




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE    :   Tuple       =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE    :   str       =["UtteranceLevel"]

@torch.no_grad()
def 			lowerCAmelCase_			(  lowerCamelCase:       Dict     ,				lowerCamelCase:       Union[str, Any]     ,				lowerCamelCase:       Tuple     ,				lowerCamelCase:       Tuple						)					->			Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__        =				torch.load(snake_case_     ,				map_location='cpu'						)
      if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
            raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

      lowerCamelCase__        =				checkpoint['''Downstream''']

      lowerCamelCase__        =				HubertConfig.from_pretrained(snake_case_						)
      lowerCamelCase__        =				HubertForSequenceClassification.from_pretrained(snake_case_     ,				config=snake_case_						)
      lowerCamelCase__        =				WavaVecaFeatureExtractor.from_pretrained(
          snake_case_     ,				return_attention_mask=snake_case_     ,				do_normalize=snake_case_						)

      if hf_congfig.use_weighted_layer_sum:
            lowerCamelCase__        =				checkpoint['''Featurizer''']['''weights''']

      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''']

      hf_feature_extractor.save_pretrained(snake_case_						)
      hf_model.save_pretrained(snake_case_						)


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE    :   Tuple       =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.""")
      __SCREAMING_SNAKE_CASE    :   List[Any]       =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


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






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     def  __snake_case   (    self  :   List[Any]			)				->       Optional[int]:
           # lt + different version strings
           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(A__			)
                 except ImportError as e:
                       self.assertIn(F"""{req} is required"""	,  str(A__			)			)
                       self.assertIn('but found'	,  str(A__			)			)

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

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




     def  __snake_case   (    self  :   str			)				->       Optional[int]:
           # matching requirement
           require_version('python>=3.6.0'			)

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