






'''simple docstring'''


import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


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







def 						__magic_name__(			_A    ,   _A    ,   _A       ):
						'''simple docstring'''





						UpperCamelCase__       =			WavLMForSequenceClassification.from_pretrained(_A    ,   config=_A       )
						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"""]
						return model







def 						__magic_name__(			_A    ,   _A    ,   _A       ):
						'''simple docstring'''





						UpperCamelCase__       =			WavLMForAudioFrameClassification.from_pretrained(_A    ,   config=_A       )
						UpperCamelCase__       =			downstream_dict["""model.linear.weight"""]
						UpperCamelCase__       =			downstream_dict["""model.linear.bias"""]
						return model







def 						__magic_name__(			_A    ,   _A    ,   _A       ):
						'''simple docstring'''





						UpperCamelCase__       =			WavLMForXVector.from_pretrained(_A    ,   config=_A       )
						UpperCamelCase__       =			downstream_dict["""connector.weight"""]
						UpperCamelCase__       =			downstream_dict["""connector.bias"""]
						for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
												UpperCamelCase__       =			downstream_dict[
												    f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
												]
												UpperCamelCase__       =			downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

						UpperCamelCase__       =			downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
						UpperCamelCase__       =			downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
						UpperCamelCase__       =			downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
						UpperCamelCase__       =			downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
						UpperCamelCase__       =			downstream_dict["""objective.W"""]
						return model







@torch.no_grad()
def 						__magic_name__(			_A    ,   _A    ,   _A    ,   _A       ):
						'''simple docstring'''





						UpperCamelCase__       =			torch.load(_A    ,   map_location="""cpu"""       )

						UpperCamelCase__       =			checkpoint["""Downstream"""]

						UpperCamelCase__       =			WavLMConfig.from_pretrained(_A       )
						UpperCamelCase__       =			WavaVecaFeatureExtractor.from_pretrained(
						    _A    ,   return_attention_mask=_A    ,   do_normalize=_A       )

						UpperCamelCase__       =			hf_config.architectures[0]
						if arch.endswith("""ForSequenceClassification"""       ):
												UpperCamelCase__       =			convert_classification(_A    ,   _A    ,   _A       )
						elif arch.endswith("""ForAudioFrameClassification"""       ):
												UpperCamelCase__       =			convert_diarization(_A    ,   _A    ,   _A       )
						elif arch.endswith("""ForXVector"""       ):
												UpperCamelCase__       =			convert_xvector(_A    ,   _A    ,   _A       )
						else:
												raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}"       )

						if hf_config.use_weighted_layer_sum:
												UpperCamelCase__       =			checkpoint["""Featurizer"""]["""weights"""]

						hf_feature_extractor.save_pretrained(_A       )
						hf_model.save_pretrained(_A       )


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








'''simple docstring'''


from itertools import combinations_with_replacement







def 						__magic_name__(			_A = 100       ):
						'''simple docstring'''





						UpperCamelCase__       =			[*list(range(1    ,   21       )       ), 25]
						UpperCamelCase__       =			[2 * x for x in range(1    ,   21       )] + [50]
						UpperCamelCase__       =			[3 * x for x in range(1    ,   21       )]
						UpperCamelCase__       =			singles + doubles + triples + [0]

						UpperCamelCase__       =			0
						UpperCamelCase__       =			42
						UpperCamelCase__       =			42
						UpperCamelCase__       =			42
						UpperCamelCase__       =			42

						for double in doubles:
												for throwa, throwa in combinations_with_replacement(_A    ,   2       ):
																		UpperCamelCase__       =			double + throwa + throwa
																		if checkout_total < limit:
																								num_checkouts += 1

						return num_checkouts


if __name__ == "__main__":
					print(f"""{solution() = }""")
