




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

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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
SCREAMING_SNAKE_CASE:     Optional[int]									= 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-pretraining/requirements.txt''')

SCREAMING_SNAKE_CASE:     int									= list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
SCREAMING_SNAKE_CASE:     str									= tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class    lowercase_		:
						lowerCAmelCase__			      =field(
						    default="cifar10"							,	metadata={"help": "Name of a dataset from the datasets package"} )
						lowerCAmelCase__			      =field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
						lowerCAmelCase__			      =field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."}							,	)
						lowerCAmelCase__			      =field(default=SCREAMING_SNAKE_CASE__							,	metadata={"help": "A folder containing the training data."} )
						lowerCAmelCase__			      =field(default=SCREAMING_SNAKE_CASE__							,	metadata={"help": "A folder containing the validation data."} )
						lowerCAmelCase__			      =field(
						    default=0.15							,	metadata={"help": "Percent to split off of train for validation."} )
						lowerCAmelCase__			      =field(default=32							,	metadata={"help": "The size of the square patches to use for masking."} )
						lowerCAmelCase__			      =field(
						    default=0.6							,	metadata={"help": "Percentage of patches to mask."}							,	)
						lowerCAmelCase__			      =field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={
						        "help": (
						            "For debugging purposes or quicker training, truncate the number of training examples to this "
						            "value if set."
						        )
						    }							,	)
						lowerCAmelCase__			      =field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={
						        "help": (
						            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
						            "value if set."
						        )
						    }							,	)



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{}
								if self.train_dir is not None:
										SCREAMING_SNAKE_CASE_					      =							self.train_dir
								if self.validation_dir is not None:
										SCREAMING_SNAKE_CASE_					      =							self.validation_dir
								SCREAMING_SNAKE_CASE_					      =							data_files if data_files else None



@dataclass
class    lowercase_		:
						lowerCAmelCase__			      =field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={
						        "help": (
						            "The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a "
						            "checkpoint identifier on the hub. "
						            "Don't set if you want to train a model from scratch."
						        )
						    }							,	)
						lowerCAmelCase__			      =field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(SCREAMING_SNAKE_CASE__ )}							,	)
						lowerCAmelCase__			      =field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={"help": "Pretrained config name or path if not the same as model_name"} )
						lowerCAmelCase__			      =field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={
						        "help": (
						            "Override some existing default config settings when a model is trained from scratch. Example: "
						            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
						        )
						    }							,	)
						lowerCAmelCase__			      =field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"}							,	)
						lowerCAmelCase__			      =field(
						    default="main"							,	metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}							,	)
						lowerCAmelCase__			      =field(default=SCREAMING_SNAKE_CASE__							,	metadata={"help": "Name or path of preprocessor config."} )
						lowerCAmelCase__			      =field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={
						        "help": (
						            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
						            "with private models)."
						        )
						    }							,	)
						lowerCAmelCase__			      =field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={
						        "help": (
						            "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
						        )
						    }							,	)
						lowerCAmelCase__			      =field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={
						        "help": (
						            "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
						        )
						    }							,	)
						lowerCAmelCase__			      =field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={"help": "Stride to use for the encoder."}							,	)



class    lowercase_		:



						def __init__(			self	:   str					,							snake_case__	:   Any=1_92					,							snake_case__	:   Optional[int]=32					,							snake_case__	:   int=4					,							snake_case__	:   Tuple=0.6  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							input_size
								SCREAMING_SNAKE_CASE_					      =							mask_patch_size
								SCREAMING_SNAKE_CASE_					      =							model_patch_size
								SCREAMING_SNAKE_CASE_					      =							mask_ratio

								if self.input_size % self.mask_patch_size != 0:
										raise ValueError('Input size must be divisible by mask patch size'  )
								if self.mask_patch_size % self.model_patch_size != 0:
										raise ValueError('Mask patch size must be divisible by model patch size'  )

								SCREAMING_SNAKE_CASE_					      =							self.input_size // self.mask_patch_size
								SCREAMING_SNAKE_CASE_					      =							self.mask_patch_size // self.model_patch_size

								SCREAMING_SNAKE_CASE_					      =							self.rand_size**2
								SCREAMING_SNAKE_CASE_					      =							int(np.ceil(self.token_count * self.mask_ratio  )  )






						def __call__(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							np.random.permutation(self.token_count  )[: self.mask_count]
								SCREAMING_SNAKE_CASE_					      =							np.zeros(self.token_count					,							dtype=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							1

								SCREAMING_SNAKE_CASE_					      =							mask.reshape((self.rand_size, self.rand_size)  )
								SCREAMING_SNAKE_CASE_					      =							mask.repeat(self.scale					,							axis=0  ).repeat(self.scale					,							axis=1  )

								return torch.tensor(mask.flatten()  )


def 					_a							(					lowerCAmelCase )->	str:
		SCREAMING_SNAKE_CASE_					      =							torch.stack([example['pixel_values'] for example in examples] )
		SCREAMING_SNAKE_CASE_					      =							torch.stack([example['mask'] for example in examples] )
		return {"pixel_values": pixel_values, "bool_masked_pos": mask}


def 					_a							(					)->	Optional[int]:
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		SCREAMING_SNAKE_CASE_					      =							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.
				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
		else:
				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							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_mim'    ,						lowerCAmelCase    ,						lowerCAmelCase )

		# 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()

		SCREAMING_SNAKE_CASE_					      =							training_args.get_process_log_level()
		logger.setLevel(lowerCAmelCase )
		transformers.utils.logging.set_verbosity(lowerCAmelCase )
		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.
		SCREAMING_SNAKE_CASE_					      =							None
		if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
				SCREAMING_SNAKE_CASE_					      =							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.' )

    # Initialize our dataset.
		SCREAMING_SNAKE_CASE_					      =							load_dataset(
		    data_args.dataset_name    ,						data_args.dataset_config_name    ,						data_files=data_args.data_files    ,						cache_dir=model_args.cache_dir    ,						use_auth_token=True if model_args.use_auth_token else None    ,						)

		# If we don't have a validation split, split off a percentage of train as validation.
		SCREAMING_SNAKE_CASE_					      =							None if 'validation' in ds.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split    ,						lowerCAmelCase ) and data_args.train_val_split > 0.0:
				SCREAMING_SNAKE_CASE_					      =							ds['train'].train_test_split(data_args.train_val_split )
				SCREAMING_SNAKE_CASE_					      =							split['train']
				SCREAMING_SNAKE_CASE_					      =							split['test']

		# Create config
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		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,
		}
		if model_args.config_name_or_path:
				SCREAMING_SNAKE_CASE_					      =							AutoConfig.from_pretrained(model_args.config_name_or_path    ,						**lowerCAmelCase )
		elif model_args.model_name_or_path:
				SCREAMING_SNAKE_CASE_					      =							AutoConfig.from_pretrained(model_args.model_name_or_path    ,						**lowerCAmelCase )
		else:
				SCREAMING_SNAKE_CASE_					      =							CONFIG_MAPPING[model_args.model_type]()
				logger.warning('You are instantiating a new config instance from scratch.' )
				if model_args.config_overrides is not None:
						logger.info(F'''Overriding config: {model_args.config_overrides}''' )
						config.update_from_string(model_args.config_overrides )
						logger.info(F'''New config: {config}''' )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
		if hasattr(lowerCAmelCase    ,						'decoder_type' ):
				SCREAMING_SNAKE_CASE_					      =							'simmim'

		# adapt config
		SCREAMING_SNAKE_CASE_					      =							model_args.image_size if model_args.image_size is not None else config.image_size
		SCREAMING_SNAKE_CASE_					      =							model_args.patch_size if model_args.patch_size is not None else config.patch_size
		SCREAMING_SNAKE_CASE_					      =							(
		    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
		)

		config.update(
		    {
		        'image_size': model_args.image_size,
		        'patch_size': model_args.patch_size,
		        'encoder_stride': model_args.encoder_stride,
		    } )

		# create image processor
		if model_args.image_processor_name:
				SCREAMING_SNAKE_CASE_					      =							AutoImageProcessor.from_pretrained(model_args.image_processor_name    ,						**lowerCAmelCase )
		elif model_args.model_name_or_path:
				SCREAMING_SNAKE_CASE_					      =							AutoImageProcessor.from_pretrained(model_args.model_name_or_path    ,						**lowerCAmelCase )
		else:
				SCREAMING_SNAKE_CASE_					      =							{
				    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
				}
				SCREAMING_SNAKE_CASE_					      =							IMAGE_PROCESSOR_TYPES[model_args.model_type]()

		# create model
		if model_args.model_name_or_path:
				SCREAMING_SNAKE_CASE_					      =							AutoModelForMaskedImageModeling.from_pretrained(
				    model_args.model_name_or_path    ,						from_tf=bool('.ckpt' in model_args.model_name_or_path )    ,						config=lowerCAmelCase    ,						cache_dir=model_args.cache_dir    ,						revision=model_args.model_revision    ,						use_auth_token=True if model_args.use_auth_token else None    ,						)
		else:
				logger.info('Training new model from scratch' )
				SCREAMING_SNAKE_CASE_					      =							AutoModelForMaskedImageModeling.from_config(lowerCAmelCase )

		if training_args.do_train:
				SCREAMING_SNAKE_CASE_					      =							ds['train'].column_names
		else:
				SCREAMING_SNAKE_CASE_					      =							ds['validation'].column_names

		if data_args.image_column_name is not None:
				SCREAMING_SNAKE_CASE_					      =							data_args.image_column_name
		elif "image" in column_names:
				SCREAMING_SNAKE_CASE_					      =							'image'
		elif "img" in column_names:
				SCREAMING_SNAKE_CASE_					      =							'img'
		else:
				SCREAMING_SNAKE_CASE_					      =							column_names[0]

		# transformations as done in original SimMIM paper
		# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
		SCREAMING_SNAKE_CASE_					      =							Compose(
		    [
		        Lambda(lambda lowerCAmelCase : img.convert('RGB' ) if img.mode != "RGB" else img ),
		        RandomResizedCrop(model_args.image_size    ,						scale=(0.6_7, 1.0)    ,						ratio=(3.0 / 4.0, 4.0 / 3.0) ),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        Normalize(mean=image_processor.image_mean    ,						std=image_processor.image_std ),
		    ] )

		# create mask generator
		SCREAMING_SNAKE_CASE_					      =							MaskGenerator(
		    input_size=model_args.image_size    ,						mask_patch_size=data_args.mask_patch_size    ,						model_patch_size=model_args.patch_size    ,						mask_ratio=data_args.mask_ratio    ,						)

		def preprocess_images(lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							[transforms(lowerCAmelCase ) for image in examples[image_column_name]]
				SCREAMING_SNAKE_CASE_					      =							[mask_generator() for i in range(len(examples[image_column_name] ) )]

				return examples

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

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

		# Initialize our trainer
		SCREAMING_SNAKE_CASE_					      =							Trainer(
		    model=lowerCAmelCase    ,						args=lowerCAmelCase    ,						train_dataset=ds['train'] if training_args.do_train else None    ,						eval_dataset=ds['validation'] if training_args.do_eval else None    ,						tokenizer=lowerCAmelCase    ,						data_collator=lowerCAmelCase    ,						)

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

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


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    lowercase_		:



												@staticmethod
												def 	__a					(			*snake_case__	:   List[Any]					,							**snake_case__	:   List[Any]  ):

														"""simple docstring"""

														pass



@is_pipeline_test
@require_vision
@require_torch
class    lowercase_		(unittest.TestCase ):
						lowerCAmelCase__			      =MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING



						def 	__a					(			self	:   Any					,							snake_case__	:   List[str]					,							snake_case__	:   int					,							snake_case__	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							pipeline(
								    'zero-shot-object-detection'					,							model='hf-internal-testing/tiny-random-owlvit-object-detection'  )

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



						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Union[str, Any]					,							snake_case__	:   Union[str, Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							object_detector(examples[0]					,							threshold=0.0  )

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



						@require_tf
						@unittest.skip('Zero Shot Object Detection not implemented in TF'  )
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								pass



						@require_torch
						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							pipeline(
								    'zero-shot-object-detection'					,							model='hf-internal-testing/tiny-random-owlvit-object-detection'  )

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

								self.assertEqual(
								    nested_simplify(snake_case__					,							decimals=4  )					,							[
								        {'score': 0.72_35, 'label': 'cat', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								        {'score': 0.72_18, 'label': 'remote', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								        {'score': 0.71_84, 'label': 'couch', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								        {'score': 0.67_48, 'label': 'remote', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								        {'score': 0.66_56, 'label': 'cat', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								        {'score': 0.66_14, 'label': 'couch', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								        {'score': 0.64_56, 'label': 'remote', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
								        {'score': 0.6_42, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 2_74, 'xmax': 93, 'ymax': 2_97}},
								        {'score': 0.64_19, 'label': 'cat', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
								    ]					,							)

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

								self.assertEqual(
								    nested_simplify(snake_case__					,							decimals=4  )					,							[
								        [
								            {'score': 0.72_35, 'label': 'cat', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								            {'score': 0.72_18, 'label': 'remote', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								            {'score': 0.71_84, 'label': 'couch', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								            {'score': 0.67_48, 'label': 'remote', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								            {'score': 0.66_56, 'label': 'cat', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								            {'score': 0.66_14, 'label': 'couch', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								            {'score': 0.64_56, 'label': 'remote', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
								            {'score': 0.6_42, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 2_74, 'xmax': 93, 'ymax': 2_97}},
								            {'score': 0.64_19, 'label': 'cat', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
								        ]
								    ]					,							)



						@require_torch
						@slow
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							pipeline('zero-shot-object-detection'  )

								SCREAMING_SNAKE_CASE_					      =							object_detector(
								    'http://images.cocodataset.org/val2017/000000039769.jpg'					,							candidate_labels=['cat', 'remote', 'couch']					,							)
								self.assertEqual(
								    nested_simplify(snake_case__					,							decimals=4  )					,							[
								        {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								        {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								        {'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
								        {'score': 0.14_74, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
								        {'score': 0.12_08, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
								    ]					,							)

								SCREAMING_SNAKE_CASE_					      =							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(snake_case__					,							decimals=4  )					,							[
								        [
								            {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								            {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								            {'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
								            {'score': 0.14_74, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
								            {'score': 0.12_08, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
								        ],
								        [
								            {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								            {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								            {'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
								            {'score': 0.14_74, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
								            {'score': 0.12_08, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
								        ],
								    ]					,							)



						@require_tf
						@unittest.skip('Zero Shot Object Detection not implemented in TF'  )
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								pass



						@require_torch
						@slow
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							0.2
								SCREAMING_SNAKE_CASE_					      =							pipeline('zero-shot-object-detection'  )

								SCREAMING_SNAKE_CASE_					      =							object_detector(
								    'http://images.cocodataset.org/val2017/000000039769.jpg'					,							candidate_labels=['cat', 'remote', 'couch']					,							threshold=snake_case__					,							)
								self.assertEqual(
								    nested_simplify(snake_case__					,							decimals=4  )					,							[
								        {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								        {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								        {'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
								    ]					,							)






						@require_torch
						@slow
						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							2
								SCREAMING_SNAKE_CASE_					      =							pipeline('zero-shot-object-detection'  )

								SCREAMING_SNAKE_CASE_					      =							object_detector(
								    'http://images.cocodataset.org/val2017/000000039769.jpg'					,							candidate_labels=['cat', 'remote', 'couch']					,							top_k=snake_case__					,							)
								self.assertEqual(
								    nested_simplify(snake_case__					,							decimals=4  )					,							[
								        {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								        {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								    ]					,							)