import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowercase_  							=			logging.get_logger(__name__)

lowercase_  							=			['UtteranceLevel']







@torch.no_grad()
def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					List[Any]		,							__SCREAMING_SNAKE_CASE       :					Any		,							__SCREAMING_SNAKE_CASE       :					int		,							__SCREAMING_SNAKE_CASE       :					Dict   ):







    '''simple docstring'''
    __snake_case       :				List[Any] 			=    torch.load(_UpperCamelCase		,							map_location="""cpu"""   )
    if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
        raise NotImplementedError(F'''The supported s3prl models are {SUPPORTED_MODELS}'''   )

    __snake_case       :				int 			=    checkpoint['''Downstream''']

    __snake_case       :				Tuple 			=    HubertConfig.from_pretrained(_UpperCamelCase   )
    __snake_case       :				Optional[int] 			=    HubertForSequenceClassification.from_pretrained(_UpperCamelCase		,							config=_UpperCamelCase   )
    __snake_case       :				Optional[int] 			=    WavaVecaFeatureExtractor.from_pretrained(
        _UpperCamelCase		,							return_attention_mask=_UpperCamelCase		,							do_normalize=_UpperCamelCase   )

    if hf_congfig.use_weighted_layer_sum:
        __snake_case       :				Union[str, Any] 			=    checkpoint['''Featurizer''']['''weights''']

    __snake_case       :				int 			=    downstream_dict['''projector.weight''']
    __snake_case       :				str 			=    downstream_dict['''projector.bias''']
    __snake_case       :				Dict 			=    downstream_dict['''model.post_net.linear.weight''']
    __snake_case       :				Union[str, Any] 			=    downstream_dict['''model.post_net.linear.bias''']

    hf_feature_extractor.save_pretrained(_UpperCamelCase   )
    hf_model.save_pretrained(_UpperCamelCase   )


if __name__ == "__main__":
     lowercase_  							=			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.")
     lowercase_  							=			parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)








import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


UpperCAmelCase__			:   str      			=    importlib.metadata.version('numpy')
UpperCAmelCase__			:   Optional[int]      			=    '.'.join([str(x) for x in sys.version_info[:3]])





class 							lowerCAmelCase_		(		lowercase_     ):







		def 					a_  (    self  :   Tuple					)       ->		Tuple:


						'''simple docstring'''







						require_version_core('''numpy<1000.4.5'''					)
						require_version_core('''numpy<1000.4'''					)
						require_version_core('''numpy<1000'''					)

						# le
						require_version_core('''numpy<=1000.4.5'''					)
						require_version_core(F'''numpy<={numpy_ver}'''					)

						# eq
						require_version_core(F'''numpy=={numpy_ver}'''					)

						# ne
						require_version_core('''numpy!=1000.4.5'''					)

						# ge
						require_version_core('''numpy>=1.0'''					)
						require_version_core('''numpy>=1.0.0'''					)
						require_version_core(F'''numpy>={numpy_ver}'''					)

						# gt
						require_version_core('''numpy>1.0.0'''					)

						# mix
						require_version_core('''numpy>1.0.0,<1000'''					)

						# requirement w/o version
						require_version_core('''numpy'''					)

						# unmet requirements due to version conflict
						for req in ["numpy==1.0.0", "numpy>=1000.0.0", F'''numpy<{numpy_ver}''']:
										try:
														require_version_core(UpperCAmelCase_					)
										except ImportError as e:
														self.assertIn(F'''{req} is required'''       ,		str(UpperCAmelCase_					)					)
														self.assertIn('''but found'''       ,		str(UpperCAmelCase_					)					)

        # unmet requirements due to missing module
						for req in ["numpipypie>1", "numpipypie2"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except importlib.metadata.PackageNotFoundError as e:
														self.assertIn(F'''The \'{req}\' distribution was not found and is required by this application'''       ,		str(UpperCAmelCase_					)					)
														self.assertIn('''Try: pip install transformers -U'''       ,		str(UpperCAmelCase_					)					)

        # bogus requirements formats:
        # 1. whole thing
						for req in ["numpy??1.0.0", "numpy1.0.0"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except ValueError as e:
														self.assertIn('''requirement needs to be in the pip package format'''       ,		str(UpperCAmelCase_					)					)
        # 2. only operators
						for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except ValueError as e:
														self.assertIn('''need one of '''       ,		str(UpperCAmelCase_					)					)







		def 					a_  (    self  :   Tuple					)       ->		List[Any]:


						'''simple docstring'''







						require_version('''python>=3.6.0'''					)

						# not matching requirements
						for req in ["python>9.9.9", "python<3.0.0"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except ImportError as e:
														self.assertIn(F'''{req} is required'''       ,		str(UpperCAmelCase_					)					)
														self.assertIn(F'''but found python=={python_ver}'''       ,		str(UpperCAmelCase_					)					)
