import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase_    :		int							=	logging.get_logger(__name__)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       UniSpeechSatForSequenceClassification.from_pretrained(__lowerCamelCase				,    config=__lowerCamelCase						)
					__a		 =       downstream_dict['projector.weight']
					__a		 =       downstream_dict['projector.bias']
					__a		 =       downstream_dict['model.post_net.linear.weight']
					__a		 =       downstream_dict['model.post_net.linear.bias']
					return model
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       UniSpeechSatForAudioFrameClassification.from_pretrained(__lowerCamelCase				,    config=__lowerCamelCase						)
					__a		 =       downstream_dict['model.linear.weight']
					__a		 =       downstream_dict['model.linear.bias']
					return model
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       UniSpeechSatForXVector.from_pretrained(__lowerCamelCase				,    config=__lowerCamelCase						)
					__a		 =       downstream_dict['connector.weight']
					__a		 =       downstream_dict['connector.bias']
					for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
										__a		 =       downstream_dict[
										    f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
										]
										__a		 =       downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

					__a		 =       downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
					__a		 =       downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
					__a		 =       downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
					__a		 =       downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
					__a		 =       downstream_dict['objective.W']
					return model
@torch.no_grad()
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       torch.load(__lowerCamelCase				,    map_location='cpu'						)

					__a		 =       checkpoint['Downstream']

					__a		 =       UniSpeechSatConfig.from_pretrained(__lowerCamelCase						)
					__a		 =       WavaVecaFeatureExtractor.from_pretrained(
					    __lowerCamelCase				,    return_attention_mask=__lowerCamelCase				,    do_normalize=__lowerCamelCase						)

					__a		 =       hf_config.architectures[0]
					if arch.endswith('ForSequenceClassification'						):
										__a		 =       convert_classification(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						)
					elif arch.endswith('ForAudioFrameClassification'						):
										__a		 =       convert_diarization(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						)
					elif arch.endswith('ForXVector'						):
										__a		 =       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		 =       checkpoint['Featurizer']['weights']

					hf_feature_extractor.save_pretrained(__lowerCamelCase						)
					hf_model.save_pretrained(__lowerCamelCase						)


if __name__ == "__main__":
	lowerCamelCase_    :		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.""")
	lowerCamelCase_    :		Union[str, Any]							=	parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class 						a__							(       __snake_case		):
			A__    :       torch.FloatTensor
			A__    :       torch.FloatTensor
			A__    :       Optional[torch.FloatTensor]        =      None






class 						a__							(       __snake_case				,						__snake_case		):
			A__    :       Optional[Any]        =      2



			@register_to_config
			def __init__(						self				,						UpperCAmelCase = 0.02				,						UpperCAmelCase = 1_0_0				,						UpperCAmelCase = 1.007				,						UpperCAmelCase = 8_0				,						UpperCAmelCase = 0.05				,						UpperCAmelCase = 5_0				,						)			->   Optional[Any]:
								# standard deviation of the initial noise distribution
								__a		 =       sigma_max

								# setable values
								__a		 =       None
								__a		 =       None
								__a		 =       None  # sigma(t_i)



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   torch.FloatTensor:
								return sample



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   int:
								__a		 =       num_inference_steps
								__a		 =       np.arange(0				,						self.num_inference_steps   )[::-1].copy()
								__a		 =       torch.from_numpy(UpperCAmelCase   ).to(UpperCAmelCase   )
								__a		 =       [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								__a		 =       torch.tensor(UpperCAmelCase				,						dtype=torch.floataa				,						device=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   Tuple[torch.FloatTensor, float]:
								if self.config.s_min <= sigma <= self.config.s_max:
													__a		 =       min(self.config.s_churn / self.num_inference_steps				,						2**0.5 - 1   )
								else:
													__a		 =       0

								# sample eps ~ N(0, S_noise^2 * I)
								__a		 =       self.config.s_noise * randn_tensor(sample.shape				,						generator=UpperCAmelCase   ).to(sample.device   )
								__a		 =       sigma + gamma * sigma
								__a		 =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						)			->   Union[KarrasVeOutput, Tuple]:
								__a		 =       sample_hat + sigma_hat * model_output
								__a		 =       (sample_hat - pred_original_sample) / sigma_hat
								__a		 =       sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
													return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=UpperCAmelCase				,						derivative=UpperCAmelCase				,						pred_original_sample=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						)			->   Union[KarrasVeOutput, Tuple]:
								__a		 =       sample_prev + sigma_prev * model_output
								__a		 =       (sample_prev - pred_original_sample) / sigma_prev
								__a		 =       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
													return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=UpperCAmelCase				,						derivative=UpperCAmelCase				,						pred_original_sample=UpperCAmelCase   )







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   List[Any]:
								raise NotImplementedError()




