'''simple docstring'''




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


_A										= logging.get_logger(__name__)

_A										= {
    'google/reformer-crime-and-punishment': (
        'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json'
    ),
    'google/reformer-enwik8': 'https://huggingface.co/google/reformer-enwik8/resolve/main/config.json',
}


class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   A				:							Tuple			     =     '''reformer'''
   A				:							int			     =     ['''past_buckets_states''']
   A				:							str			     =     {}
   def __init__(self							,					_a=64							,					_a=["local", "lsh", "local", "lsh", "local", "lsh"]							,					_a=1.0							,					_a=True							,					_a=[64, 64]							,					_a=[64, 192]							,					_a=0							,					_a=2							,					_a=512							,					_a=None							,					_a="relu"							,					_a=0.05							,					_a=256							,					_a=0.02							,					_a=False							,					_a=1e-12							,					_a=1							,					_a=0							,					_a=0.05							,					_a=64							,					_a=64							,					_a=0.0							,					_a=1							,					_a=0							,					_a=4_096							,					_a=12							,					_a=None							,					_a=1							,					_a=0							,					_a=320							,					_a=False							,					_a=True							,					_a=None							,					**_a							,					)     ->  List[str]:
        lowercase_       :							Dict  					=       hash_seed
        lowercase_       :							Optional[Any]  					=       vocab_size
        lowercase_       :							str  					=       attention_head_size
        lowercase_       :							Any  					=       hidden_size
        lowercase_       :							int  					=       num_attention_heads
        lowercase_       :							Dict  					=       num_hashes
        lowercase_       :							Dict  					=       len(_a					)
        lowercase_       :							str  					=       tuple(_a					) if isinstance(_a							,					_a					) else num_buckets
        lowercase_       :							Dict  					=       lsh_attn_chunk_length
        lowercase_       :							Optional[Any]  					=       local_attn_chunk_length
        lowercase_       :							List[Any]  					=       lsh_num_chunks_after
        lowercase_       :							Dict  					=       lsh_num_chunks_before
        lowercase_       :							Optional[Any]  					=       local_num_chunks_after
        lowercase_       :							List[str]  					=       local_num_chunks_before
        lowercase_       :							Dict  					=       hidden_act
        lowercase_       :							Any  					=       feed_forward_size
        lowercase_       :							Optional[int]  					=       hidden_dropout_prob
        lowercase_       :							Any  					=       lsh_attention_probs_dropout_prob
        lowercase_       :							Tuple  					=       local_attention_probs_dropout_prob
        lowercase_       :							str  					=       max_position_embeddings
        lowercase_       :							List[Any]  					=       initializer_range
        lowercase_       :							str  					=       layer_norm_eps
        lowercase_       :							Any  					=       axial_pos_embds
        lowercase_       :							Tuple  					=       tuple(_a					)
        lowercase_       :							str  					=       tuple(_a					)
        lowercase_       :							Tuple  					=       axial_norm_std
        lowercase_       :							Union[str, Any]  					=       chunk_size_lm_head
        lowercase_       :							Optional[Any]  					=       attn_layers
        lowercase_       :							Tuple  					=       use_cache
        lowercase_       :							Any  					=       classifier_dropout
        super().__init__(
            pad_token_id=_a							,					eos_token_id=_a							,					is_decoder=_a							,					tie_word_embeddings=_a							,					**_a							,					)





'''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)




