


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}},
										    ]			,						)









'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


__A   :    str											= {
    "configuration_audio_spectrogram_transformer": [
        "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ASTConfig",
    ]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A   :    List[str]											= [
      "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ASTForAudioClassification",
      "ASTModel",
      "ASTPreTrainedModel",
  ]

try:
  if not is_speech_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A   :    Union[str, Any]											= ["ASTFeatureExtractor"]

if TYPE_CHECKING:
  from .configuration_audio_spectrogram_transformer import (
      AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      ASTConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_audio_spectrogram_transformer import (
        AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        ASTForAudioClassification,
        ASTModel,
        ASTPreTrainedModel,
    )

  try:
    if not is_speech_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
  import sys

  __A   :    List[Any]											= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



