





'''simple docstring'''
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
a__							:							List[str] =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''')

a__							:							Union[str, Any] =list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
a__							:							Optional[Any] =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def 				lowercase__   (   __lowercase						:  str     ) ->      int:



       """simple docstring"""






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

@dataclass
class      snake_case     :




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[str]					=field(
     default=__lowerCamelCase			,       metadata={
         "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
     }			,       )
 SCREAMING_SNAKE_CASE_	:					Optional[str]					=field(
     default=__lowerCamelCase			,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}       )
 SCREAMING_SNAKE_CASE_	:					Optional[str]					=field(default=__lowerCamelCase			,       metadata={"help": "A folder containing the training data."}       )
 SCREAMING_SNAKE_CASE_	:					Optional[str]					=field(default=__lowerCamelCase			,       metadata={"help": "A folder containing the validation data."}       )
 SCREAMING_SNAKE_CASE_	:					Optional[float]					=field(
     default=0.15			,       metadata={"help": "Percent to split off of train for validation."}       )
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=field(
     default=__lowerCamelCase			,       metadata={
         "help": (
             "For debugging purposes or quicker training, truncate the number of training examples to this "
             "value if set."
         )
     }			,       )
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=field(
     default=__lowerCamelCase			,       metadata={
         "help": (
             "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
             "value if set."
         )
     }			,       )






 def        _lowerCamelCase					(       self       :							Tuple	):
        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      snake_case     :




 """simple docstring"""



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






def 				lowercase__   (   __lowercase						:  Any     ) ->      str:



       """simple docstring"""






       __UpperCamelCase       =  torch.stack([example['pixel_values'] for example in examples]     )
       __UpperCamelCase       =  torch.tensor([example['labels'] for example in examples]     )
       return {"pixel_values": pixel_values, "labels": labels}
def 				lowercase__   (   ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  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       ,						__UpperCamelCase       ,						__UpperCamelCase       =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
       else:
              __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  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'				,  __lowercase				,  __lowercase     )

       # 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       =  training_args.get_process_log_level()
       logger.setLevel(__lowercase     )
       transformers.utils.logging.set_verbosity(__lowercase     )
       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       =  None
       if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
              __UpperCamelCase       =  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       =  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       =  {}
              if data_args.train_dir is not None:
                     __UpperCamelCase       =  os.path.join(data_args.train_dir				,  '**'     )
              if data_args.validation_dir is not None:
                     __UpperCamelCase       =  os.path.join(data_args.validation_dir				,  '**'     )
              __UpperCamelCase       =  load_dataset(
                  'imagefolder'				,  data_files=__lowercase				,  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       =  None if 'validation' in dataset.keys() else data_args.train_val_split
       if isinstance(data_args.train_val_split				,  __lowercase     ) and data_args.train_val_split > 0.0:
              __UpperCamelCase       =  dataset['train'].train_test_split(data_args.train_val_split     )
              __UpperCamelCase       =  split['train']
              __UpperCamelCase       =  split['test']

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

       # Load the accuracy metric from the datasets package
       __UpperCamelCase       =  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(__lowercase						:  Optional[int]     ):
              return metric.compute(predictions=np.argmax(p.predictions				,  axis=1     )				,  references=p.label_ids     )

       __UpperCamelCase       =  AutoConfig.from_pretrained(
           model_args.config_name or model_args.model_name_or_path				,  num_labels=len(__lowercase     )				,  labelaid=__lowercase				,  idalabel=__lowercase				,  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       =  AutoModelForImageClassification.from_pretrained(
           model_args.model_name_or_path				,  from_tf=bool('.ckpt' in model_args.model_name_or_path     )				,  config=__lowercase				,  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       =  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       =  image_processor.size['shortest_edge']
       else:
              __UpperCamelCase       =  (image_processor.size['height'], image_processor.size['width'])
       __UpperCamelCase       =  Normalize(mean=image_processor.image_mean				,  std=image_processor.image_std     )
       __UpperCamelCase       =  Compose(
           [
               RandomResizedCrop(__lowercase     ),
               RandomHorizontalFlip(),
               ToTensor(),
               normalize,
           ]     )
       __UpperCamelCase       =  Compose(
           [
               Resize(__lowercase     ),
               CenterCrop(__lowercase     ),
               ToTensor(),
               normalize,
           ]     )

       def train_transforms(__lowercase						:  List[str]     ):
              __UpperCamelCase       =  [
                  _train_transforms(pil_img.convert('RGB'     )     ) for pil_img in example_batch['image']
              ]
              return example_batch

       def val_transforms(__lowercase						:  Union[str, Any]     ):
              __UpperCamelCase       =  [_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       =  (
                         dataset['train'].shuffle(seed=training_args.seed     ).select(range(data_args.max_train_samples     )     )
                     )
              # Set the training transforms
              dataset["train"].set_transform(__lowercase     )

       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       =  (
                         dataset['validation'].shuffle(seed=training_args.seed     ).select(range(data_args.max_eval_samples     )     )
                     )
              # Set the validation transforms
              dataset["validation"].set_transform(__lowercase     )

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

       # Training
       if training_args.do_train:
              __UpperCamelCase       =  None
              if training_args.resume_from_checkpoint is not None:
                     __UpperCamelCase       =  training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     __UpperCamelCase       =  last_checkpoint
              __UpperCamelCase       =  trainer.train(resume_from_checkpoint=__lowercase     )
              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       =  trainer.evaluate()
              trainer.log_metrics('eval'				,  __lowercase     )
              trainer.save_metrics('eval'				,  __lowercase     )

       # Write model card and (optionally) push to hub
       __UpperCamelCase       =  {
           '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(**__lowercase     )
       else:
              trainer.create_model_card(**__lowercase     )


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__   :







												"""simple docstring"""


												@staticmethod
												def lowerCamelCase__							(       *UpperCamelCase    : List[str] ,       **UpperCamelCase    : Any  ):
																			'''simple docstring'''






																			pass





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







					"""simple docstring"""


					__a	       =							MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING






					def lowerCamelCase__							(       self    : List[str] ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : Optional[int] ,       UpperCamelCase    : Tuple  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[int]      							=					pipeline(
												    """zero-shot-object-detection""" ,       model="""hf-internal-testing/tiny-random-owlvit-object-detection"""  )

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






					def lowerCamelCase__							(       self    : int ,       UpperCamelCase    : Optional[int] ,       UpperCamelCase    : List[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					object_detector(examples[0] ,       threshold=0.0  )

												__UpperCAmelCase   :						Tuple      							=					len(UpperCamelCase  )
												self.assertGreater(UpperCamelCase ,       0  )
												self.assertEqual(
												    UpperCamelCase ,       [
												        {
												            """score""": ANY(UpperCamelCase  ),
												            """label""": ANY(UpperCamelCase  ),
												            """box""": {"""xmin""": ANY(UpperCamelCase  ), """ymin""": ANY(UpperCamelCase  ), """xmax""": ANY(UpperCamelCase  ), """ymax""": ANY(UpperCamelCase  )},
												        }
												        for i in range(UpperCamelCase  )
												    ] ,       )






					@require_tf
					@unittest.skip("""Zero Shot Object Detection not implemented in TF"""  )
					def lowerCamelCase__							(       self    : Optional[int]  ):
												'''simple docstring'''






												pass






					@require_torch
					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					pipeline(
												    """zero-shot-object-detection""" ,       model="""hf-internal-testing/tiny-random-owlvit-object-detection"""  )

												__UpperCAmelCase   :						Tuple      							=					object_detector(
												    """./tests/fixtures/tests_samples/COCO/000000039769.png""" ,       candidate_labels=["""cat""", """remote""", """couch"""] ,       threshold=0.64 ,       )

												self.assertEqual(
												    nested_simplify(UpperCamelCase ,       decimals=4  ) ,       [
												        {"""score""": 0.7235, """label""": """cat""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
												        {"""score""": 0.7218, """label""": """remote""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
												        {"""score""": 0.7184, """label""": """couch""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
												        {"""score""": 0.6748, """label""": """remote""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
												        {"""score""": 0.6656, """label""": """cat""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
												        {"""score""": 0.6614, """label""": """couch""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
												        {"""score""": 0.6456, """label""": """remote""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
												        {"""score""": 0.642, """label""": """remote""", """box""": {"""xmin""": 67, """ymin""": 274, """xmax""": 93, """ymax""": 297}},
												        {"""score""": 0.6419, """label""": """cat""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
												    ] ,       )

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

												self.assertEqual(
												    nested_simplify(UpperCamelCase ,       decimals=4  ) ,       [
												        [
												            {"""score""": 0.7235, """label""": """cat""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
												            {"""score""": 0.7218, """label""": """remote""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
												            {"""score""": 0.7184, """label""": """couch""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
												            {"""score""": 0.6748, """label""": """remote""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
												            {"""score""": 0.6656, """label""": """cat""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
												            {"""score""": 0.6614, """label""": """couch""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
												            {"""score""": 0.6456, """label""": """remote""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
												            {"""score""": 0.642, """label""": """remote""", """box""": {"""xmin""": 67, """ymin""": 274, """xmax""": 93, """ymax""": 297}},
												            {"""score""": 0.6419, """label""": """cat""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
												        ]
												    ] ,       )






					@require_torch
					@slow
					def lowerCamelCase__							(       self    : Optional[int]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					pipeline("""zero-shot-object-detection"""  )

												__UpperCAmelCase   :						int      							=					object_detector(
												    """http://images.cocodataset.org/val2017/000000039769.jpg""" ,       candidate_labels=["""cat""", """remote""", """couch"""] ,       )
												self.assertEqual(
												    nested_simplify(UpperCamelCase ,       decimals=4  ) ,       [
												        {"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
												        {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
												        {"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
												        {"""score""": 0.1474, """label""": """remote""", """box""": {"""xmin""": 335, """ymin""": 74, """xmax""": 371, """ymax""": 187}},
												        {"""score""": 0.1208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 642, """ymax""": 476}},
												    ] ,       )

												__UpperCAmelCase   :						List[str]      							=					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(UpperCamelCase ,       decimals=4  ) ,       [
												        [
												            {"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
												            {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
												            {"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
												            {"""score""": 0.1474, """label""": """remote""", """box""": {"""xmin""": 335, """ymin""": 74, """xmax""": 371, """ymax""": 187}},
												            {"""score""": 0.1208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 642, """ymax""": 476}},
												        ],
												        [
												            {"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
												            {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
												            {"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
												            {"""score""": 0.1474, """label""": """remote""", """box""": {"""xmin""": 335, """ymin""": 74, """xmax""": 371, """ymax""": 187}},
												            {"""score""": 0.1208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 642, """ymax""": 476}},
												        ],
												    ] ,       )






					@require_tf
					@unittest.skip("""Zero Shot Object Detection not implemented in TF"""  )
					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												pass






					@require_torch
					@slow
					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					0.2
												__UpperCAmelCase   :						List[Any]      							=					pipeline("""zero-shot-object-detection"""  )

												__UpperCAmelCase   :						Any      							=					object_detector(
												    """http://images.cocodataset.org/val2017/000000039769.jpg""" ,       candidate_labels=["""cat""", """remote""", """couch"""] ,       threshold=UpperCamelCase ,       )
												self.assertEqual(
												    nested_simplify(UpperCamelCase ,       decimals=4  ) ,       [
												        {"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
												        {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
												        {"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
												    ] ,       )






					@require_torch
					@slow
					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					2
												__UpperCAmelCase   :						Union[str, Any]      							=					pipeline("""zero-shot-object-detection"""  )

												__UpperCAmelCase   :						List[str]      							=					object_detector(
												    """http://images.cocodataset.org/val2017/000000039769.jpg""" ,       candidate_labels=["""cat""", """remote""", """couch"""] ,       top_k=UpperCamelCase ,       )
												self.assertEqual(
												    nested_simplify(UpperCamelCase ,       decimals=4  ) ,       [
												        {"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
												        {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
												    ] ,       )
