

import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


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

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     List[Any]     ,					lowerCAmelCase:     int     ,					lowerCAmelCase:     Union[str, Any]       )      ->				Union[str, Any]:
				_UpperCAmelCase				:						Dict       						=			WavLMForSequenceClassification.from_pretrained(lowerCAmelCase     ,					config=lowerCAmelCase       )
				_UpperCAmelCase				:						List[Any]       						=			downstream_dict["projector.weight"]
				_UpperCAmelCase				:						Optional[Any]       						=			downstream_dict["projector.bias"]
				_UpperCAmelCase				:						Union[str, Any]       						=			downstream_dict["model.post_net.linear.weight"]
				_UpperCAmelCase				:						Optional[Any]       						=			downstream_dict["model.post_net.linear.bias"]
				return model

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Optional[Any]     ,					lowerCAmelCase:     Union[str, Any]     ,					lowerCAmelCase:     Optional[Any]       )      ->				Dict:
				_UpperCAmelCase				:						Optional[int]       						=			WavLMForAudioFrameClassification.from_pretrained(lowerCAmelCase     ,					config=lowerCAmelCase       )
				_UpperCAmelCase				:						int       						=			downstream_dict["model.linear.weight"]
				_UpperCAmelCase				:						str       						=			downstream_dict["model.linear.bias"]
				return model

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Optional[Any]     ,					lowerCAmelCase:     Union[str, Any]     ,					lowerCAmelCase:     Optional[Any]       )      ->				Optional[int]:
				_UpperCAmelCase				:						str       						=			WavLMForXVector.from_pretrained(lowerCAmelCase     ,					config=lowerCAmelCase       )
				_UpperCAmelCase				:						Tuple       						=			downstream_dict["connector.weight"]
				_UpperCAmelCase				:						str       						=			downstream_dict["connector.bias"]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
								_UpperCAmelCase				:						List[str]       						=			downstream_dict[
								    F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
								]
								_UpperCAmelCase				:						int       						=			downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

				_UpperCAmelCase				:						Tuple       						=			downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
				_UpperCAmelCase				:						Dict       						=			downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
				_UpperCAmelCase				:						Optional[Any]       						=			downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
				_UpperCAmelCase				:						Tuple       						=			downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
				_UpperCAmelCase				:						Optional[int]       						=			downstream_dict["objective.W"]
				return model

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Tuple     ,					lowerCAmelCase:     str     ,					lowerCAmelCase:     Dict     ,					lowerCAmelCase:     int       )      ->				Optional[Any]:
				_UpperCAmelCase				:						str       						=			torch.load(lowerCAmelCase     ,					map_location="cpu"       )

				_UpperCAmelCase				:						Any       						=			checkpoint["Downstream"]

				_UpperCAmelCase				:						Optional[int]       						=			WavLMConfig.from_pretrained(lowerCAmelCase       )
				_UpperCAmelCase				:						int       						=			WavaVecaFeatureExtractor.from_pretrained(
				    lowerCAmelCase     ,					return_attention_mask=lowerCAmelCase     ,					do_normalize=lowerCAmelCase       )

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

				if hf_config.use_weighted_layer_sum:
								_UpperCAmelCase				:						List[str]       						=			checkpoint["Featurizer"]["weights"]

				hf_feature_extractor.save_pretrained(lowerCAmelCase       )
				hf_model.save_pretrained(lowerCAmelCase       )


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)



'''simple docstring'''




import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					str    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Union[str, Any]:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Tuple    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Union[str, Any]:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[Any]    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Dict:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[Any]    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 5]    ,     _SCREAMING_SNAKE_CASE=[5, 4]    ,     )		->     List[str]:
           pass




class 	__UpperCAmelCase				(				unittest.TestCase     ):





       '''simple docstring'''



       def 					__A       (      self )		->     Optional[int]:
           with self.assertRaises(_SCREAMING_SNAKE_CASE ):
               ConfigMixin.load_config('''dummy_path''' )

       def 					__A       (      self )		->     Union[str, Any]:
           A_     =   SampleObject()
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # init ignore private arguments
           A_     =   SampleObject(_name_or_path='''lalala''' )
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # can override default
           A_     =   SampleObject(c=6 )
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == 6
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # can use positional arguments.
           A_     =   SampleObject(1    ,     c=6 )
           A_     =   obj.config
           assert config["a"] == 1
           assert config["b"] == 5
           assert config["c"] == 6
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

       def 					__A       (      self )		->     int:
           A_     =   SampleObject()
           A_     =   obj.config

           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           with tempfile.TemporaryDirectory() as tmpdirname:
               obj.save_config(_SCREAMING_SNAKE_CASE )
               A_     =   SampleObject.from_config(SampleObject.load_config(_SCREAMING_SNAKE_CASE ) )
               A_     =   new_obj.config

           # unfreeze configs
           A_     =   dict(_SCREAMING_SNAKE_CASE )
           A_     =   dict(_SCREAMING_SNAKE_CASE )

           assert config.pop('''c''' ) == (2, 5)  # instantiated as tuple
           assert new_config.pop('''c''' ) == [2, 5]  # saved & loaded as list because of json
           config.pop('''_use_default_values''' )
           assert config == new_config

       def 					__A       (      self )		->     List[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DDIMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert ddim.__class__ == DDIMScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     List[str]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   EulerDiscreteScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert euler.__class__ == EulerDiscreteScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   EulerAncestralDiscreteScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert euler.__class__ == EulerAncestralDiscreteScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[int]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   PNDMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert pndm.__class__ == PNDMScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Tuple:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DDPMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler'''    ,     prediction_type='''sample'''    ,     beta_end=8    ,     )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger_a:
               A_     =   DDPMScheduler.from_pretrained('''google/ddpm-celebahq-256'''    ,     beta_start=88 )

           assert ddpm.__class__ == DDPMScheduler
           assert ddpm.config.prediction_type == "sample"
           assert ddpm.config.beta_end == 8
           assert ddpm_a.config.beta_start == 88

           # no warning should be thrown
           assert cap_logger.out == ""
           assert cap_logger_a.out == ""

       def 					__A       (      self )		->     List[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DPMSolverMultistepScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert dpm.__class__ == DPMSolverMultistepScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[int]:
           # let's first save a config that should be in the form
           #    a=2,
           #    b=5,
           #    c=(2, 5),
           #    d="for diffusion",
           #    e=[1, 3],

           A_     =   SampleObject()

           A_     =   {k: v for k, v in config.config.items() if not k.startswith('''_''' )}

           # make sure that default config has all keys in `_use_default_values`
           assert set(config_dict.keys() ) == set(config.config._use_default_values )

           with tempfile.TemporaryDirectory() as tmpdirname:
               config.save_config(_SCREAMING_SNAKE_CASE )

               # now loading it with SampleObject2 should put f into `_use_default_values`
               A_     =   SampleObjecta.from_config(_SCREAMING_SNAKE_CASE )

               assert "f" in config._use_default_values
               assert config.f == [1, 3]

           # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
           # **BECAUSE** it is part of `config._use_default_values`
           A_     =   SampleObjecta.from_config(config.config )
           assert new_config.f == [5, 4]

           config.config._use_default_values.pop()
           A_     =   SampleObjecta.from_config(config.config )
           assert new_config_a.f == [1, 3]

           # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
           assert new_config_a.e == [1, 3]






