"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase       :    Union[str, Any]									=			logging.get_logger(__name__)

UpperCAmelCase       :    List[Any]									=			{
    "google/rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}

class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase							):
				UpperCAmelCase	=      '''rembert'''







				def __init__(    self    :int      ,__UpperCAmelCase    :Tuple=25_03_00      ,__UpperCAmelCase    :Dict=11_52      ,__UpperCAmelCase    :Union[str, Any]=32      ,__UpperCAmelCase    :List[Any]=18      ,__UpperCAmelCase    :str=2_56      ,__UpperCAmelCase    :Optional[Any]=16_64      ,__UpperCAmelCase    :Optional[int]=46_08      ,__UpperCAmelCase    :List[Any]="gelu"      ,__UpperCAmelCase    :Dict=0.0      ,__UpperCAmelCase    :Optional[Any]=0.0      ,__UpperCAmelCase    :Tuple=0.1      ,__UpperCAmelCase    :List[str]=5_12      ,__UpperCAmelCase    :Tuple=2      ,__UpperCAmelCase    :Any=0.02      ,__UpperCAmelCase    :int=1E-12      ,__UpperCAmelCase    :List[Any]=True      ,__UpperCAmelCase    :Dict=0      ,__UpperCAmelCase    :Union[str, Any]=3_12      ,__UpperCAmelCase    :Optional[int]=3_13      ,**__UpperCAmelCase    :List[str]      ,)      ->					Any:




									"""simple docstring"""



									super().__init__(pad_token_id=__UpperCAmelCase      ,bos_token_id=__UpperCAmelCase      ,eos_token_id=__UpperCAmelCase      ,**__UpperCAmelCase  )

									lowerCamelCase__		:   Any  				=					vocab_size
									lowerCamelCase__		:   List[Any]  				=					input_embedding_size
									lowerCamelCase__		:   str  				=					output_embedding_size
									lowerCamelCase__		:   List[Any]  				=					max_position_embeddings
									lowerCamelCase__		:   Union[str, Any]  				=					hidden_size
									lowerCamelCase__		:   Optional[Any]  				=					num_hidden_layers
									lowerCamelCase__		:   Optional[int]  				=					num_attention_heads
									lowerCamelCase__		:   Tuple  				=					intermediate_size
									lowerCamelCase__		:   Dict  				=					hidden_act
									lowerCamelCase__		:   Union[str, Any]  				=					hidden_dropout_prob
									lowerCamelCase__		:   List[str]  				=					attention_probs_dropout_prob
									lowerCamelCase__		:   Tuple  				=					classifier_dropout_prob
									lowerCamelCase__		:   Union[str, Any]  				=					initializer_range
									lowerCamelCase__		:   Union[str, Any]  				=					type_vocab_size
									lowerCamelCase__		:   Optional[int]  				=					layer_norm_eps
									lowerCamelCase__		:   Optional[Any]  				=					use_cache
									lowerCamelCase__		:   int  				=					False





class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase							):







				@property
				def   lowercase_		(    self    :Any  )      ->					Mapping[str, Mapping[int, str]]:




									"""simple docstring"""



									if self.task == "multiple-choice":
														lowerCamelCase__		:   int  				=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
														lowerCamelCase__		:   Tuple  				=					{0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									        ('''token_type_ids''', dynamic_axis),
									    ]  )







				@property
				def   lowercase_		(    self    :Any  )      ->					float:




									"""simple docstring"""



									return 1E-4







"""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)






