





import copy
import re






class 							__lowercase       :







	'''simple docstring'''

	a    :				Optional[Any]											=       "hp"
	a    :				List[Any]											=       {}
	a    :				Union[str, Any]											=       None


	@classmethod
	def 						_UpperCAmelCase     (cls     ,_lowerCamelCase     ,_lowerCamelCase )							->							Dict:







				'''simple docstring'''





				__lowercase										=							prefix
				__lowercase										=							defaults
				cls.build_naming_info()


	@staticmethod
	def 						_UpperCAmelCase     (_lowerCamelCase     ,_lowerCamelCase )							->							Union[str, Any]:







				'''simple docstring'''





				if len(__a ) == 0:
							return ""
				__lowercase										=							None
				if any(char.isdigit() for char in word ):
							raise Exception(f"Parameters should not contain numbers: \'{word}\' contains a number" )
				if word in info["short_word"]:
							return info["short_word"][word]
				for prefix_len in range(1     ,len(__a ) + 1 ):
							__lowercase										=							word[:prefix_len]
							if prefix in info["reverse_short_word"]:
										continue
							else:
										__lowercase										=							prefix
										break

				if short_word is None:
							# Paranoid fallback
							def int_to_alphabetic(_lowerCamelCase ):
										__lowercase										=							''''''
										while integer != 0:
													__lowercase										=							chr(ord('''A''' ) + integer % 10 ) + s
													integer //= 10
										return s

							__lowercase										=							0
							while True:
										__lowercase										=							word + '''#''' + int_to_alphabetic(__a )
										if sword in info["reverse_short_word"]:
													continue
										else:
													__lowercase										=							sword
													break

				__lowercase										=							short_word
				__lowercase										=							word
				return short_word


	@staticmethod
	def 						_UpperCAmelCase     (_lowerCamelCase     ,_lowerCamelCase )							->							Any:







				'''simple docstring'''





				__lowercase										=							param_name.split('''_''' )

				__lowercase										=							[TrialShortNamer.shortname_for_word(__a     ,__a ) for word in words]

				# We try to create a separatorless short name, but if there is a collision we have to fallback
				# to a separated short name
				__lowercase										=							['''''', '''_''']

				for separator in separators:
							__lowercase										=							separator.join(__a )
							if shortname not in info["reverse_short_param"]:
										__lowercase										=							shortname
										__lowercase										=							param_name
										return shortname

				return param_name


	@staticmethod
	def 						_UpperCAmelCase     (_lowerCamelCase     ,_lowerCamelCase )							->							List[str]:







				'''simple docstring'''





				__lowercase										=							TrialShortNamer.shortname_for_key(__a     ,__a )
				__lowercase										=							short_name
				__lowercase										=							param_name


	@classmethod
	def 						_UpperCAmelCase     (cls )							->							Any:







				'''simple docstring'''





				if cls.NAMING_INFO is not None:
							return

				__lowercase										=							{
				    '''short_word''': {},
				    '''reverse_short_word''': {},
				    '''short_param''': {},
				    '''reverse_short_param''': {},
				}

				__lowercase										=							list(cls.DEFAULTS.keys() )

				for k in field_keys:
							cls.add_new_param_name(__a     ,__a )

				__lowercase										=							info


	@classmethod
	def 						_UpperCAmelCase     (cls     ,_lowerCamelCase )							->							Optional[Any]:







				'''simple docstring'''





				cls.build_naming_info()
				assert cls.PREFIX is not None
				__lowercase										=							[copy.copy(cls.PREFIX )]

				for k, v in params.items():
							if k not in cls.DEFAULTS:
										raise Exception(f"You should provide a default value for the param name {k} with value {v}" )
							if v == cls.DEFAULTS[k]:
										# The default value is not added to the name
										continue

							__lowercase										=							cls.NAMING_INFO['''short_param'''][k]

							if isinstance(__a     ,__a ):
										__lowercase										=							1 if v else 0

							__lowercase										=							'''''' if isinstance(__a     ,(int, float) ) else '''-'''
							__lowercase										=							f"{key}{sep}{v}"
							name.append(__a )

				return "_".join(__a )


	@classmethod
	def 						_UpperCAmelCase     (cls     ,_lowerCamelCase )							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							repr[len(cls.PREFIX ) + 1 :]
				if repr == "":
							__lowercase										=							[]
				else:
							__lowercase										=							repr.split('''_''' )

				__lowercase										=							{}

				for value in values:
							if "-" in value:
										__lowercase						,		__lowercase										=							value.split('''-''' )
							else:
										__lowercase										=							re.sub('''[0-9.]'''     ,''''''     ,__a )
										__lowercase										=							float(re.sub('''[^0-9.]'''     ,''''''     ,__a ) )

							__lowercase										=							cls.NAMING_INFO['''reverse_short_param'''][p_k]

							__lowercase										=							p_v

				for k in cls.DEFAULTS:
							if k not in parameters:
										__lowercase										=							cls.DEFAULTS[k]

				return parameters












'''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    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  List[str]				):
			__lowercase										=							UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase_		,       config=lowerCamelCase_				)
			__lowercase										=							downstream_dict['''projector.weight''']
			__lowercase										=							downstream_dict['''projector.bias''']
			__lowercase										=							downstream_dict['''model.post_net.linear.weight''']
			__lowercase										=							downstream_dict['''model.post_net.linear.bias''']
			return model




def    _lowerCAmelCase						(				lowerCamelCase_			:  str		,       lowerCamelCase_			:  Optional[int]		,       lowerCamelCase_			:  Optional[Any]				):
			__lowercase										=							UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase_		,       config=lowerCamelCase_				)
			__lowercase										=							downstream_dict['''model.linear.weight''']
			__lowercase										=							downstream_dict['''model.linear.bias''']
			return model




def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							UniSpeechSatForXVector.from_pretrained(lowerCamelCase_		,       config=lowerCamelCase_				)
			__lowercase										=							downstream_dict['''connector.weight''']
			__lowercase										=							downstream_dict['''connector.bias''']
			for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
						__lowercase										=							downstream_dict[
						    f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
						]
						__lowercase										=							downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

			__lowercase										=							downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
			__lowercase										=							downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
			__lowercase										=							downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
			__lowercase										=							downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
			__lowercase										=							downstream_dict['''objective.W''']
			return model




@torch.no_grad()
def    _lowerCAmelCase						(				lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)

			__lowercase										=							checkpoint['''Downstream''']

			__lowercase										=							UniSpeechSatConfig.from_pretrained(lowerCamelCase_				)
			__lowercase										=							WavaVecaFeatureExtractor.from_pretrained(
			    lowerCamelCase_		,       return_attention_mask=lowerCamelCase_		,       do_normalize=lowerCamelCase_				)

			__lowercase										=							hf_config.architectures[0]
			if arch.endswith('''ForSequenceClassification'''				):
						__lowercase										=							convert_classification(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			elif arch.endswith('''ForAudioFrameClassification'''				):
						__lowercase										=							convert_diarization(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			elif arch.endswith('''ForXVector'''				):
						__lowercase										=							convert_xvector(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			else:
						raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}"				)

			if hf_config.use_weighted_layer_sum:
						__lowercase										=							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)





