"""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


UpperCAmelCase       :    List[str]									=			logging.get_logger(__name__)

UpperCAmelCase       :    int									=			{
    "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        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase							):
				UpperCAmelCase	=      '''vit-hybrid'''







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




									"""simple docstring"""



									super().__init__(**__UpperCAmelCase  )

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

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

									lowerCamelCase__		:   int  				=					backbone_featmap_shape
									lowerCamelCase__		:   Dict  				=					backbone_config
									lowerCamelCase__		:   Union[str, Any]  				=					hidden_size
									lowerCamelCase__		:   str  				=					num_hidden_layers
									lowerCamelCase__		:   Tuple  				=					num_attention_heads
									lowerCamelCase__		:   List[str]  				=					intermediate_size
									lowerCamelCase__		:   List[str]  				=					hidden_act
									lowerCamelCase__		:   List[Any]  				=					hidden_dropout_prob
									lowerCamelCase__		:   Union[str, Any]  				=					attention_probs_dropout_prob
									lowerCamelCase__		:   Any  				=					initializer_range
									lowerCamelCase__		:   List[str]  				=					layer_norm_eps
									lowerCamelCase__		:   Any  				=					image_size
									lowerCamelCase__		:   Any  				=					patch_size
									lowerCamelCase__		:   List[Any]  				=					num_channels
									lowerCamelCase__		:   Tuple  				=					qkv_bias







				def   lowercase_		(    self    :Dict  )      ->					Dict[str, any]:




									"""simple docstring"""



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







"""simple docstring"""




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase       :    int									=			logging.get_logger(__name__)

UpperCAmelCase       :    Optional[int]									=			["UtteranceLevel"]



@torch.no_grad()
def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   List[str]  				=					torch.load(_lowercase							, map_location='''cpu'''						)
					if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
										raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

					lowerCamelCase__		:   List[Any]  				=					checkpoint['''Downstream''']

					lowerCamelCase__		:   int  				=					HubertConfig.from_pretrained(_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					HubertForSequenceClassification.from_pretrained(_lowercase							, config=_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					WavaVecaFeatureExtractor.from_pretrained(
					    _lowercase							, return_attention_mask=_lowercase							, do_normalize=_lowercase						)

					if hf_congfig.use_weighted_layer_sum:
										lowerCamelCase__		:   Dict  				=					checkpoint['''Featurizer''']['''weights''']

					lowerCamelCase__		:   Any  				=					downstream_dict['''projector.weight''']
					lowerCamelCase__		:   Optional[int]  				=					downstream_dict['''projector.bias''']
					lowerCamelCase__		:   Dict  				=					downstream_dict['''model.post_net.linear.weight''']
					lowerCamelCase__		:   str  				=					downstream_dict['''model.post_net.linear.bias''']

					hf_feature_extractor.save_pretrained(_lowercase						)
					hf_model.save_pretrained(_lowercase						)


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






