate_labels=['''cat''', '''remote''', '''couch''']				,				)
        self.assertEqual(
            nested_simplify(__lowercase				,				decimals=4)				,				[
                {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
            ]				,				)

        __UpperCamelCase  :Dict    						=	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''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                    {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                    {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                    {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                    {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
                ],
                [
                    {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                    {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                    {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                    {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                    {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
                ],
            ]				,				)







    @require_tf
    @unittest.skip('''Zero Shot Object Detection not implemented in TF''')
    def     UpperCamelCase__     (			self) ->			Dict:
        pass







    @require_torch
    @slow
    def     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :Tuple    						=	0.2
        __UpperCamelCase  :str    						=	pipeline('''zero-shot-object-detection''')

        __UpperCamelCase  :Any    						=	object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg'''				,				candidate_labels=['''cat''', '''remote''', '''couch''']				,				threshold=__lowercase				,				)
        self.assertEqual(
            nested_simplify(__lowercase				,				decimals=4)				,				[
                {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
            ]				,				)







    @require_torch
    @slow
    def     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :Optional[int]    						=	2
        __UpperCamelCase  :Union[str, Any]    						=	pipeline('''zero-shot-object-detection''')

        __UpperCamelCase  :Any    						=	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''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
            ]				,				)





