


'''simple docstring'''

import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils


class    a__					(			unittest.TestCase   ):







				"""simple docstring"""



				@patch('''diffusers.utils.hub_utils.get_full_repo_name'''		)
				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					'full_repo_name'
										with TemporaryDirectory() as tmpdir:
																# Dummy args values
																__lowerCAmelCase											=					Mock()
																__lowerCAmelCase											=					tmpdir
																__lowerCAmelCase											=					0
																__lowerCAmelCase											=					'hub_token'
																__lowerCAmelCase											=					'dataset_name'
																__lowerCAmelCase											=					0.0_1
																__lowerCAmelCase											=					10_00_00
																__lowerCAmelCase											=					1_00_00
																__lowerCAmelCase											=					0.0_1
																__lowerCAmelCase											=					0.0_2
																__lowerCAmelCase											=					0.0_3
																__lowerCAmelCase											=					0.0_0_0_5
																__lowerCAmelCase											=					0.0_0_0_0_0_1
																__lowerCAmelCase											=					1
																__lowerCAmelCase											=					10
																__lowerCAmelCase											=					0.0_0_1
																__lowerCAmelCase											=					0.1
																__lowerCAmelCase											=					0.2
																__lowerCAmelCase											=					True

																# Model card mush be rendered and saved
																diffusers.utils.hub_utils.create_model_card(_a							,      model_name='''model_name'''		)
																self.assertTrue((Path(_a		) / '''README.md''').is_file()		)


'''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)




