





"""simple docstring"""
import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCamelCase_       =		{
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCamelCase_       =		logging.get_logger(__name__)






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Any     						=  '''maskformer'''
       SCREAMING_SNAKE_CASE_       :				Any     						=  {'''hidden_size''': '''mask_feature_size'''}
       SCREAMING_SNAKE_CASE_       :				List[str]     						=  ['''resnet''', '''swin''']
       SCREAMING_SNAKE_CASE_       :				Tuple     						=  ['''detr''']




       def __init__(						self		,SCREAMING_SNAKE_CASE__ = 2_56		,SCREAMING_SNAKE_CASE__ = 2_56		,SCREAMING_SNAKE_CASE__ = 0.1		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = 0.0_2		,SCREAMING_SNAKE_CASE__ = 1.0		,SCREAMING_SNAKE_CASE__ = 1.0		,SCREAMING_SNAKE_CASE__ = 1.0		,SCREAMING_SNAKE_CASE__ = 2_0.0		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__		,)      ->				List[str]:





             """simple docstring"""






             if backbone_config is None:
                   # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  SwinConfig(
                       image_size=3_84		,in_channels=3		,patch_size=4		,embed_dim=1_28		,depths=[2, 2, 18, 2]		,num_heads=[4, 8, 16, 32]		,window_size=12		,drop_path_rate=0.3		,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']		,)

             if isinstance(_a		,_a							):
                   __SCREAMING_SNAKE_CASE       :int		    =  backbone_config.pop('''model_type'''							)
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  CONFIG_MAPPING[backbone_model_type]
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  config_class.from_dict(_a							)

             # verify that the backbone is supported
             if backbone_config.model_type not in self.backbones_supported:
                   logger.warning_once(
                       f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '''
                       f'''Supported model types: {",".join(self.backbones_supported							)}'''							)

             if decoder_config is None:
                   # fall back to https://huggingface.co/facebook/detr-resnet-50
                   __SCREAMING_SNAKE_CASE       :str		    =  DetrConfig()
             else:
                   # verify that the decoder is supported
                   __SCREAMING_SNAKE_CASE       :str		    =  (
                       decoder_config.pop('''model_type'''							) if isinstance(_a		,_a							) else decoder_config.model_type
                   )
                   if decoder_type not in self.decoders_supported:
                         raise ValueError(
                             f'''Transformer Decoder {decoder_type} not supported, please use one of'''
                             f''' {",".join(self.decoders_supported							)}'''							)
                   if isinstance(_a		,_a							):
                         __SCREAMING_SNAKE_CASE       :List[Any]		    =  CONFIG_MAPPING[decoder_type]
                         __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  config_class.from_dict(_a							)

             __SCREAMING_SNAKE_CASE       :Dict		    =  backbone_config
             __SCREAMING_SNAKE_CASE       :Any		    =  decoder_config
             # main feature dimension for the model
             __SCREAMING_SNAKE_CASE       :Dict		    =  fpn_feature_size
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  mask_feature_size
             # initializer
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  init_std
             __SCREAMING_SNAKE_CASE       :Any		    =  init_xavier_std
             # Hungarian matcher && loss
             __SCREAMING_SNAKE_CASE       :Tuple		    =  cross_entropy_weight
             __SCREAMING_SNAKE_CASE       :int		    =  dice_weight
             __SCREAMING_SNAKE_CASE       :List[str]		    =  mask_weight
             __SCREAMING_SNAKE_CASE       :List[str]		    =  use_auxiliary_loss
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  no_object_weight
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  output_auxiliary_logits

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.decoder_config.encoder_attention_heads
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  self.decoder_config.num_hidden_layers
             super().__init__(**_a							)




       @classmethod
       def    _UpperCamelCase					(						cls		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






             return cls(
                 backbone_config=_a		,decoder_config=_a		,**_a		,)




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :str		    =  copy.deepcopy(self.__dict__							)
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.backbone_config.to_dict()
             __SCREAMING_SNAKE_CASE       :str		    =  self.decoder_config.to_dict()
             __SCREAMING_SNAKE_CASE       :str		    =  self.__class__.model_type
             return output






"""simple docstring"""
import argparse

import torch

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


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



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Optional[int]			,       a_					:		Any							)      ->				List[Any]:
      __SCREAMING_SNAKE_CASE       :Any		    =  UniSpeechSatForSequenceClassification.from_pretrained(a_			,       config=a_							)
      __SCREAMING_SNAKE_CASE       :int		    =  downstream_dict['''projector.weight''']
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  downstream_dict['''projector.bias''']
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  downstream_dict['''model.post_net.linear.weight''']
      __SCREAMING_SNAKE_CASE       :List[str]		    =  downstream_dict['''model.post_net.linear.bias''']
      return model



def 					__lowerCamelCase       (							a_					:		Union[str, Any]			,       a_					:		List[Any]			,       a_					:		List[Any]							)      ->				Optional[int]:
      __SCREAMING_SNAKE_CASE       :Any		    =  UniSpeechSatForAudioFrameClassification.from_pretrained(a_			,       config=a_							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  downstream_dict['''model.linear.weight''']
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  downstream_dict['''model.linear.bias''']
      return model



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Optional[Any]			,       a_					:		int							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :List[str]		    =  UniSpeechSatForXVector.from_pretrained(a_			,       config=a_							)
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  downstream_dict['''connector.weight''']
      __SCREAMING_SNAKE_CASE       :Tuple		    =  downstream_dict['''connector.bias''']
      for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
            __SCREAMING_SNAKE_CASE       :str		    =  downstream_dict[
                f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            __SCREAMING_SNAKE_CASE       :int		    =  downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      __SCREAMING_SNAKE_CASE       :Any		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
      __SCREAMING_SNAKE_CASE       :Dict		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
      __SCREAMING_SNAKE_CASE       :str		    =  downstream_dict['''objective.W''']
      return model



@torch.no_grad()
def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Union[str, Any]			,       a_					:		Any			,       a_					:		Union[str, Any]							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :str		    =  torch.load(a_			,       map_location='''cpu'''							)

      __SCREAMING_SNAKE_CASE       :str		    =  checkpoint['''Downstream''']

      __SCREAMING_SNAKE_CASE       :str		    =  UniSpeechSatConfig.from_pretrained(a_							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  WavaVecaFeatureExtractor.from_pretrained(
          a_			,       return_attention_mask=a_			,       do_normalize=a_							)

      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hf_config.architectures[0]
      if arch.endswith('''ForSequenceClassification'''							):
            __SCREAMING_SNAKE_CASE       :str		    =  convert_classification(a_			,       a_			,       a_							)
      elif arch.endswith('''ForAudioFrameClassification'''							):
            __SCREAMING_SNAKE_CASE       :Tuple		    =  convert_diarization(a_			,       a_			,       a_							)
      elif arch.endswith('''ForXVector'''							):
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  convert_xvector(a_			,       a_			,       a_							)
      else:
            raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}'''							)

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

      hf_feature_extractor.save_pretrained(a_							)
      hf_model.save_pretrained(a_							)


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