import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
lowercase_  : Dict								=					logging.get_logger(__name__)







def       A__   (						snake_case_			:		Optional[int]							,					snake_case_			:		int							,					snake_case_			:		Union[str, Any]   ):
 SCREAMING_SNAKE_CASE__: Dict=   UniSpeechSatForSequenceClassification.from_pretrained(snake_case_							,					config=snake_case_   )
 SCREAMING_SNAKE_CASE__: str=   downstream_dict['''projector.weight''']
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   downstream_dict['''projector.bias''']
 SCREAMING_SNAKE_CASE__: Tuple=   downstream_dict['''model.post_net.linear.weight''']
 SCREAMING_SNAKE_CASE__: List[str]=   downstream_dict['''model.post_net.linear.bias''']
 return model







def       A__   (						snake_case_			:		int							,					snake_case_			:		Union[str, Any]							,					snake_case_			:		str   ):
 SCREAMING_SNAKE_CASE__: Optional[int]=   UniSpeechSatForAudioFrameClassification.from_pretrained(snake_case_							,					config=snake_case_   )
 SCREAMING_SNAKE_CASE__: str=   downstream_dict['''model.linear.weight''']
 SCREAMING_SNAKE_CASE__: List[str]=   downstream_dict['''model.linear.bias''']
 return model







def       A__   (						snake_case_			:		Any							,					snake_case_			:		Any							,					snake_case_			:		Dict   ):
 SCREAMING_SNAKE_CASE__: Optional[Any]=   UniSpeechSatForXVector.from_pretrained(snake_case_							,					config=snake_case_   )
 SCREAMING_SNAKE_CASE__: List[str]=   downstream_dict['''connector.weight''']
 SCREAMING_SNAKE_CASE__: str=   downstream_dict['''connector.bias''']
 for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
  SCREAMING_SNAKE_CASE__: List[Any]=   downstream_dict[
      F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
  ]
  SCREAMING_SNAKE_CASE__: Any=   downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

 SCREAMING_SNAKE_CASE__: Union[str, Any]=   downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
 SCREAMING_SNAKE_CASE__: List[Any]=   downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
 SCREAMING_SNAKE_CASE__: Optional[Any]=   downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
 SCREAMING_SNAKE_CASE__: Optional[Any]=   downstream_dict['''objective.W''']
 return model







@torch.no_grad()
def       A__   (						snake_case_			:		Optional[Any]							,					snake_case_			:		Optional[int]							,					snake_case_			:		List[Any]							,					snake_case_			:		Dict   ):
 SCREAMING_SNAKE_CASE__: Optional[Any]=   torch.load(snake_case_							,					map_location='''cpu'''   )

 SCREAMING_SNAKE_CASE__: Union[str, Any]=   checkpoint['''Downstream''']

 SCREAMING_SNAKE_CASE__: Optional[int]=   UniSpeechSatConfig.from_pretrained(snake_case_   )
 SCREAMING_SNAKE_CASE__: Optional[Any]=   WavaVecaFeatureExtractor.from_pretrained(
     snake_case_							,					return_attention_mask=snake_case_							,					do_normalize=snake_case_   )

 SCREAMING_SNAKE_CASE__: int=   hf_config.architectures[0]
 if arch.endswith('''ForSequenceClassification'''   ):
  SCREAMING_SNAKE_CASE__: int=   convert_classification(snake_case_							,					snake_case_							,					snake_case_   )
 elif arch.endswith('''ForAudioFrameClassification'''   ):
  SCREAMING_SNAKE_CASE__: Tuple=   convert_diarization(snake_case_							,					snake_case_							,					snake_case_   )
 elif arch.endswith('''ForXVector'''   ):
  SCREAMING_SNAKE_CASE__: Dict=   convert_xvector(snake_case_							,					snake_case_							,					snake_case_   )
 else:
  raise NotImplementedError(F'S3PRL weights conversion is not supported for {arch}'   )

 if hf_config.use_weighted_layer_sum:
  SCREAMING_SNAKE_CASE__: Any=   checkpoint['''Featurizer''']['''weights''']

 hf_feature_extractor.save_pretrained(snake_case_   )
 hf_model.save_pretrained(snake_case_   )


if __name__ == "__main__":
      lowercase_  : List[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.')
      lowercase_  : List[str]								=					parser.parse_args()
      convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE							:				List[Any]     	=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:				Tuple     	=			{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     """roberta"""



				def __init__(    self,     _lowercase=50265,     _lowercase=768,     _lowercase=12,     _lowercase=12,     _lowercase=3072,     _lowercase="gelu",     _lowercase=0.1,     _lowercase=0.1,     _lowercase=512,     _lowercase=2,     _lowercase=0.02,     _lowercase=1E-12,     _lowercase=1,     _lowercase=0,     _lowercase=2,     _lowercase="absolute",     _lowercase=True,     _lowercase=None,     **_lowercase,     )     ->							Dict:
											super().__init__(pad_token_id=_lowercase,     bos_token_id=_lowercase,     eos_token_id=_lowercase,     **_lowercase       )

											SCREAMING_SNAKE_CASE_				=				vocab_size
											SCREAMING_SNAKE_CASE_				=				hidden_size
											SCREAMING_SNAKE_CASE_				=				num_hidden_layers
											SCREAMING_SNAKE_CASE_				=				num_attention_heads
											SCREAMING_SNAKE_CASE_				=				hidden_act
											SCREAMING_SNAKE_CASE_				=				intermediate_size
											SCREAMING_SNAKE_CASE_				=				hidden_dropout_prob
											SCREAMING_SNAKE_CASE_				=				attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE_				=				max_position_embeddings
											SCREAMING_SNAKE_CASE_				=				type_vocab_size
											SCREAMING_SNAKE_CASE_				=				initializer_range
											SCREAMING_SNAKE_CASE_				=				layer_norm_eps
											SCREAMING_SNAKE_CASE_				=				position_embedding_type
											SCREAMING_SNAKE_CASE_				=				use_cache
											SCREAMING_SNAKE_CASE_				=				classifier_dropout






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				@property
				def   a__					(    self       )     ->							Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE_				=				{0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		SCREAMING_SNAKE_CASE_				=				{0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ]       )





