






"""simple docstring"""


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

__SCREAMING_SNAKE_CASE      =      ['''UtteranceLevel''']


@torch.no_grad()
def 					UpperCAmelCase			(				a__    ,			a__    ,			a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :List[Any]				  = torch.load(__lowerCAmelCase    ,			map_location='cpu'   )
						if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
												raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""   )

						lowerCAmelCase      :Tuple				  = checkpoint["Downstream"]

						lowerCAmelCase      :Dict				  = HubertConfig.from_pretrained(__lowerCAmelCase   )
						lowerCAmelCase      :Union[str, Any]				  = HubertForSequenceClassification.from_pretrained(__lowerCAmelCase    ,			config=__lowerCAmelCase   )
						lowerCAmelCase      :List[Any]				  = WavaVecaFeatureExtractor.from_pretrained(
						    __lowerCAmelCase    ,			return_attention_mask=__lowerCAmelCase    ,			do_normalize=__lowerCAmelCase   )

						if hf_congfig.use_weighted_layer_sum:
												lowerCAmelCase      :List[Any]				  = checkpoint["Featurizer"]["weights"]

						lowerCAmelCase      :Optional[int]				  = downstream_dict["projector.weight"]
						lowerCAmelCase      :Union[str, Any]				  = downstream_dict["projector.bias"]
						lowerCAmelCase      :List[Any]				  = downstream_dict["model.post_net.linear.weight"]
						lowerCAmelCase      :Optional[Any]				  = downstream_dict["model.post_net.linear.bias"]

						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)







"""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 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					Optional[Any]:
											lowerCAmelCase      :Union[str, Any]				  = 'runwayml/stable-diffusion-v1-5'
											lowerCAmelCase      :Tuple				  = TextToVideoZeroPipeline.from_pretrained(UpperCAmelCase    ,   torch_dtype=torch.floataa      ).to('cuda'      )
											lowerCAmelCase      :Union[str, Any]				  = DDIMScheduler.from_config(pipe.scheduler.config      )
											lowerCAmelCase      :str				  = torch.Generator(device='cuda'      ).manual_seed(0      )

											lowerCAmelCase      :Any				  = 'A bear is playing a guitar on Times Square'
											lowerCAmelCase      :Optional[Any]				  = pipe(prompt=UpperCAmelCase    ,   generator=UpperCAmelCase      ).images

											lowerCAmelCase      :List[str]				  = 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(UpperCAmelCase    ,   UpperCAmelCase      )