


import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property


class        __lowerCamelCase      (				unittest.TestCase ):


				"""simple docstring"""







				@cached_property
				def        lowerCAmelCase__			(				self     :    int					):
								return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""					)






				@cached_property
				def        lowerCAmelCase__			(				self     :    Optional[int]					):
								return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""					)






				def        lowerCAmelCase__			(				self     :    List[str]					):
								_lowerCAmelCase  						=self.tokenizer_ab
								_lowerCAmelCase  						=''' I am a small frog.'''
								_lowerCAmelCase  						=tok([src_text]	,							padding=__UpperCAmelCase	,							truncation=__UpperCAmelCase					)['''input_ids''']
								_lowerCAmelCase  						=tok.batch_decode(__UpperCAmelCase	,							skip_special_tokens=__UpperCAmelCase	,							clean_up_tokenization_spaces=__UpperCAmelCase					)[0]
								assert src_text == decoded






				def        lowerCAmelCase__			(				self     :    Union[str, Any]					):
								_lowerCAmelCase  						=self.rust_tokenizer_ab
								_lowerCAmelCase  						=''' I am a small frog.'''
								_lowerCAmelCase  						=tok([src_text]	,							padding=__UpperCAmelCase	,							truncation=__UpperCAmelCase					)['''input_ids''']
								_lowerCAmelCase  						=tok.batch_decode(__UpperCAmelCase	,							skip_special_tokens=__UpperCAmelCase	,							clean_up_tokenization_spaces=__UpperCAmelCase					)[0]
								assert src_text == decoded






				def        lowerCAmelCase__			(				self     :    Optional[Any]					):
								assert self.tokenizer_ab.add_prefix_space
								assert self.tokenizer_ab([""" Sam""", """Sam"""]					).input_ids == [[5502, 2], [5502, 2]]






				def        lowerCAmelCase__			(				self     :    Optional[Any]					):
								assert self.rust_tokenizer_ab.add_prefix_space
								assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]					).input_ids == [[5502, 2], [5502, 2]]


"""simple docstring"""




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase       :    int									=			logging.get_logger(__name__)

UpperCAmelCase       :    Optional[int]									=			["UtteranceLevel"]



@torch.no_grad()
def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   List[str]  				=					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}"""						)

					lowerCamelCase__		:   List[Any]  				=					checkpoint['''Downstream''']

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

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

					lowerCamelCase__		:   Any  				=					downstream_dict['''projector.weight''']
					lowerCamelCase__		:   Optional[int]  				=					downstream_dict['''projector.bias''']
					lowerCamelCase__		:   Dict  				=					downstream_dict['''model.post_net.linear.weight''']
					lowerCamelCase__		:   str  				=					downstream_dict['''model.post_net.linear.bias''']

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


if __name__ == "__main__":
					UpperCAmelCase       :    int									=			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.")
					UpperCAmelCase       :    Dict									=			parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






