





import argparse

import torch

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


logging.set_verbosity_info()
__lowercase							:Union[str, Any]							=	logging.get_logger(__name__)







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]      , _lowerCamelCase   :	List[str]      , _lowerCamelCase   :	Dict  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					str =       WavLMForSequenceClassification.from_pretrained(_lowerCamelCase      , config=_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					str =       downstream_dict["projector.weight"]
						SCREAMING_SNAKE_CASE__  :					int =       downstream_dict["projector.bias"]
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       downstream_dict["model.post_net.linear.weight"]
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       downstream_dict["model.post_net.linear.bias"]
						return model







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	List[str]      , _lowerCamelCase   :	List[str]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Tuple =       WavLMForAudioFrameClassification.from_pretrained(_lowerCamelCase      , config=_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       downstream_dict["model.linear.weight"]
						SCREAMING_SNAKE_CASE__  :					List[Any] =       downstream_dict["model.linear.bias"]
						return model







def    UpperCAmelCase				(  _lowerCamelCase   :	List[str]      , _lowerCamelCase   :	Tuple      , _lowerCamelCase   :	int  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[str] =       WavLMForXVector.from_pretrained(_lowerCamelCase      , config=_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Tuple =       downstream_dict["connector.weight"]
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       downstream_dict["connector.bias"]
						for i, kernel_size in enumerate(hf_config.tdnn_kernel  ):
												SCREAMING_SNAKE_CASE__  :					List[str] =       downstream_dict[
												    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
												]
												SCREAMING_SNAKE_CASE__  :					int =       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

						SCREAMING_SNAKE_CASE__  :					Any =       downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
						SCREAMING_SNAKE_CASE__  :					str =       downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
						SCREAMING_SNAKE_CASE__  :					str =       downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
						SCREAMING_SNAKE_CASE__  :					Tuple =       downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
						SCREAMING_SNAKE_CASE__  :					Dict =       downstream_dict["objective.W"]
						return model







@torch.no_grad()
def    UpperCAmelCase				(  _lowerCamelCase   :	int      , _lowerCamelCase   :	Any      , _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	Tuple  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       torch.load(_lowerCamelCase      , map_location="cpu"  )

						SCREAMING_SNAKE_CASE__  :					int =       checkpoint["Downstream"]

						SCREAMING_SNAKE_CASE__  :					str =       WavLMConfig.from_pretrained(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Tuple =       WavaVecaFeatureExtractor.from_pretrained(
						    _lowerCamelCase      , return_attention_mask=_lowerCamelCase      , do_normalize=_lowerCamelCase  )

						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       hf_config.architectures[0]
						if arch.endswith("ForSequenceClassification"  ):
												SCREAMING_SNAKE_CASE__  :					str =       convert_classification(_lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase  )
						elif arch.endswith("ForAudioFrameClassification"  ):
												SCREAMING_SNAKE_CASE__  :					List[str] =       convert_diarization(_lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase  )
						elif arch.endswith("ForXVector"  ):
												SCREAMING_SNAKE_CASE__  :					int =       convert_xvector(_lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase  )
						else:
												raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""  )

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

						hf_feature_extractor.save_pretrained(_lowerCamelCase  )
						hf_model.save_pretrained(_lowerCamelCase  )


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






from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  Dict				,						a					:  str				,						a					:  Optional[Any]     )						->Any:
													super().__init__()
													self.register_modules(unet=a				,						scheduler=a     )






							@torch.no_grad()
							def __call__(   self					:  int				,						a					:  int = 1				,						a					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None				,						a					:  int = 10_00				,						a					:  Optional[str] = "pil"				,						a					:  bool = True				,						)						->Union[ImagePipelineOutput, Tuple]:
													# Sample gaussian noise to begin loop
													if isinstance(self.unet.config.sample_size				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					int =       (
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			SCREAMING_SNAKE_CASE__  :					Dict =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													if self.device.type == "mps":
																			# randn does not work reproducibly on mps
																			SCREAMING_SNAKE_CASE__  :					List[str] =       randn_tensor(a				,						generator=a     )
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       image.to(self.device     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       randn_tensor(a				,						generator=a				,						device=self.device     )

													# set step values
													self.scheduler.set_timesteps(a     )

													for t in self.progress_bar(self.scheduler.timesteps     ):
																			# 1. predict noise model_output
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.unet(a				,						a     ).sample

																			# 2. compute previous image: x_t -> x_t-1
																			SCREAMING_SNAKE_CASE__  :					Tuple =       self.scheduler.step(a				,						a				,						a				,						generator=a     ).prev_sample

													SCREAMING_SNAKE_CASE__  :					List[str] =       (image / 2 + 0.5).clamp(0				,						1     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       image.cpu().permute(0				,						2				,						3				,						1     ).numpy()
													if output_type == "pil":
																			SCREAMING_SNAKE_CASE__  :					Dict =       self.numpy_to_pil(a     )

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=a     )