import argparse

import torch

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


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







def       A__   (						snake_case_			:		Optional[int]							,					snake_case_			:		int							,					snake_case_			:		Union[str, Any]   ):
 SCREAMING_SNAKE_CASE__: Dict=   UniSpeechSatForSequenceClassification.from_pretrained(snake_case_							,					config=snake_case_   )
 SCREAMING_SNAKE_CASE__: str=   downstream_dict['''projector.weight''']
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   downstream_dict['''projector.bias''']
 SCREAMING_SNAKE_CASE__: Tuple=   downstream_dict['''model.post_net.linear.weight''']
 SCREAMING_SNAKE_CASE__: List[str]=   downstream_dict['''model.post_net.linear.bias''']
 return model







def       A__   (						snake_case_			:		int							,					snake_case_			:		Union[str, Any]							,					snake_case_			:		str   ):
 SCREAMING_SNAKE_CASE__: Optional[int]=   UniSpeechSatForAudioFrameClassification.from_pretrained(snake_case_							,					config=snake_case_   )
 SCREAMING_SNAKE_CASE__: str=   downstream_dict['''model.linear.weight''']
 SCREAMING_SNAKE_CASE__: List[str]=   downstream_dict['''model.linear.bias''']
 return model







def       A__   (						snake_case_			:		Any							,					snake_case_			:		Any							,					snake_case_			:		Dict   ):
 SCREAMING_SNAKE_CASE__: Optional[Any]=   UniSpeechSatForXVector.from_pretrained(snake_case_							,					config=snake_case_   )
 SCREAMING_SNAKE_CASE__: List[str]=   downstream_dict['''connector.weight''']
 SCREAMING_SNAKE_CASE__: str=   downstream_dict['''connector.bias''']
 for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
  SCREAMING_SNAKE_CASE__: List[Any]=   downstream_dict[
      F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
  ]
  SCREAMING_SNAKE_CASE__: Any=   downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

 SCREAMING_SNAKE_CASE__: Union[str, Any]=   downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
 SCREAMING_SNAKE_CASE__: List[Any]=   downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
 SCREAMING_SNAKE_CASE__: Optional[Any]=   downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
 SCREAMING_SNAKE_CASE__: Optional[Any]=   downstream_dict['''objective.W''']
 return model







@torch.no_grad()
def       A__   (						snake_case_			:		Optional[Any]							,					snake_case_			:		Optional[int]							,					snake_case_			:		List[Any]							,					snake_case_			:		Dict   ):
 SCREAMING_SNAKE_CASE__: Optional[Any]=   torch.load(snake_case_							,					map_location='''cpu'''   )

 SCREAMING_SNAKE_CASE__: Union[str, Any]=   checkpoint['''Downstream''']

 SCREAMING_SNAKE_CASE__: Optional[int]=   UniSpeechSatConfig.from_pretrained(snake_case_   )
 SCREAMING_SNAKE_CASE__: Optional[Any]=   WavaVecaFeatureExtractor.from_pretrained(
     snake_case_							,					return_attention_mask=snake_case_							,					do_normalize=snake_case_   )

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

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

 hf_feature_extractor.save_pretrained(snake_case_   )
 hf_model.save_pretrained(snake_case_   )


if __name__ == "__main__":
      lowercase_  : List[Any]								=					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_  : List[str]								=					parser.parse_args()
      convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)








from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
					import timm


if is_torch_available():
					from torch import Tensor

class 							__SCREAMING_SNAKE_CASE       (  _a ,		_a							):
							snake_case  :   int      =							"""pixel_values"""
							snake_case  :   List[Any]      =							False
							snake_case  :   str      =							TimmBackboneConfig






							def __init__(    self	,			__lowerCAmelCase	,			**__lowerCAmelCase	):
														requires_backends(self	,			"""timm"""	)
														super().__init__(__lowerCAmelCase	)
														UpperCamelCase__					  =      config

														if config.backbone is None:
																					raise ValueError("""backbone is not set in the config. Please set it to a timm model name."""	)

														if config.backbone not in timm.list_models():
																					raise ValueError(f"""backbone {config.backbone} is not supported by timm."""	)

														if hasattr(__lowerCAmelCase	,			"""out_features"""	) and config.out_features is not None:
																					raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""	)

														UpperCamelCase__					  =      getattr(__lowerCAmelCase	,			"""use_pretrained_backbone"""	,			__lowerCAmelCase	)
														if pretrained is None:
																					raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False."""	)

														# We just take the final layer by default. This matches the default for the transformers models.
														UpperCamelCase__					  =      config.out_indices if getattr(__lowerCAmelCase	,			"""out_indices"""	,			__lowerCAmelCase	) is not None else (-1,)

														UpperCamelCase__					  =      timm.create_model(
														    config.backbone	,			pretrained=__lowerCAmelCase	,			features_only=config.features_only	,			in_chans=config.num_channels	,			out_indices=__lowerCAmelCase	,			**__lowerCAmelCase	,			)
														# These are used to control the output of the model when called. If output_hidden_states is True, then
														# return_layers is modified to include all layers.
														UpperCamelCase__					  =      self._backbone.return_layers
														UpperCamelCase__					  =      {layer["""module"""]: str(__lowerCAmelCase	) for i, layer in enumerate(self._backbone.feature_info.info	)}
														super()._init_backbone(__lowerCAmelCase	)






							@classmethod
							def 					_lowerCamelCase		(    cls	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														requires_backends(cls	,			["""vision""", """timm"""]	)
														from ...models.timm_backbone import TimmBackboneConfig

														UpperCamelCase__					  =      kwargs.pop("""config"""	,			TimmBackboneConfig()	)

														UpperCamelCase__					  =      kwargs.pop("""use_timm_backbone"""	,			__lowerCAmelCase	)
														if not use_timm:
																					raise ValueError("""use_timm_backbone must be True for timm backbones"""	)

														UpperCamelCase__					  =      kwargs.pop("""num_channels"""	,			config.num_channels	)
														UpperCamelCase__					  =      kwargs.pop("""features_only"""	,			config.features_only	)
														UpperCamelCase__					  =      kwargs.pop("""use_pretrained_backbone"""	,			config.use_pretrained_backbone	)
														UpperCamelCase__					  =      kwargs.pop("""out_indices"""	,			config.out_indices	)
														UpperCamelCase__					  =      TimmBackboneConfig(
														    backbone=__lowerCAmelCase	,			num_channels=__lowerCAmelCase	,			features_only=__lowerCAmelCase	,			use_pretrained_backbone=__lowerCAmelCase	,			out_indices=__lowerCAmelCase	,			)
														return super()._from_config(__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														pass







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			**__lowerCAmelCase	):
														UpperCamelCase__					  =      return_dict if return_dict is not None else self.config.use_return_dict
														UpperCamelCase__					  =      (
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)
														UpperCamelCase__					  =      output_attentions if output_attentions is not None else self.config.output_attentions

														if output_attentions:
																					raise ValueError("""Cannot output attentions for timm backbones at the moment"""	)

														if output_hidden_states:
																					# We modify the return layers to include all the stages of the backbone
																					UpperCamelCase__					  =      self._all_layers
																					UpperCamelCase__					  =      self._backbone(__lowerCAmelCase	,			**__lowerCAmelCase	)
																					UpperCamelCase__					  =      self._return_layers
																					UpperCamelCase__					  =      tuple(hidden_states[i] for i in self.out_indices	)
														else:
																					UpperCamelCase__					  =      self._backbone(__lowerCAmelCase	,			**__lowerCAmelCase	)
																					UpperCamelCase__					  =      None

														UpperCamelCase__					  =      tuple(__lowerCAmelCase	)
														UpperCamelCase__					  =      tuple(__lowerCAmelCase	) if hidden_states is not None else None

														if not return_dict:
																					UpperCamelCase__					  =      (feature_maps,)
																					if output_hidden_states:
																												UpperCamelCase__					  =      output + (hidden_states,)
																					return output

														return BackboneOutput(feature_maps=__lowerCAmelCase	,			hidden_states=__lowerCAmelCase	,			attentions=__lowerCAmelCase	)




