


from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_  :	int							  =							logging.get_logger(__name__)

snake_case_  :	Tuple							  =							{
    '''dandelin/vilt-b32-mlm''': '''https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'''
}

class   A__							(						UpperCamelCase__     ):
						UpperCAmelCase									=  "vilt"




						def __init__(				self    :					List[str] ,			_a    :					Union[str, Any]=3_0522 ,			_a    :					int=2 ,			_a    :					int=2 ,			_a    :					str=40 ,			_a    :					Union[str, Any]=768 ,			_a    :					List[str]=12 ,			_a    :					List[str]=12 ,			_a    :					int=3072 ,			_a    :					Optional[int]="gelu" ,			_a    :					Tuple=0.0 ,			_a    :					Union[str, Any]=0.0 ,			_a    :					List[str]=0.02 ,			_a    :					Tuple=1E-12 ,			_a    :					Tuple=384 ,			_a    :					str=32 ,			_a    :					Any=3 ,			_a    :					Optional[int]=True ,			_a    :					Dict=-1 ,			_a    :					Union[str, Any]=False ,			_a    :					Optional[Any]=-1 ,			**_a    :					List[Any] ,			)							->   Union[str, Any]:


										"""simple docstring"""





										super().__init__(tie_word_embeddings=_a ,			**_a     )

										_SCREAMING_SNAKE_CASE	     =vocab_size
										_SCREAMING_SNAKE_CASE	     =type_vocab_size
										_SCREAMING_SNAKE_CASE	     =modality_type_vocab_size
										_SCREAMING_SNAKE_CASE	     =max_position_embeddings

										_SCREAMING_SNAKE_CASE	     =hidden_size
										_SCREAMING_SNAKE_CASE	     =num_hidden_layers
										_SCREAMING_SNAKE_CASE	     =num_attention_heads
										_SCREAMING_SNAKE_CASE	     =intermediate_size
										_SCREAMING_SNAKE_CASE	     =hidden_act
										_SCREAMING_SNAKE_CASE	     =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE	     =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE	     =initializer_range
										_SCREAMING_SNAKE_CASE	     =layer_norm_eps

										_SCREAMING_SNAKE_CASE	     =image_size
										_SCREAMING_SNAKE_CASE	     =patch_size
										_SCREAMING_SNAKE_CASE	     =num_channels
										_SCREAMING_SNAKE_CASE	     =qkv_bias
										_SCREAMING_SNAKE_CASE	     =max_image_length
										_SCREAMING_SNAKE_CASE	     =num_images





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_lowerCamelCase					:     Tuple					  =  ["UtteranceLevel"]




@torch.no_grad()
def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       List[Any]					,			UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       Dict				):
				'''simple docstring'''
				_lowerCAmelCase			:			List[str]        =					torch.load(UpperCamelCase_					,			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[str]        =					checkpoint["""Downstream"""]

				_lowerCAmelCase			:			Optional[int]        =					HubertConfig.from_pretrained(UpperCamelCase_				)
				_lowerCAmelCase			:			str        =					HubertForSequenceClassification.from_pretrained(UpperCamelCase_					,			config=UpperCamelCase_				)
				_lowerCAmelCase			:			Union[str, Any]        =					WavaVecaFeatureExtractor.from_pretrained(
				    UpperCamelCase_					,			return_attention_mask=UpperCamelCase_					,			do_normalize=UpperCamelCase_				)

				if hf_congfig.use_weighted_layer_sum:
								_lowerCAmelCase			:			int        =					checkpoint["""Featurizer"""]["""weights"""]

				_lowerCAmelCase			:			Optional[int]        =					downstream_dict["""projector.weight"""]
				_lowerCAmelCase			:			int        =					downstream_dict["""projector.bias"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.weight"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.bias"""]

				hf_feature_extractor.save_pretrained(UpperCamelCase_				)
				hf_model.save_pretrained(UpperCamelCase_				)


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

