



"""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   A_    (     _lowercase,   _lowercase,   _lowercase,   _lowercase      ):







						'''simple docstring'''


						snake_case_					:List[Any]		=     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}"""      )

						snake_case_					:Optional[Any]		=     checkpoint["""Downstream"""]

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

						if hf_congfig.use_weighted_layer_sum:
												snake_case_					:str		=     checkpoint["""Featurizer"""]["""weights"""]

						snake_case_					:Union[str, Any]		=     downstream_dict["""projector.weight"""]
						snake_case_					:Optional[Any]		=     downstream_dict["""projector.bias"""]
						snake_case_					:str		=     downstream_dict["""model.post_net.linear.weight"""]
						snake_case_					:Optional[int]		=     downstream_dict["""model.post_net.linear.bias"""]

						hf_feature_extractor.save_pretrained(_lowercase      )
						hf_model.save_pretrained(_lowercase      )


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)





"""simple docstring"""
from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest
@custom_tokenizers
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					def   lowerCAmelCase_							(			self:				Optional[int]		,	snake_case:				Optional[Any]		,	snake_case:				Optional[Any]		,	snake_case:				Tuple		,	snake_case:				int		,	snake_case:				Optional[Any]		)  ->      int:
											return True






					def   lowerCAmelCase_							(			self:				Tuple		)  ->      Optional[int]:
											snake_case_					:Dict		=     CpmTokenizer.from_pretrained("""TsinghuaAI/CPM-Generate"""		)
											snake_case_					:Any		=     """Hugging Face大法好，谁用谁知道。"""
											snake_case_					:Union[str, Any]		=     """Hugging Face大法好,谁用谁知道。<unk>"""
											snake_case_					:Dict		=     """▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。""".split()

											snake_case_					:str		=     tokenizer.tokenize(snake_case		)
											self.assertListEqual(snake_case		,	snake_case		)

											snake_case_					:List[Any]		=     tokens + [tokenizer.unk_token]

											snake_case_					:Optional[Any]		=     [13_789, 13_283, 1_421, 8, 10, 1_164, 13_608, 16_528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case		)		,	snake_case		)

											snake_case_					:int		=     tokenizer.decode(snake_case		)
											self.assertEqual(snake_case		,	snake_case		)
