'''simple docstring'''

import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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



def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=WavaVecaForSequenceClassification.from_pretrained(a__			,      config=a__      )
						_lowerCAmelCase										=downstream_dict['projector.weight']
						_lowerCAmelCase										=downstream_dict['projector.bias']
						_lowerCAmelCase										=downstream_dict['model.post_net.linear.weight']
						_lowerCAmelCase										=downstream_dict['model.post_net.linear.bias']
						return model



def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=WavaVecaForAudioFrameClassification.from_pretrained(a__			,      config=a__      )
						_lowerCAmelCase										=downstream_dict['model.linear.weight']
						_lowerCAmelCase										=downstream_dict['model.linear.bias']
						return model



def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=WavaVecaForXVector.from_pretrained(a__			,      config=a__      )
						_lowerCAmelCase										=downstream_dict['connector.weight']
						_lowerCAmelCase										=downstream_dict['connector.bias']
						for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
												_lowerCAmelCase										=downstream_dict[
												    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
												]
												_lowerCAmelCase										=downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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



@torch.no_grad()
def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=torch.load(a__			,      map_location='cpu'      )

						_lowerCAmelCase										=checkpoint['Downstream']

						_lowerCAmelCase										=WavaVecaConfig.from_pretrained(a__      )
						_lowerCAmelCase										=WavaVecaFeatureExtractor.from_pretrained(
						    a__			,      return_attention_mask=a__			,      do_normalize=a__      )

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

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

						hf_feature_extractor.save_pretrained(a__      )
						hf_model.save_pretrained(a__      )


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






'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Any       =							['image_processor', 'tokenizer']
		lowercase   :      Any       =							'CLIPImageProcessor'
		lowercase   :      int       =							('CLIPTokenizer', 'CLIPTokenizerFast')






		def __init__(   self		,						__A=None		,						__A=None		,						**__A				) ->     str:
								_lowerCAmelCase										=None
								if "feature_extractor" in kwargs:
														warnings.warn(
														    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
														    ' instead.'		,						__A		,						)
														_lowerCAmelCase										=kwargs.pop('feature_extractor'				)

								_lowerCAmelCase										=image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
														raise ValueError('You need to specify an `image_processor`.'				)
								if tokenizer is None:
														raise ValueError('You need to specify a `tokenizer`.'				)

								super().__init__(__A		,						__A				)






		def __call__(   self		,						__A=None		,						__A=None		,						__A=None		,						**__A				) ->     Optional[int]:

								if text is None and images is None:
														raise ValueError('You have to specify either text or images. Both cannot be none.'				)

								if text is not None:
														_lowerCAmelCase										=self.tokenizer(__A		,						return_tensors=__A		,						**__A				)

								if images is not None:
														_lowerCAmelCase										=self.image_processor(__A		,						return_tensors=__A		,						**__A				)

								if text is not None and images is not None:
														_lowerCAmelCase										=image_features.pixel_values
														return encoding
								elif text is not None:
														return encoding
								else:
														return BatchEncoding(data=dict(**__A				)		,						tensor_type=__A				)






		def 		UpperCamelCase__       (   self		,						*__A		,						**__A				) ->     Any:
								return self.tokenizer.batch_decode(*__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						*__A		,						**__A				) ->     Optional[int]:
								return self.tokenizer.decode(*__A		,						**__A				)






		@property
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=self.tokenizer.model_input_names
								_lowerCAmelCase										=self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								warnings.warn(
								    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,						__A		,						)
								return self.image_processor_class






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								warnings.warn(
								    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,						__A		,						)
								return self.image_processor





