

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 		A__					(				*__snake_case	,       **__snake_case):
										pass







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


		"""simple docstring"""




		a__					       =  MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Optional[Any]			=						pipeline(
			    'zero-shot-object-detection'	,       model='hf-internal-testing/tiny-random-owlvit-object-detection')

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


		def 		A__					(				self	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		int			=						object_detector(examples[0]	,       threshold=0.0)

			_UpperCamelCase   :		Tuple			=						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):
			pass


		@require_torch
		def 		A__					(				self):
			_UpperCamelCase   :		str			=						pipeline(
			    'zero-shot-object-detection'	,       model='hf-internal-testing/tiny-random-owlvit-object-detection')

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

			self.assertEqual(
			    nested_simplify(__snake_case	,       decimals=4)	,       [
			        {'score': 0.7_2_3_5, 'label': 'cat', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
			        {'score': 0.7_2_1_8, 'label': 'remote', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
			        {'score': 0.7_1_8_4, 'label': 'couch', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
			        {'score': 0.6_7_4_8, 'label': 'remote', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
			        {'score': 0.6_6_5_6, 'label': 'cat', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
			        {'score': 0.6_6_1_4, 'label': 'couch', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
			        {'score': 0.6_4_5_6, 'label': 'remote', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
			        {'score': 0.6_4_2, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 2_74, 'xmax': 93, 'ymax': 2_97}},
			        {'score': 0.6_4_1_9, 'label': 'cat', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
			    ]	,       )

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

			self.assertEqual(
			    nested_simplify(__snake_case	,       decimals=4)	,       [
			        [
			            {'score': 0.7_2_3_5, 'label': 'cat', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
			            {'score': 0.7_2_1_8, 'label': 'remote', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
			            {'score': 0.7_1_8_4, 'label': 'couch', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
			            {'score': 0.6_7_4_8, 'label': 'remote', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
			            {'score': 0.6_6_5_6, 'label': 'cat', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
			            {'score': 0.6_6_1_4, 'label': 'couch', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
			            {'score': 0.6_4_5_6, 'label': 'remote', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
			            {'score': 0.6_4_2, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 2_74, 'xmax': 93, 'ymax': 2_97}},
			            {'score': 0.6_4_1_9, 'label': 'cat', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
			        ]
			    ]	,       )


		@require_torch
		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Any			=						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(__snake_case	,       decimals=4)	,       [
			        {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
			        {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
			        {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
			        {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
			        {'score': 0.1_2_0_8, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
			    ]	,       )

			_UpperCamelCase   :		int			=						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': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
			            {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
			            {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
			            {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
			            {'score': 0.1_2_0_8, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
			        ],
			        [
			            {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
			            {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
			            {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
			            {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
			            {'score': 0.1_2_0_8, '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):
			pass


		@require_torch
		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						0.2
			_UpperCamelCase   :		Dict			=						pipeline('zero-shot-object-detection')

			_UpperCamelCase   :		List[Any]			=						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': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
			        {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
			        {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
			    ]	,       )






		@require_torch
		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						2
			_UpperCamelCase   :		List[str]			=						pipeline('zero-shot-object-detection')

			_UpperCamelCase   :		Any			=						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': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
			        {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
			    ]	,       )







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		{
    """facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "vit_mae"


		def __init__(				self	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.0	,       __snake_case=0.0	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=2_24	,       __snake_case=16	,       __snake_case=3	,       __snake_case=True	,       __snake_case=16	,       __snake_case=5_12	,       __snake_case=8	,       __snake_case=20_48	,       __snake_case=0.7_5	,       __snake_case=False	,       **__snake_case	,       ):
			super().__init__(**__snake_case)

			_UpperCamelCase   :		Optional[int]			=						hidden_size
			_UpperCamelCase   :		Optional[int]			=						num_hidden_layers
			_UpperCamelCase   :		Tuple			=						num_attention_heads
			_UpperCamelCase   :		List[str]			=						intermediate_size
			_UpperCamelCase   :		str			=						hidden_act
			_UpperCamelCase   :		List[str]			=						hidden_dropout_prob
			_UpperCamelCase   :		List[Any]			=						attention_probs_dropout_prob
			_UpperCamelCase   :		str			=						initializer_range
			_UpperCamelCase   :		Any			=						layer_norm_eps
			_UpperCamelCase   :		int			=						image_size
			_UpperCamelCase   :		Any			=						patch_size
			_UpperCamelCase   :		List[Any]			=						num_channels
			_UpperCamelCase   :		Union[str, Any]			=						qkv_bias
			_UpperCamelCase   :		str			=						decoder_num_attention_heads
			_UpperCamelCase   :		Union[str, Any]			=						decoder_hidden_size
			_UpperCamelCase   :		Union[str, Any]			=						decoder_num_hidden_layers
			_UpperCamelCase   :		Any			=						decoder_intermediate_size
			_UpperCamelCase   :		int			=						mask_ratio
			_UpperCamelCase   :		List[Any]			=						norm_pix_loss




