





'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     42
		__lowerCAmelCase								=     42







		def __init__(self    :   Union[str, Any]   ,   _lowerCAmelCase    :   UNetaDModel   ,   _lowerCAmelCase    :   PNDMScheduler   ):
			super().__init__()

			A					=   PNDMScheduler.from_config(scheduler.config   )

			self.register_modules(unet=_lowerCAmelCase   ,   scheduler=_lowerCAmelCase   )







		@torch.no_grad()
		def __call__(self    :   int   ,   _lowerCAmelCase    :   int = 1   ,   _lowerCAmelCase    :   int = 50   ,   _lowerCAmelCase    :   Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,   _lowerCAmelCase    :   Optional[str] = "pil"   ,   _lowerCAmelCase    :   bool = True   ,   **_lowerCAmelCase    :   int   ,   ):
			A					=   randn_tensor(
			    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,   generator=_lowerCAmelCase   ,   device=self.device   ,   )

			self.scheduler.set_timesteps(_lowerCAmelCase   )
			for t in self.progress_bar(self.scheduler.timesteps   ):
				A					=   self.unet(_lowerCAmelCase   ,   _lowerCAmelCase   ).sample

				A					=   self.scheduler.step(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   ).prev_sample

			A					=   (image / 2 + 0.5).clamp(0   ,   1   )
			A					=   image.cpu().permute(0   ,   2   ,   3   ,   1   ).numpy()
			if output_type == "pil":
				A					=   self.numpy_to_pil(_lowerCAmelCase   )

			if not return_dict:
				return (image,)

			return ImagePipelineOutput(images=_lowerCAmelCase   )








'''simple docstring'''


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:						Dict					     =							logging.get_logger(__name__)

_lowerCamelCase					:						List[Any]					     =							['UtteranceLevel']




@torch.no_grad()
def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	A					=   torch.load(UpperCAmelCase							,      map_location="""cpu"""     )
	if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
		raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""     )

	A					=   checkpoint["""Downstream"""]

	A					=   HubertConfig.from_pretrained(UpperCAmelCase     )
	A					=   HubertForSequenceClassification.from_pretrained(UpperCAmelCase							,      config=UpperCAmelCase     )
	A					=   WavaVecaFeatureExtractor.from_pretrained(
	    UpperCAmelCase							,      return_attention_mask=UpperCAmelCase							,      do_normalize=UpperCAmelCase     )

	if hf_congfig.use_weighted_layer_sum:
		A					=   checkpoint["""Featurizer"""]["""weights"""]

	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"""]

	hf_feature_extractor.save_pretrained(UpperCAmelCase     )
	hf_model.save_pretrained(UpperCAmelCase     )


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

