


'''simple docstring'''

import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
_UpperCAmelCase				: Optional[int]							    =				logging.get_logger(__name__)

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				WavaVecaForSequenceClassification.from_pretrained(lowerCamelCase,				config=lowerCamelCase)
						__lowerCAmelCase							       =				downstream_dict['''projector.weight''']
						__lowerCAmelCase							       =				downstream_dict['''projector.bias''']
						__lowerCAmelCase							       =				downstream_dict['''model.post_net.linear.weight''']
						__lowerCAmelCase							       =				downstream_dict['''model.post_net.linear.bias''']
						return model

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				WavaVecaForAudioFrameClassification.from_pretrained(lowerCamelCase,				config=lowerCamelCase)
						__lowerCAmelCase							       =				downstream_dict['''model.linear.weight''']
						__lowerCAmelCase							       =				downstream_dict['''model.linear.bias''']
						return model

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				WavaVecaForXVector.from_pretrained(lowerCamelCase,				config=lowerCamelCase)
						__lowerCAmelCase							       =				downstream_dict['''connector.weight''']
						__lowerCAmelCase							       =				downstream_dict['''connector.bias''']
						for i, kernel_size in enumerate(hf_config.tdnn_kernel):
												__lowerCAmelCase							       =				downstream_dict[
												    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
												]
												__lowerCAmelCase							       =				downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

						__lowerCAmelCase							       =				downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
						__lowerCAmelCase							       =				downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
						__lowerCAmelCase							       =				downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
						__lowerCAmelCase							       =				downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
						__lowerCAmelCase							       =				downstream_dict['''objective.W''']
						return model

@torch.no_grad()
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				torch.load(lowerCamelCase,				map_location='''cpu''')

						__lowerCAmelCase							       =				checkpoint['''Downstream''']

						__lowerCAmelCase							       =				WavaVecaConfig.from_pretrained(lowerCamelCase)
						__lowerCAmelCase							       =				WavaVecaFeatureExtractor.from_pretrained(
						    lowerCamelCase,				return_attention_mask=lowerCamelCase,				do_normalize=lowerCamelCase)

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

						if hf_config.use_weighted_layer_sum:
												__lowerCAmelCase							       =				checkpoint['''Featurizer''']['''weights''']

						hf_feature_extractor.save_pretrained(lowerCamelCase)
						hf_model.save_pretrained(lowerCamelCase)


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





'''simple docstring'''

from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


_UpperCAmelCase				: str							    =				{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase				: Union[str, Any]							    =				[
	    """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXJapaneseForCausalLM""",
	    """GPTNeoXJapaneseLayer""",
	    """GPTNeoXJapaneseModel""",
	    """GPTNeoXJapanesePreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	_UpperCAmelCase				: int							    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

