
'''simple docstring'''





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
a__						:				str           =    logging.get_logger(__name__)

a__						:				Optional[int]           =    ["UtteranceLevel"]





@torch.no_grad()
def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     List[Any]:







      """simple docstring"""
      __A    		=     torch.load(_A ,					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(_A		)
      __A    		=     HubertForSequenceClassification.from_pretrained(_A ,					config=_A		)
      __A    		=     WavaVecaFeatureExtractor.from_pretrained(
          _A ,					return_attention_mask=_A ,					do_normalize=_A		)

      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(_A		)
      hf_model.save_pretrained(_A		)


if __name__ == "__main__":
      a__						:				Optional[Any]           =    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.")
      a__						:				int           =    parser.parse_args()
      convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)








'''simple docstring'''





import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference


@slow
@require_torch_gpu
class 	UpperCamelCase__					(      unittest.TestCase):






 def 		lowercase_  (     self					:Optional[int]     )			->     List[Any]:







       '''simple docstring'''



       __A    		=     'runwayml/stable-diffusion-v1-5'
       __A    		=     TextToVideoZeroPipeline.from_pretrained(_A		,			torch_dtype=torch.floataa     ).to('cuda'     )
       __A    		=     DDIMScheduler.from_config(pipe.scheduler.config     )
       __A    		=     torch.Generator(device='cuda'     ).manual_seed(0     )

       __A    		=     'A bear is playing a guitar on Times Square'
       __A    		=     pipe(prompt=_A		,			generator=_A     ).images

       __A    		=     load_pt(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'     )

       assert_mean_pixel_difference(_A		,			_A     )






