"""simple docstring"""






import argparse

import torch

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


logging.set_verbosity_info()
UpperCamelCase     					=     logging.get_logger(__name__)

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:List[str]		,			_SCREAMING_SNAKE_CASE	:List[Any]		,			_SCREAMING_SNAKE_CASE	:Tuple  )				-> List[str]:
						a_							:			Any     		=					WavLMForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE		,			config=_SCREAMING_SNAKE_CASE  )
						a_							:			Optional[int]     		=					downstream_dict["projector.weight"]
						a_							:			Any     		=					downstream_dict["projector.bias"]
						a_							:			int     		=					downstream_dict["model.post_net.linear.weight"]
						a_							:			List[str]     		=					downstream_dict["model.post_net.linear.bias"]
						return model

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Tuple		,			_SCREAMING_SNAKE_CASE	:Optional[int]		,			_SCREAMING_SNAKE_CASE	:Any  )				-> List[Any]:
						a_							:			List[Any]     		=					WavLMForAudioFrameClassification.from_pretrained(_SCREAMING_SNAKE_CASE		,			config=_SCREAMING_SNAKE_CASE  )
						a_							:			Optional[int]     		=					downstream_dict["model.linear.weight"]
						a_							:			List[Any]     		=					downstream_dict["model.linear.bias"]
						return model

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Tuple		,			_SCREAMING_SNAKE_CASE	:Tuple		,			_SCREAMING_SNAKE_CASE	:str  )				-> Any:
						a_							:			Optional[Any]     		=					WavLMForXVector.from_pretrained(_SCREAMING_SNAKE_CASE		,			config=_SCREAMING_SNAKE_CASE  )
						a_							:			Tuple     		=					downstream_dict["connector.weight"]
						a_							:			Dict     		=					downstream_dict["connector.bias"]
						for i, kernel_size in enumerate(hf_config.tdnn_kernel  ):
												a_							:			List[Any]     		=					downstream_dict[
												    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
												]
												a_							:			str     		=					downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

						a_							:			Any     		=					downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
						a_							:			Optional[int]     		=					downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
						a_							:			Any     		=					downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
						a_							:			str     		=					downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
						a_							:			Any     		=					downstream_dict["objective.W"]
						return model

@torch.no_grad()
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Tuple		,			_SCREAMING_SNAKE_CASE	:Optional[int]		,			_SCREAMING_SNAKE_CASE	:Optional[Any]		,			_SCREAMING_SNAKE_CASE	:Union[str, Any]  )				-> Optional[Any]:
						a_							:			str     		=					torch.load(_SCREAMING_SNAKE_CASE		,			map_location="cpu"  )

						a_							:			Tuple     		=					checkpoint["Downstream"]

						a_							:			Union[str, Any]     		=					WavLMConfig.from_pretrained(_SCREAMING_SNAKE_CASE  )
						a_							:			str     		=					WavaVecaFeatureExtractor.from_pretrained(
						    _SCREAMING_SNAKE_CASE		,			return_attention_mask=_SCREAMING_SNAKE_CASE		,			do_normalize=_SCREAMING_SNAKE_CASE  )

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

						if hf_config.use_weighted_layer_sum:
												a_							:			Union[str, Any]     		=					checkpoint["Featurizer"]["weights"]

						hf_feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE  )
						hf_model.save_pretrained(_SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
				UpperCamelCase     					=     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     					=     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 ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase     					=     {'configuration_levit': ['LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LevitConfig', 'LevitOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     ['LevitFeatureExtractor']
				UpperCamelCase     					=     ['LevitImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     [
				    'LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'LevitForImageClassification',
				    'LevitForImageClassificationWithTeacher',
				    'LevitModel',
				    'LevitPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_levit import LevitFeatureExtractor
								from .image_processing_levit import LevitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_levit import (
								    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LevitForImageClassification,
								    LevitForImageClassificationWithTeacher,
								    LevitModel,
								    LevitPreTrainedModel,
								)
else:
				import sys

				UpperCamelCase     					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




