


"""simple docstring"""


import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase						:				int             =      logging.get_logger(__name__)







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		List[Any]:







		"""simple docstring"""





		A					       = UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase_ ,			config=lowerCamelCase_	)
		A					       = downstream_dict['projector.weight']
		A					       = downstream_dict['projector.bias']
		A					       = downstream_dict['model.post_net.linear.weight']
		A					       = downstream_dict['model.post_net.linear.bias']
		return model







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		A					       = UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase_ ,			config=lowerCamelCase_	)
		A					       = downstream_dict['model.linear.weight']
		A					       = downstream_dict['model.linear.bias']
		return model







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		Tuple:







		"""simple docstring"""





		A					       = UniSpeechSatForXVector.from_pretrained(lowerCamelCase_ ,			config=lowerCamelCase_	)
		A					       = downstream_dict['connector.weight']
		A					       = downstream_dict['connector.bias']
		for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
				A					       = downstream_dict[
				    f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
				]
				A					       = downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

		A					       = downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
		A					       = downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
		A					       = downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
		A					       = downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
		A					       = downstream_dict['objective.W']
		return model







@torch.no_grad()
def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		Dict:







		"""simple docstring"""





		A					       = torch.load(lowerCamelCase_ ,			map_location='cpu'	)

		A					       = checkpoint['Downstream']

		A					       = UniSpeechSatConfig.from_pretrained(lowerCamelCase_	)
		A					       = WavaVecaFeatureExtractor.from_pretrained(
		    lowerCamelCase_ ,			return_attention_mask=lowerCamelCase_ ,			do_normalize=lowerCamelCase_	)

		A					       = hf_config.architectures[0]
		if arch.endswith('ForSequenceClassification'	):
				A					       = convert_classification(lowerCamelCase_ ,			lowerCamelCase_ ,			lowerCamelCase_	)
		elif arch.endswith('ForAudioFrameClassification'	):
				A					       = convert_diarization(lowerCamelCase_ ,			lowerCamelCase_ ,			lowerCamelCase_	)
		elif arch.endswith('ForXVector'	):
				A					       = convert_xvector(lowerCamelCase_ ,			lowerCamelCase_ ,			lowerCamelCase_	)
		else:
				raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'	)

		if hf_config.use_weighted_layer_sum:
				A					       = checkpoint['Featurizer']['weights']

		hf_feature_extractor.save_pretrained(lowerCamelCase_	)
		hf_model.save_pretrained(lowerCamelCase_	)


if __name__ == "__main__":
	__UpperCamelCase						:				List[Any]             =      argparse.ArgumentParser()
	parser.add_argument(
	    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
	)
	parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
	parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
	parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
	__UpperCamelCase						:				Optional[int]             =      parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)









"""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__						:







							@staticmethod
							def __a						(							*_lowercase   :    int			,			**_lowercase   :    Optional[int]		):
									pass



@is_pipeline_test
@require_vision
@require_torch
class 			lowerCamelCase__						(   unittest.TestCase			):
						lowerCAmelCase       =     MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING







						def __a						(							self   :    int			,			_lowercase   :    Optional[Any]			,			_lowercase   :    int			,			_lowercase   :    Union[str, Any]		):
								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 __a						(							self   :    Tuple			,			_lowercase   :    int			,			_lowercase   :    Tuple		):
								A					       = object_detector(examples[0]			,			threshold=0.0		)

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







						@require_tf
						@unittest.skip('Zero Shot Object Detection not implemented in TF'		)
						def __a						(							self   :    Optional[int]		):
								pass







						@require_torch
						def __a						(							self   :    Optional[Any]		):
								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=0.6_4			,			)

								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        {'score': 0.7_2_3_5, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								        {'score': 0.7_2_1_8, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								        {'score': 0.7_1_8_4, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								        {'score': 0.6_7_4_8, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								        {'score': 0.6_6_5_6, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								        {'score': 0.6_6_1_4, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								        {'score': 0.6_4_5_6, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
								        {'score': 0.6_4_2, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
								        {'score': 0.6_4_1_9, '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=0.6_4			,			)

								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        [
								            {'score': 0.7_2_3_5, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								            {'score': 0.7_2_1_8, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								            {'score': 0.7_1_8_4, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								            {'score': 0.6_7_4_8, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								            {'score': 0.6_6_5_6, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								            {'score': 0.6_6_1_4, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								            {'score': 0.6_4_5_6, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
								            {'score': 0.6_4_2, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
								            {'score': 0.6_4_1_9, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
								        ]
								    ]			,			)







						@require_torch
						@slow
						def __a						(							self   :    List[str]		):
								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(_lowercase			,			decimals=4		)			,			[
								        {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
								        {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
								        {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
								        {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
								        {'score': 0.1_2_0_8, '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(_lowercase			,			decimals=4		)			,			[
								        [
								            {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
								            {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
								            {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
								            {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
								            {'score': 0.1_2_0_8, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
								        ],
								        [
								            {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
								            {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
								            {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
								            {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
								            {'score': 0.1_2_0_8, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
								        ],
								    ]			,			)







						@require_tf
						@unittest.skip('Zero Shot Object Detection not implemented in TF'		)
						def __a						(							self   :    Optional[int]		):
								pass







						@require_torch
						@slow
						def __a						(							self   :    Optional[Any]		):
								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=_lowercase			,			)
								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
								        {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
								        {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
								    ]			,			)







						@require_torch
						@slow
						def __a						(							self   :    Optional[int]		):
								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=_lowercase			,			)
								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
								        {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
								    ]			,			)





