"""simple docstring"""







from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching



def     lowercase__( ):
     import os as original_os
     from os import path as original_path
     from os import rename as original_rename
     from os.path import dirname as original_dirname
     from os.path import join as original_join

     assert _test_patching.os is original_os
     assert _test_patching.path is original_path
     assert _test_patching.join is original_join

     assert _test_patching.renamed_os is original_os
     assert _test_patching.renamed_path is original_path
     assert _test_patching.renamed_join is original_join

     lowercase_			:       int									=			'__test_patch_submodule_mock__'
     with patch_submodule(_test_patching  ,    'os.path.join'  ,    __SCREAMING_SNAKE_CASE   ):
          # Every way to access os.path.join must be patched, and the rest must stay untouched

          # check os.path.join
          assert isinstance(_test_patching.os  ,    _PatchedModuleObj   )
          assert isinstance(_test_patching.os.path  ,    _PatchedModuleObj   )
          assert _test_patching.os.path.join is mock

          # check path.join
          assert isinstance(_test_patching.path  ,    _PatchedModuleObj   )
          assert _test_patching.path.join is mock

          # check join
          assert _test_patching.join is mock

          # check that the other attributes are untouched
          assert _test_patching.os.rename is original_rename
          assert _test_patching.path.dirname is original_dirname
          assert _test_patching.os.path.dirname is original_dirname

          # Even renamed modules or objects must be patched

          # check renamed_os.path.join
          assert isinstance(_test_patching.renamed_os  ,    _PatchedModuleObj   )
          assert isinstance(_test_patching.renamed_os.path  ,    _PatchedModuleObj   )
          assert _test_patching.renamed_os.path.join is mock

          # check renamed_path.join
          assert isinstance(_test_patching.renamed_path  ,    _PatchedModuleObj   )
          assert _test_patching.renamed_path.join is mock

          # check renamed_join
          assert _test_patching.renamed_join is mock

          # check that the other attributes are untouched
          assert _test_patching.renamed_os.rename is original_rename
          assert _test_patching.renamed_path.dirname is original_dirname
          assert _test_patching.renamed_os.path.dirname is original_dirname

     # check that everthing is back to normal when the patch is over

     assert _test_patching.os is original_os
     assert _test_patching.path is original_path
     assert _test_patching.join is original_join

     assert _test_patching.renamed_os is original_os
     assert _test_patching.renamed_path is original_path
     assert _test_patching.renamed_join is original_join



def     lowercase__( ):
     assert _test_patching.open is open

     lowercase_			:       Dict									=			'__test_patch_submodule_builtin_mock__'
     # _test_patching has "open" in its globals
     assert _test_patching.open is open
     with patch_submodule(_test_patching  ,    'open'  ,    __SCREAMING_SNAKE_CASE   ):
          assert _test_patching.open is mock

     # check that everthing is back to normal when the patch is over

     assert _test_patching.open is open



def     lowercase__( ):
     # pandas.read_csv is not present in _test_patching
     lowercase_			:       Any									=			'__test_patch_submodule_missing_mock__'
     with patch_submodule(_test_patching  ,    'pandas.read_csv'  ,    __SCREAMING_SNAKE_CASE   ):
          pass



def     lowercase__( ):
     # builtin should always be mocked even if they're not in the globals
     # in case they're loaded at one point
     lowercase_			:       int									=			'__test_patch_submodule_missing_builtin_mock__'
     # _test_patching doesn't have "len" in its globals
     assert getattr(_test_patching  ,    'len'  ,    __SCREAMING_SNAKE_CASE   ) is None
     with patch_submodule(_test_patching  ,    'len'  ,    __SCREAMING_SNAKE_CASE   ):
          assert _test_patching.len is mock
     assert _test_patching.len is len



def     lowercase__( ):
     lowercase_			:       Dict									=			'__test_patch_submodule_start_and_stop_mock__'
     lowercase_			:       Optional[int]									=			patch_submodule(_test_patching  ,    'open'  ,    __SCREAMING_SNAKE_CASE   )
     assert _test_patching.open is open
     patch.start()
     assert _test_patching.open is mock
     patch.stop()
     assert _test_patching.open is open



def     lowercase__( ):
     from os import rename as original_rename
     from os.path import dirname as original_dirname
     from os.path import join as original_join

     lowercase_			:       Optional[int]									=			'__test_patch_submodule_successive_join__'
     lowercase_			:       Dict									=			'__test_patch_submodule_successive_dirname__'
     lowercase_			:       List[Any]									=			'__test_patch_submodule_successive_rename__'
     assert _test_patching.os.path.join is original_join
     assert _test_patching.os.path.dirname is original_dirname
     assert _test_patching.os.rename is original_rename

     with patch_submodule(_test_patching  ,    'os.path.join'  ,    __SCREAMING_SNAKE_CASE   ):
          with patch_submodule(_test_patching  ,    'os.rename'  ,    __SCREAMING_SNAKE_CASE   ):
               with patch_submodule(_test_patching  ,    'os.path.dirname'  ,    __SCREAMING_SNAKE_CASE   ):
                    assert _test_patching.os.path.join is mock_join
                    assert _test_patching.os.path.dirname is mock_dirname
                    assert _test_patching.os.rename is mock_rename

    # try another order
     with patch_submodule(_test_patching  ,    'os.rename'  ,    __SCREAMING_SNAKE_CASE   ):
          with patch_submodule(_test_patching  ,    'os.path.join'  ,    __SCREAMING_SNAKE_CASE   ):
               with patch_submodule(_test_patching  ,    'os.path.dirname'  ,    __SCREAMING_SNAKE_CASE   ):
                    assert _test_patching.os.path.join is mock_join
                    assert _test_patching.os.path.dirname is mock_dirname
                    assert _test_patching.os.rename is mock_rename

     assert _test_patching.os.path.join is original_join
     assert _test_patching.os.path.dirname is original_dirname
     assert _test_patching.os.rename is original_rename







def     lowercase__( ):
     lowercase_			:       Optional[Any]									=			'__test_patch_submodule_doesnt_exist_mock__'
     with patch_submodule(_test_patching  ,    '__module_that_doesn_exist__.__attribute_that_doesn_exist__'  ,    __SCREAMING_SNAKE_CASE   ):
          pass
     with patch_submodule(_test_patching  ,    'os.__attribute_that_doesn_exist__'  ,    __SCREAMING_SNAKE_CASE   ):
          pass


"""simple docstring"""







import argparse

import torch

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


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



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       List[str]									=			UniSpeechSatForSequenceClassification.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       List[Any]									=			downstream_dict['projector.weight']
     lowercase_			:       Optional[Any]									=			downstream_dict['projector.bias']
     lowercase_			:       Optional[int]									=			downstream_dict['model.post_net.linear.weight']
     lowercase_			:       Tuple									=			downstream_dict['model.post_net.linear.bias']
     return model



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Tuple   ):
     lowercase_			:       List[Any]									=			UniSpeechSatForAudioFrameClassification.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Dict									=			downstream_dict['model.linear.weight']
     lowercase_			:       Union[str, Any]									=			downstream_dict['model.linear.bias']
     return model



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]   ):
     lowercase_			:       Any									=			UniSpeechSatForXVector.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       int									=			downstream_dict['connector.weight']
     lowercase_			:       List[str]									=			downstream_dict['connector.bias']
     for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
          lowercase_			:       Dict									=			downstream_dict[
              F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
          ]
          lowercase_			:       List[Any]									=			downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

     lowercase_			:       Optional[Any]									=			downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
     lowercase_			:       int									=			downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
     lowercase_			:       Dict									=			downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
     lowercase_			:       int									=			downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
     lowercase_			:       Dict									=			downstream_dict['objective.W']
     return model



@torch.no_grad()
def     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]   ):
     lowercase_			:       int									=			torch.load(__SCREAMING_SNAKE_CASE  ,    map_location='cpu'   )

     lowercase_			:       str									=			checkpoint['Downstream']

     lowercase_			:       str									=			UniSpeechSatConfig.from_pretrained(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Tuple									=			WavaVecaFeatureExtractor.from_pretrained(
         __SCREAMING_SNAKE_CASE  ,    return_attention_mask=__SCREAMING_SNAKE_CASE  ,    do_normalize=__SCREAMING_SNAKE_CASE   )

     lowercase_			:       List[str]									=			hf_config.architectures[0]
     if arch.endswith('ForSequenceClassification'   ):
          lowercase_			:       List[Any]									=			convert_classification(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     elif arch.endswith('ForAudioFrameClassification'   ):
          lowercase_			:       List[str]									=			convert_diarization(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     elif arch.endswith('ForXVector'   ):
          lowercase_			:       Optional[Any]									=			convert_xvector(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     else:
          raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''   )

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

     hf_feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE   )
     hf_model.save_pretrained(__SCREAMING_SNAKE_CASE   )


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

