



"""simple docstring"""


import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


snake_case_								=			logging.get_logger(__name__)

snake_case_								=			{
    """google/vit-hybrid-base-bit-384""": """https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json""",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				"""vit-hybrid"""






						def __init__(   self					:Union[str, Any]  ,    lowercase_					:Dict=None  ,    lowercase_					:Union[str, Any]=7_68  ,    lowercase_					:Optional[int]=12  ,    lowercase_					:Tuple=12  ,    lowercase_					:Tuple=30_72  ,    lowercase_					:List[Any]="gelu"  ,    lowercase_					:Any=0.0  ,    lowercase_					:Tuple=0.0  ,    lowercase_					:Optional[Any]=0.02  ,    lowercase_					:List[str]=1E-12  ,    lowercase_					:Optional[int]=2_24  ,    lowercase_					:List[str]=1  ,    lowercase_					:Union[str, Any]=3  ,    lowercase_					:int=[1, 10_24, 24, 24]  ,    lowercase_					:Optional[Any]=True  ,    **lowercase_					:List[str]  ,    )							->     List[str]:
												super().__init__(**lowercase_   )

												if backbone_config is None:
																		logger.info('`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'   )
																		UpperCAmelCase			 =					{
																		    'global_padding': 'same',
																		    'layer_type': 'bottleneck',
																		    'depths': [3, 4, 9],
																		    'out_features': ['stage3'],
																		    'embedding_dynamic_padding': True,
																		}

												if isinstance(lowercase_  ,    lowercase_   ):
																		if "model_type" in backbone_config:
																								UpperCAmelCase			 =					CONFIG_MAPPING[backbone_config['model_type']]
																		else:
																								logger.info(
																								    '`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'   )
																								UpperCAmelCase			 =					BitConfig
																		UpperCAmelCase			 =					backbone_config_class(**lowercase_   )

												UpperCAmelCase			 =					backbone_featmap_shape
												UpperCAmelCase			 =					backbone_config
												UpperCAmelCase			 =					hidden_size
												UpperCAmelCase			 =					num_hidden_layers
												UpperCAmelCase			 =					num_attention_heads
												UpperCAmelCase			 =					intermediate_size
												UpperCAmelCase			 =					hidden_act
												UpperCAmelCase			 =					hidden_dropout_prob
												UpperCAmelCase			 =					attention_probs_dropout_prob
												UpperCAmelCase			 =					initializer_range
												UpperCAmelCase			 =					layer_norm_eps
												UpperCAmelCase			 =					image_size
												UpperCAmelCase			 =					patch_size
												UpperCAmelCase			 =					num_channels
												UpperCAmelCase			 =					qkv_bias






						def       UpperCAmelCase__	(   self					:Tuple   )							->     Dict[str, any]:
												UpperCAmelCase			 =					copy.deepcopy(self.__dict__   )
												UpperCAmelCase			 =					self.backbone_config.to_dict()
												UpperCAmelCase			 =					self.__class__.model_type
												return output







'''simple docstring'''





import argparse

import torch

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


logging.set_verbosity_info()
A							     = logging.get_logger(__name__)
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple			,       _a  :				int      ):
      snake_case_    							=				WavLMForSequenceClassification.from_pretrained(_a			,       config=_a      )
      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 	snake_case_							(				_a  :				List[str]			,       _a  :				Optional[Any]			,       _a  :				List[str]      ):
      snake_case_    							=				WavLMForAudioFrameClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["model.linear.weight"]
      snake_case_    							=				downstream_dict["model.linear.bias"]
      return model
def 	snake_case_							(				_a  :				Dict			,       _a  :				Tuple			,       _a  :				Any      ):
      snake_case_    							=				WavLMForXVector.from_pretrained(_a			,       config=_a      )
      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 	snake_case_							(				_a  :				Union[str, Any]			,       _a  :				int			,       _a  :				str			,       _a  :				List[Any]      ):
      snake_case_    							=				torch.load(_a			,       map_location="cpu"      )

      snake_case_    							=				checkpoint["Downstream"]

      snake_case_    							=				WavLMConfig.from_pretrained(_a      )
      snake_case_    							=				WavaVecaFeatureExtractor.from_pretrained(
          _a			,       return_attention_mask=_a			,       do_normalize=_a      )

      snake_case_    							=				hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"      ):
            snake_case_    							=				convert_classification(_a			,       _a			,       _a      )
      elif arch.endswith("ForAudioFrameClassification"      ):
            snake_case_    							=				convert_diarization(_a			,       _a			,       _a      )
      elif arch.endswith("ForXVector"      ):
            snake_case_    							=				convert_xvector(_a			,       _a			,       _a      )
      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(_a      )
      hf_model.save_pretrained(_a      )


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

