





import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


UpperCAmelCase__						:			Dict									=get_tests_dir('''fixtures''')




class       __A     (						unittest.TestCase    ):


				def _snake_case       (		self							):
								# A mock response for an HTTP head request to emulate server down
								lowerCamelCase	 =mock.Mock()
								lowerCamelCase	 =500
								lowerCamelCase	 ={}
								lowerCamelCase	 =HTTPError
								lowerCamelCase	 ={}

								# Download this model to make sure it's in the cache.
								lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained("""hf-internal-testing/tiny-random-wav2vec2"""							)
								# Under the mock environment we get a 500 error when trying to reach the model.
								with mock.patch("""requests.Session.request"""   , return_value=UpperCAmelCase_							) as mock_head:
												lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained("""hf-internal-testing/tiny-random-wav2vec2"""							)
												# This check we did call the fake head request
												mock_head.assert_called()


				def _snake_case       (		self							):
								# This test is for deprecated behavior and can be removed in v5
								lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained(
								    """https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"""							)




@is_staging_test
class       __A     (						unittest.TestCase    ):


				@classmethod
				def _snake_case       (		cls							):
								lowerCamelCase	 =TOKEN
								HfFolder.save_token(UpperCAmelCase_							)


				@classmethod
				def _snake_case       (		cls							):
								try:
												delete_repo(token=cls._token   , repo_id="""test-feature-extractor"""							)
								except HTTPError:
												pass

								try:
												delete_repo(token=cls._token   , repo_id="""valid_org/test-feature-extractor-org"""							)
								except HTTPError:
												pass

								try:
												delete_repo(token=cls._token   , repo_id="""test-dynamic-feature-extractor"""							)
								except HTTPError:
												pass


				def _snake_case       (		self							):
								lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase_							)
								feature_extractor.push_to_hub("""test-feature-extractor"""   , use_auth_token=self._token							)

								lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained(f"""{USER}/test-feature-extractor"""							)
								for k, v in feature_extractor.__dict__.items():
												self.assertEqual(UpperCAmelCase_   , getattr(UpperCAmelCase_   , UpperCAmelCase_							)							)

								# Reset repo
								delete_repo(token=self._token   , repo_id="""test-feature-extractor"""							)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
												feature_extractor.save_pretrained(
												    UpperCAmelCase_   , repo_id="""test-feature-extractor"""   , push_to_hub=UpperCAmelCase_   , use_auth_token=self._token							)

								lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained(f"""{USER}/test-feature-extractor"""							)
								for k, v in feature_extractor.__dict__.items():
												self.assertEqual(UpperCAmelCase_   , getattr(UpperCAmelCase_   , UpperCAmelCase_							)							)


				def _snake_case       (		self							):
								lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase_							)
								feature_extractor.push_to_hub("""valid_org/test-feature-extractor"""   , use_auth_token=self._token							)

								lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained("""valid_org/test-feature-extractor"""							)
								for k, v in feature_extractor.__dict__.items():
												self.assertEqual(UpperCAmelCase_   , getattr(UpperCAmelCase_   , UpperCAmelCase_							)							)

								# Reset repo
								delete_repo(token=self._token   , repo_id="""valid_org/test-feature-extractor"""							)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
												feature_extractor.save_pretrained(
												    UpperCAmelCase_   , repo_id="""valid_org/test-feature-extractor-org"""   , push_to_hub=UpperCAmelCase_   , use_auth_token=self._token							)

								lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained("""valid_org/test-feature-extractor-org"""							)
								for k, v in feature_extractor.__dict__.items():
												self.assertEqual(UpperCAmelCase_   , getattr(UpperCAmelCase_   , UpperCAmelCase_							)							)


				def _snake_case       (		self							):
								CustomFeatureExtractor.register_for_auto_class()
								lowerCamelCase	 =CustomFeatureExtractor.from_pretrained(UpperCAmelCase_							)

								feature_extractor.push_to_hub("""test-dynamic-feature-extractor"""   , use_auth_token=self._token							)

								# This has added the proper auto_map field to the config
								self.assertDictEqual(
								    feature_extractor.auto_map   , {"""AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor"""}   , )

								lowerCamelCase	 =AutoFeatureExtractor.from_pretrained(
								    f"""{USER}/test-dynamic-feature-extractor"""   , trust_remote_code=UpperCAmelCase_							)
								# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
								self.assertEqual(new_feature_extractor.__class__.__name__   , """CustomFeatureExtractor"""							)












import argparse

import torch

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


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





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Any:
				lowerCamelCase	 =UniSpeechSatForSequenceClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""projector.weight"""]
				lowerCamelCase	 =downstream_dict["""projector.bias"""]
				lowerCamelCase	 =downstream_dict["""model.post_net.linear.weight"""]
				lowerCamelCase	 =downstream_dict["""model.post_net.linear.bias"""]
				return model





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  int:
				lowerCamelCase	 =UniSpeechSatForAudioFrameClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""model.linear.weight"""]
				lowerCamelCase	 =downstream_dict["""model.linear.bias"""]
				return model





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Dict:
				lowerCamelCase	 =UniSpeechSatForXVector.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""connector.weight"""]
				lowerCamelCase	 =downstream_dict["""connector.bias"""]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
								lowerCamelCase	 =downstream_dict[
								    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
								]
								lowerCamelCase	 =downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
				lowerCamelCase	 =downstream_dict["""objective.W"""]
				return model





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  str:
				lowerCamelCase	 =torch.load(_UpperCAmelCase			,      map_location="""cpu"""				)

				lowerCamelCase	 =checkpoint["""Downstream"""]

				lowerCamelCase	 =UniSpeechSatConfig.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained(
				    _UpperCAmelCase			,      return_attention_mask=_UpperCAmelCase			,      do_normalize=_UpperCAmelCase				)

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

				if hf_config.use_weighted_layer_sum:
								lowerCamelCase	 =checkpoint["""Featurizer"""]["""weights"""]

				hf_feature_extractor.save_pretrained(_UpperCAmelCase				)
				hf_model.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
							UpperCAmelCase__						:			Dict									=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__						:			List[Any]									=parser.parse_args()
							convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





