import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__A    :   Optional[Any]          =       logging.get_logger(__name__)

__A    :   Dict          =       ["UtteranceLevel"]





@torch.no_grad()
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    torch.load(_lowerCAmelCase      ,      map_location='''cpu'''			)
			if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
						raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""			)

			UpperCAmelCase       		=    checkpoint['''Downstream''']

			UpperCAmelCase       		=    HubertConfig.from_pretrained(_lowerCAmelCase			)
			UpperCAmelCase       		=    HubertForSequenceClassification.from_pretrained(_lowerCAmelCase      ,      config=_lowerCAmelCase			)
			UpperCAmelCase       		=    WavaVecaFeatureExtractor.from_pretrained(
			    _lowerCAmelCase      ,      return_attention_mask=_lowerCAmelCase      ,      do_normalize=_lowerCAmelCase			)

			if hf_congfig.use_weighted_layer_sum:
						UpperCAmelCase       		=    checkpoint['''Featurizer''']['''weights''']

			UpperCAmelCase       		=    downstream_dict['''projector.weight''']
			UpperCAmelCase       		=    downstream_dict['''projector.bias''']
			UpperCAmelCase       		=    downstream_dict['''model.post_net.linear.weight''']
			UpperCAmelCase       		=    downstream_dict['''model.post_net.linear.bias''']

			hf_feature_extractor.save_pretrained(_lowerCAmelCase			)
			hf_model.save_pretrained(_lowerCAmelCase			)


if __name__ == "__main__":
				__A    :   Union[str, 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.")
				__A    :   Dict          =       parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__A    :   Any          =       {}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A    :   Union[str, Any]          =       ["CpmTokenizer"]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A    :   List[Any]          =       ["CpmTokenizerFast"]


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_cpm import CpmTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_cpm_fast import CpmTokenizerFast

else:
				import sys

				__A    :   str          =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



