





'''simple docstring'''



from ...processing_utils import ProcessorMixin






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				int											=       "SpeechT5FeatureExtractor"
	a    :				Dict											=       "SpeechT5Tokenizer"


	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase )							->							Tuple:







				'''simple docstring'''





				super().__init__(_lowerCamelCase     ,_lowerCamelCase )


	def __call__(self     ,*_lowerCamelCase     ,**_lowerCamelCase )							->							str:







				'''simple docstring'''





				__lowercase										=							kwargs.pop('''audio'''     ,_lowerCamelCase )
				__lowercase										=							kwargs.pop('''text'''     ,_lowerCamelCase )
				__lowercase										=							kwargs.pop('''text_target'''     ,_lowerCamelCase )
				__lowercase										=							kwargs.pop('''audio_target'''     ,_lowerCamelCase )
				__lowercase										=							kwargs.pop('''sampling_rate'''     ,_lowerCamelCase )

				if audio is not None and text is not None:
							raise ValueError(
							    '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?''' )
				if audio_target is not None and text_target is not None:
							raise ValueError(
							    '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?''' )
				if audio is None and audio_target is None and text is None and text_target is None:
							raise ValueError(
							    '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.''' )

				if audio is not None:
							__lowercase										=							self.feature_extractor(_lowerCamelCase     ,*_lowerCamelCase     ,sampling_rate=_lowerCamelCase     ,**_lowerCamelCase )
				elif text is not None:
							__lowercase										=							self.tokenizer(_lowerCamelCase     ,**_lowerCamelCase )
				else:
							__lowercase										=							None

				if audio_target is not None:
							__lowercase										=							self.feature_extractor(audio_target=_lowerCamelCase     ,*_lowerCamelCase     ,sampling_rate=_lowerCamelCase     ,**_lowerCamelCase )
							__lowercase										=							targets['''input_values''']
				elif text_target is not None:
							__lowercase										=							self.tokenizer(_lowerCamelCase     ,**_lowerCamelCase )
							__lowercase										=							targets['''input_ids''']
				else:
							__lowercase										=							None

				if inputs is None:
							return targets

				if targets is not None:
							__lowercase										=							labels

							__lowercase										=							targets.get('''attention_mask''' )
							if decoder_attention_mask is not None:
										__lowercase										=							decoder_attention_mask

				return inputs


	def 						_UpperCAmelCase     (self     ,*_lowerCamelCase     ,**_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							kwargs.pop('''input_values'''     ,_lowerCamelCase )
				__lowercase										=							kwargs.pop('''input_ids'''     ,_lowerCamelCase )
				__lowercase										=							kwargs.pop('''labels'''     ,_lowerCamelCase )

				if input_values is not None and input_ids is not None:
							raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.''' )
				if input_values is None and input_ids is None and labels is None:
							raise ValueError(
							    '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.''' )

				if input_values is not None:
							__lowercase										=							self.feature_extractor.pad(_lowerCamelCase     ,*_lowerCamelCase     ,**_lowerCamelCase )
				elif input_ids is not None:
							__lowercase										=							self.tokenizer.pad(_lowerCamelCase     ,**_lowerCamelCase )
				else:
							__lowercase										=							None

				if labels is not None:
							if "input_ids" in labels or (isinstance(_lowerCamelCase     ,_lowerCamelCase ) and "input_ids" in labels[0]):
										__lowercase										=							self.tokenizer.pad(_lowerCamelCase     ,**_lowerCamelCase )
										__lowercase										=							targets['''input_ids''']
							else:
										__lowercase										=							self.feature_extractor.feature_size
										__lowercase										=							self.feature_extractor.num_mel_bins
										__lowercase										=							self.feature_extractor.pad(_lowerCamelCase     ,*_lowerCamelCase     ,**_lowerCamelCase )
										__lowercase										=							feature_size_hack
										__lowercase										=							targets['''input_values''']
				else:
							__lowercase										=							None

				if inputs is None:
							return targets

				if targets is not None:
							__lowercase										=							labels

							__lowercase										=							targets.get('''attention_mask''' )
							if decoder_attention_mask is not None:
										__lowercase										=							decoder_attention_mask

				return inputs


	def 						_UpperCAmelCase     (self     ,*_lowerCamelCase     ,**_lowerCamelCase )							->							int:







				'''simple docstring'''





				return self.tokenizer.batch_decode(*_lowerCamelCase     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,*_lowerCamelCase     ,**_lowerCamelCase )							->							Tuple:







				'''simple docstring'''





				return self.tokenizer.decode(*_lowerCamelCase     ,**_lowerCamelCase )












'''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)





