


"""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 			__UpperCAmelCase       (  *_a							, **_a      ):
								pass
@is_pipeline_test
@require_vision
@require_torch
class 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]										=				MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a      ):
				__a           =			pipeline(
				    '''zero-shot-object-detection'''							, model='''hf-internal-testing/tiny-random-owlvit-object-detection'''      )

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

		def 			__UpperCAmelCase       (  self							, _a							, _a      ):
				__a           =			object_detector(examples[0]							, threshold=0.0      )

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

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

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

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

				self.assertEqual(
				    nested_simplify(_a							, 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}},
				    ]							, )

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

				self.assertEqual(
				    nested_simplify(_a							, 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 			__UpperCAmelCase       (  self      ):
				__a           =			pipeline('''zero-shot-object-detection'''      )

				__a           =			object_detector(
				    '''http://images.cocodataset.org/val2017/000000039769.jpg'''							, candidate_labels=['''cat''', '''remote''', '''couch''']							, )
				self.assertEqual(
				    nested_simplify(_a							, 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}},
				    ]							, )

				__a           =			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(_a							, 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 			__UpperCAmelCase       (  self      ):
				pass

		@require_torch
		@slow
		def 			__UpperCAmelCase       (  self      ):
				__a           =			0.2
				__a           =			pipeline('''zero-shot-object-detection'''      )

				__a           =			object_detector(
				    '''http://images.cocodataset.org/val2017/000000039769.jpg'''							, candidate_labels=['''cat''', '''remote''', '''couch''']							, threshold=_a							, )
				self.assertEqual(
				    nested_simplify(_a							, 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 			__UpperCAmelCase       (  self      ):
				__a           =			2
				__a           =			pipeline('''zero-shot-object-detection'''      )

				__a           =			object_detector(
				    '''http://images.cocodataset.org/val2017/000000039769.jpg'''							, candidate_labels=['''cat''', '''remote''', '''couch''']							, top_k=_a							, )
				self.assertEqual(
				    nested_simplify(_a							, 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}},
				    ]							, )










"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase_			     =  {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "UniSpeechForCTC",
				    "UniSpeechForPreTraining",
				    "UniSpeechForSequenceClassification",
				    "UniSpeechModel",
				    "UniSpeechPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_unispeech import (
								    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
								    UniSpeechForCTC,
								    UniSpeechForPreTraining,
								    UniSpeechForSequenceClassification,
								    UniSpeechModel,
								    UniSpeechPreTrainedModel,
								)

else:
				import sys

				lowercase_			     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






