



import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Tuple              =						UniSpeechSatForSequenceClassification.from_pretrained(_lowerCamelCase ,    config=_lowerCamelCase    )
			A							:					Optional[int]              =						downstream_dict["projector.weight"]
			A							:					List[str]              =						downstream_dict["projector.bias"]
			A							:					List[str]              =						downstream_dict["model.post_net.linear.weight"]
			A							:					List[str]              =						downstream_dict["model.post_net.linear.bias"]
			return model




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					str              =						UniSpeechSatForAudioFrameClassification.from_pretrained(_lowerCamelCase ,    config=_lowerCamelCase    )
			A							:					Tuple              =						downstream_dict["model.linear.weight"]
			A							:					str              =						downstream_dict["model.linear.bias"]
			return model




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					int              =						UniSpeechSatForXVector.from_pretrained(_lowerCamelCase ,    config=_lowerCamelCase    )
			A							:					Optional[int]              =						downstream_dict["connector.weight"]
			A							:					str              =						downstream_dict["connector.bias"]
			for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
						A							:					Dict              =						downstream_dict[
						    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
						]
						A							:					Union[str, Any]              =						downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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




@torch.no_grad()
def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[Any]              =						torch.load(_lowerCamelCase ,    map_location="cpu"    )

			A							:					Dict              =						checkpoint["Downstream"]

			A							:					Dict              =						UniSpeechSatConfig.from_pretrained(_lowerCamelCase    )
			A							:					Any              =						WavaVecaFeatureExtractor.from_pretrained(
			    _lowerCamelCase ,    return_attention_mask=_lowerCamelCase ,    do_normalize=_lowerCamelCase    )

			A							:					Dict              =						hf_config.architectures[0]
			if arch.endswith("ForSequenceClassification"    ):
						A							:					str              =						convert_classification(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )
			elif arch.endswith("ForAudioFrameClassification"    ):
						A							:					Optional[int]              =						convert_diarization(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )
			elif arch.endswith("ForXVector"    ):
						A							:					Optional[Any]              =						convert_xvector(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )
			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(_lowerCamelCase    )
			hf_model.save_pretrained(_lowerCamelCase    )


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




from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__SCREAMING_SNAKE_CASE           =  1.0_5_4_5_7_1_8_1_7e-3_4  # unit of ℏ : J * s

__SCREAMING_SNAKE_CASE           =  3e8  # unit of c : m * s^-1




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):

			if (force, area, distance).count(0    ) != 1:
						raise ValueError("One and only one argument must be 0"    )
			if force < 0:
						raise ValueError("Magnitude of force can not be negative"    )
			if distance < 0:
						raise ValueError("Distance can not be negative"    )
			if area < 0:
						raise ValueError("Area can not be negative"    )
			if force == 0:
						A							:					int              =						(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
						    240 * (distance) ** 4
						)
						return {"force": force}
			elif area == 0:
						A							:					Tuple              =						(240 * force * (distance) ** 4) / (
						    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
						)
						return {"area": area}
			elif distance == 0:
						A							:					Dict              =						(
						    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
						) ** (1 / 4)
						return {"distance": distance}
			raise ValueError("One and only one argument must be 0"    )


# Run doctest
if __name__ == "__main__":
							import doctest

							doctest.testmod()