


from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)









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    :





											"""simple docstring"""







											@staticmethod
											def 					UpperCamelCase__     (      *lowerCAmelCase__			,						**lowerCAmelCase__       ):





														"""simple docstring"""




														pass







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





							"""simple docstring"""







							_UpperCAmelCase      =						MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




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

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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




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

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






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





										"""simple docstring"""




										pass






							@require_torch
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




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

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

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,						decimals=4       )			,						[
										        {'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
										        {'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
										        {'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
										        {'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
										        {'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
										        {'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
										        {'score': 0.6_456, 'label': 'remote', 'box': {'xmin': 4_9_4, 'ymin': 1_0_5, 'xmax': 5_2_1, 'ymax': 1_2_7}},
										        {'score': 0.642, 'label': 'remote', 'box': {'xmin': 6_7, 'ymin': 2_7_4, 'xmax': 9_3, 'ymax': 2_9_7}},
										        {'score': 0.6_419, 'label': 'cat', 'box': {'xmin': 4_9_4, 'ymin': 1_0_5, 'xmax': 5_2_1, 'ymax': 1_2_7}},
										    ]			,						)

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

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,						decimals=4       )			,						[
										        [
										            {'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
										            {'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
										            {'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
										            {'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
										            {'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
										            {'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
										            {'score': 0.6_456, 'label': 'remote', 'box': {'xmin': 4_9_4, 'ymin': 1_0_5, 'xmax': 5_2_1, 'ymax': 1_2_7}},
										            {'score': 0.642, 'label': 'remote', 'box': {'xmin': 6_7, 'ymin': 2_7_4, 'xmax': 9_3, 'ymax': 2_9_7}},
										            {'score': 0.6_419, 'label': 'cat', 'box': {'xmin': 4_9_4, 'ymin': 1_0_5, 'xmax': 5_2_1, 'ymax': 1_2_7}},
										        ]
										    ]			,						)






							@require_torch
							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					pipeline('zero-shot-object-detection'       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					object_detector(
										    'http://images.cocodataset.org/val2017/000000039769.jpg'			,						candidate_labels=['cat', 'remote', 'couch']			,						)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,						decimals=4       )			,						[
										        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
										        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
										        {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 5_5, 'xmax': 3_1_5, 'ymax': 4_7_2}},
										        {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 3_3_5, 'ymin': 7_4, 'xmax': 3_7_1, 'ymax': 1_8_7}},
										        {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_4_2, 'ymax': 4_7_6}},
										    ]			,						)

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					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(lowerCAmelCase__			,						decimals=4       )			,						[
										        [
										            {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
										            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
										            {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 5_5, 'xmax': 3_1_5, 'ymax': 4_7_2}},
										            {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 3_3_5, 'ymin': 7_4, 'xmax': 3_7_1, 'ymax': 1_8_7}},
										            {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_4_2, 'ymax': 4_7_6}},
										        ],
										        [
										            {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
										            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
										            {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 5_5, 'xmax': 3_1_5, 'ymax': 4_7_2}},
										            {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 3_3_5, 'ymin': 7_4, 'xmax': 3_7_1, 'ymax': 1_8_7}},
										            {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_4_2, 'ymax': 4_7_6}},
										        ],
										    ]			,						)






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





										"""simple docstring"""




										pass






							@require_torch
							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




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

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					object_detector(
										    'http://images.cocodataset.org/val2017/000000039769.jpg'			,						candidate_labels=['cat', 'remote', 'couch']			,						threshold=lowerCAmelCase__			,						)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,						decimals=4       )			,						[
										        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
										        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
										        {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 5_5, 'xmax': 3_1_5, 'ymax': 4_7_2}},
										    ]			,						)






							@require_torch
							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




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

										SCREAMING_SNAKE_CASE_		:     Tuple					=					object_detector(
										    'http://images.cocodataset.org/val2017/000000039769.jpg'			,						candidate_labels=['cat', 'remote', 'couch']			,						top_k=lowerCAmelCase__			,						)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,						decimals=4       )			,						[
										        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
										        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
										    ]			,						)





