'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_A										= logging.get_logger(__name__)

_A										= {
    'facebook/convnext-tiny-224': 'https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}


class    UpperCAmelCase__	(							_snake_case       ,  _snake_case      ):






   """simple docstring"""
   A				:							Union[str, Any]			     =     '''convnext'''
   def __init__(self							,					_a=3							,					_a=4							,					_a=4							,					_a=None							,					_a=None							,					_a="gelu"							,					_a=0.02							,					_a=1e-12							,					_a=1e-6							,					_a=0.0							,					_a=224							,					_a=None							,					_a=None							,					**_a							,					)     ->  int:
        super().__init__(**_a					)

        lowercase_       :							List[Any]  					=       num_channels
        lowercase_       :							Optional[Any]  					=       patch_size
        lowercase_       :							Optional[Any]  					=       num_stages
        lowercase_       :							List[Any]  					=       [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
        lowercase_       :							Dict  					=       [3, 3, 9, 3] if depths is None else depths
        lowercase_       :							Optional[Any]  					=       hidden_act
        lowercase_       :							Tuple  					=       initializer_range
        lowercase_       :							Any  					=       layer_norm_eps
        lowercase_       :							Dict  					=       layer_scale_init_value
        lowercase_       :							Union[str, Any]  					=       drop_path_rate
        lowercase_       :							Any  					=       image_size
        lowercase_       :							List[str]  					=       ['stem'] + [f'''stage{idx}''' for idx in range(1							,					len(self.depths					) + 1					)]
        lowercase_					,lowercase_       :							Union[str, Any]  					=       get_aligned_output_features_output_indices(
            out_features=_a							,					out_indices=_a							,					stage_names=self.stage_names					)



class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   A				:							Union[str, Any]			     =     version.parse('''1.11'''      )
   @property
   def 		_lowerCamelCase			(self					)     ->  Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ]					)





   @property
   def 		_lowerCamelCase			(self					)     ->  float:
        return 1e-5





'''simple docstring'''




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_A										= ['UtteranceLevel']

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

     lowercase_       :							Dict  					=       checkpoint['Downstream']

     lowercase_       :							List[Any]  					=       HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
     lowercase_       :							List[Any]  					=       HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_	,							config=SCREAMING_SNAKE_CASE_				)
     lowercase_       :							List[str]  					=       WavaVecaFeatureExtractor.from_pretrained(
         SCREAMING_SNAKE_CASE_	,							return_attention_mask=SCREAMING_SNAKE_CASE_	,							do_normalize=SCREAMING_SNAKE_CASE_				)

     if hf_congfig.use_weighted_layer_sum:
          lowercase_       :							List[str]  					=       checkpoint['Featurizer']['weights']

     lowercase_       :							Tuple  					=       downstream_dict['projector.weight']
     lowercase_       :							List[Any]  					=       downstream_dict['projector.bias']
     lowercase_       :							List[Any]  					=       downstream_dict['model.post_net.linear.weight']
     lowercase_       :							str  					=       downstream_dict['model.post_net.linear.bias']

     hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_				)
     hf_model.save_pretrained(SCREAMING_SNAKE_CASE_				)


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)




