

"""simple docstring"""




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class      __UpperCamelCase					(  _A		):
		SCREAMING_SNAKE_CASE														=       ["image_processor", "tokenizer"]
		SCREAMING_SNAKE_CASE														=       "AutoImageProcessor"
		SCREAMING_SNAKE_CASE														=       "AutoTokenizer"


		def __init__(self					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=None	,	__SCREAMING_SNAKE_CASE					:						int=None	,	**__SCREAMING_SNAKE_CASE					:						Any):
							A     =   None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
												    " instead."	,	__SCREAMING_SNAKE_CASE	,	)
												A     =   kwargs.pop("feature_extractor")

							A     =   image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
												raise ValueError("You have to specify an image_processor.")
							if tokenizer is None:
												raise ValueError("You have to specify a tokenizer.")

							super().__init__(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)
							A     =   self.image_processor


		def __call__(self					:						int	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]=None	,	__SCREAMING_SNAKE_CASE					:						List[Any]=None	,	__SCREAMING_SNAKE_CASE					:						Tuple=None	,	**__SCREAMING_SNAKE_CASE					:						str):

							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:
												A     =   self.tokenizer(__SCREAMING_SNAKE_CASE	,	return_tensors=__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE)

							if images is not None:
												A     =   self.image_processor(__SCREAMING_SNAKE_CASE	,	return_tensors=__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE)

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


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]	,	*__SCREAMING_SNAKE_CASE					:						Optional[Any]	,	**__SCREAMING_SNAKE_CASE					:						Tuple):
							return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]	,	*__SCREAMING_SNAKE_CASE					:						int	,	**__SCREAMING_SNAKE_CASE					:						int):
							return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   self.tokenizer.model_input_names
							A     =   self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."	,	__SCREAMING_SNAKE_CASE	,	)
							return self.image_processor_class


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."	,	__SCREAMING_SNAKE_CASE	,	)
							return self.image_processor












import argparse

import torch

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


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





def     lowerCamelCase__  (  UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Optional[Any]    )      ->			Optional[int]:



					'''simple docstring'''




					_snake_case						=					WavLMForSequenceClassification.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['projector.weight']
					_snake_case						=					downstream_dict['projector.bias']
					_snake_case						=					downstream_dict['model.post_net.linear.weight']
					_snake_case						=					downstream_dict['model.post_net.linear.bias']
					return model





def     lowerCamelCase__  (  UpperCamelCase__      :							Optional[Any]							,     UpperCamelCase__      :							str							,     UpperCamelCase__      :							List[Any]    )      ->			Optional[Any]:



					'''simple docstring'''




					_snake_case						=					WavLMForAudioFrameClassification.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['model.linear.weight']
					_snake_case						=					downstream_dict['model.linear.bias']
					return model





def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							Dict    )      ->			Tuple:



					'''simple docstring'''




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

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





@torch.no_grad()
def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Union[str, Any]							,     UpperCamelCase__      :							List[str]							,     UpperCamelCase__      :							Optional[int]    )      ->			int:



					'''simple docstring'''




					_snake_case						=					torch.load(UpperCamelCase__							,     map_location='cpu'    )

					_snake_case						=					checkpoint['Downstream']

					_snake_case						=					WavLMConfig.from_pretrained(UpperCamelCase__    )
					_snake_case						=					WavaVecaFeatureExtractor.from_pretrained(
					    UpperCamelCase__							,     return_attention_mask=UpperCamelCase__							,     do_normalize=UpperCamelCase__    )

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

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

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


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



