


import argparse

import torch

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


logging.set_verbosity_info()
lowerCAmelCase__      		= logging.get_logger(__name__)
def 							_UpperCAmelCase				(UpperCamelCase__     :  Dict				,							UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  Dict						):
       _A      :       List[Any]				    = UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase__				,							config=UpperCamelCase__						)
       _A      :       Optional[Any]				    = downstream_dict["projector.weight"]
       _A      :       List[Any]				    = downstream_dict["projector.bias"]
       _A      :       int				    = downstream_dict["model.post_net.linear.weight"]
       _A      :       Optional[int]				    = downstream_dict["model.post_net.linear.bias"]
       return model
def 							_UpperCAmelCase				(UpperCamelCase__     :  Tuple				,							UpperCamelCase__     :  List[Any]				,							UpperCamelCase__     :  List[str]						):
       _A      :       Tuple				    = UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase__				,							config=UpperCamelCase__						)
       _A      :       Optional[Any]				    = downstream_dict["model.linear.weight"]
       _A      :       Optional[int]				    = downstream_dict["model.linear.bias"]
       return model
def 							_UpperCAmelCase				(UpperCamelCase__     :  Union[str, Any]				,							UpperCamelCase__     :  List[Any]				,							UpperCamelCase__     :  Optional[Any]						):
       _A      :       List[Any]				    = UniSpeechSatForXVector.from_pretrained(UpperCamelCase__				,							config=UpperCamelCase__						)
       _A      :       str				    = downstream_dict["connector.weight"]
       _A      :       Optional[int]				    = downstream_dict["connector.bias"]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
              _A      :       Union[str, Any]				    = downstream_dict[
                  f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
              ]
              _A      :       Tuple				    = downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

       _A      :       Union[str, Any]				    = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
       _A      :       Dict				    = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
       _A      :       str				    = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
       _A      :       Union[str, Any]				    = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
       _A      :       int				    = downstream_dict["objective.W"]
       return model
@torch.no_grad()
def 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  Dict						):
       _A      :       List[Any]				    = torch.load(UpperCamelCase__				,							map_location="cpu"						)

       _A      :       List[str]				    = checkpoint["Downstream"]

       _A      :       Union[str, Any]				    = UniSpeechSatConfig.from_pretrained(UpperCamelCase__						)
       _A      :       int				    = WavaVecaFeatureExtractor.from_pretrained(
           UpperCamelCase__				,							return_attention_mask=UpperCamelCase__				,							do_normalize=UpperCamelCase__						)

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

       if hf_config.use_weighted_layer_sum:
              _A      :       Optional[int]				    = checkpoint["Featurizer"]["weights"]

       hf_feature_extractor.save_pretrained(UpperCamelCase__						)
       hf_model.save_pretrained(UpperCamelCase__						)


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

from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml






class 		a__						(					yaml.SafeLoader					):

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    [self.constructed_objects[key_node] for key_node, _ in node.value]
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    [tuple(_A    ) if isinstance(_A,_A    ) else key for key in keys]
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    Counter(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    [key for key in counter if counter[key] > 1]
							if duplicate_keys:
									raise TypeError(F'Got duplicate yaml keys: {duplicate_keys}'    )






					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       Dict,_A      :       List[Any]=False    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    super().construct_mapping(_A,deep=_A    )
							self._check_no_duplicates_on_constructed_node(_A    )
							return mapping







def       _snake_case	(   lowerCAmelCase							:   str	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		str				      =    list(readme_content.splitlines()	)
		if full_content and full_content[0] == "---" and "---" in full_content[1:]:
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    full_content[1:].index("---"	) + 1
				SCREAMING_SNAKE_CASE_  :		int				      =    "\n".join(full_content[1:sep_idx]	)
				return yamlblock, "\n".join(full_content[sep_idx + 1 :]	)

		return None, "\n".join(lowerCAmelCase	)






class 		a__						(					A__					):
					# class attributes
					A	       =							{'train_eval_index'}  # train-eval-index in the YAML metadata

					@classmethod
					def 				__UpperCamelCase   (    cls      :       Any,_A      :       Path    ):







							"""simple docstring"""






							with open(_A,encoding="utf-8"    ) as readme_file:
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Dict				      =    _split_yaml_from_readme(readme_file.read()    )
							if yaml_string is not None:
									return cls.from_yaml_string(_A    )
							else:
									return cls()

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Path    ):







							"""simple docstring"""






							if path.exists():
									with open(_A,encoding="utf-8"    ) as readme_file:
											SCREAMING_SNAKE_CASE_  :		int				      =    readme_file.read()
							else:
									SCREAMING_SNAKE_CASE_  :		Any				      =    None
							SCREAMING_SNAKE_CASE_  :		int				      =    self._to_readme(_A    )
							with open(_A,"w",encoding="utf-8"    ) as readme_file:
									readme_file.write(_A    )

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       Optional[str] = None    ):







							"""simple docstring"""






							if readme_content is not None:
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    _split_yaml_from_readme(_A    )
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    "---\n" + self.to_yaml_string() + "---\n" + content
							else:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    "---\n" + self.to_yaml_string() + "---\n"
							return full_content

					@classmethod
					def 				__UpperCamelCase   (    cls      :       Dict,_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    yaml.load(_A,Loader=_NoDuplicateSafeLoader    ) or {}

							# Convert the YAML keys to DatasetMetadata fields
							SCREAMING_SNAKE_CASE_  :		Any				      =    {
							    (key.replace("-","_"    ) if key.replace("-","_"    ) in cls._FIELDS_WITH_DASHES else key): value
							    for key, value in metadata_dict.items()
							}
							return cls(**_A    )






					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							return yaml.safe_dump(
							    {
							        (key.replace("_","-"    ) if key in self._FIELDS_WITH_DASHES else key): value
							        for key, value in self.items()
							    },sort_keys=_A,allow_unicode=_A,encoding="utf-8",).decode("utf-8"    )







__lowerCamelCase					:	List[Any]       =  {
    '''image-classification''': [],
    '''translation''': [],
    '''image-segmentation''': [],
    '''fill-mask''': [],
    '''automatic-speech-recognition''': [],
    '''token-classification''': [],
    '''sentence-similarity''': [],
    '''audio-classification''': [],
    '''question-answering''': [],
    '''summarization''': [],
    '''zero-shot-classification''': [],
    '''table-to-text''': [],
    '''feature-extraction''': [],
    '''other''': [],
    '''multiple-choice''': [],
    '''text-classification''': [],
    '''text-to-image''': [],
    '''text2text-generation''': [],
    '''zero-shot-image-classification''': [],
    '''tabular-classification''': [],
    '''tabular-regression''': [],
    '''image-to-image''': [],
    '''tabular-to-text''': [],
    '''unconditional-image-generation''': [],
    '''text-retrieval''': [],
    '''text-to-speech''': [],
    '''object-detection''': [],
    '''audio-to-audio''': [],
    '''text-generation''': [],
    '''conversational''': [],
    '''table-question-answering''': [],
    '''visual-question-answering''': [],
    '''image-to-text''': [],
    '''reinforcement-learning''': [],
    '''voice-activity-detection''': [],
    '''time-series-forecasting''': [],
    '''document-question-answering''': [],
}


if __name__ == "__main__":
		from argparse import ArgumentParser

		__lowerCamelCase					:	List[Any]       =  ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
		ap.add_argument('''readme_filepath''')
		__lowerCamelCase					:	Dict       =  ap.parse_args()

		__lowerCamelCase					:	List[Any]       =  Path(args.readme_filepath)
		__lowerCamelCase					:	Optional[int]       =  DatasetMetadata.from_readme(readme_filepath)
		print(dataset_metadata)
		dataset_metadata.to_readme(readme_filepath)
