
import argparse

import torch

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


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






def 		_UpperCAmelCase   (	UpperCamelCase:     List[str]			,							UpperCamelCase:     Optional[int]			,							UpperCamelCase:     List[Any]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							UniSpeechSatForSequenceClassification.from_pretrained(snake_case__			,							config=snake_case__  )
	__lowerCAmelCase         =							downstream_dict["projector.weight"]
	__lowerCAmelCase         =							downstream_dict["projector.bias"]
	__lowerCAmelCase         =							downstream_dict["model.post_net.linear.weight"]
	__lowerCAmelCase         =							downstream_dict["model.post_net.linear.bias"]
	return model






def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     Any  ):






	"""simple docstring"""


	__lowerCAmelCase         =							UniSpeechSatForAudioFrameClassification.from_pretrained(snake_case__			,							config=snake_case__  )
	__lowerCAmelCase         =							downstream_dict["model.linear.weight"]
	__lowerCAmelCase         =							downstream_dict["model.linear.bias"]
	return model






def 		_UpperCAmelCase   (	UpperCamelCase:     Any			,							UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     Union[str, Any]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							UniSpeechSatForXVector.from_pretrained(snake_case__			,							config=snake_case__  )
	__lowerCAmelCase         =							downstream_dict["connector.weight"]
	__lowerCAmelCase         =							downstream_dict["connector.bias"]
	for i, kernel_size in enumerate(hf_config.tdnn_kernel  ):
		__lowerCAmelCase         =							downstream_dict[
		    F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
		]
		__lowerCAmelCase         =							downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

	__lowerCAmelCase         =							downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
	__lowerCAmelCase         =							downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
	__lowerCAmelCase         =							downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
	__lowerCAmelCase         =							downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
	__lowerCAmelCase         =							downstream_dict["objective.W"]
	return model






@torch.no_grad()
def 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     int			,							UpperCamelCase:     Dict			,							UpperCamelCase:     Any  ):






	"""simple docstring"""


	__lowerCAmelCase         =							torch.load(snake_case__			,							map_location="cpu"  )

	__lowerCAmelCase         =							checkpoint["Downstream"]

	__lowerCAmelCase         =							UniSpeechSatConfig.from_pretrained(snake_case__  )
	__lowerCAmelCase         =							WavaVecaFeatureExtractor.from_pretrained(
	    snake_case__			,							return_attention_mask=snake_case__			,							do_normalize=snake_case__  )

	__lowerCAmelCase         =							hf_config.architectures[0]
	if arch.endswith("ForSequenceClassification"  ):
		__lowerCAmelCase         =							convert_classification(snake_case__			,							snake_case__			,							snake_case__  )
	elif arch.endswith("ForAudioFrameClassification"  ):
		__lowerCAmelCase         =							convert_diarization(snake_case__			,							snake_case__			,							snake_case__  )
	elif arch.endswith("ForXVector"  ):
		__lowerCAmelCase         =							convert_xvector(snake_case__			,							snake_case__			,							snake_case__  )
	else:
		raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"  )

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

	hf_feature_extractor.save_pretrained(snake_case__  )
	hf_model.save_pretrained(snake_case__  )


if __name__ == "__main__":
					UpperCamelCase_   	=	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.")
					UpperCamelCase_   	=	parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)








import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


UpperCamelCase_   	=	False

class       a		(   unittest.TestCase					):
							pass

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







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion" )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
								__lowerCAmelCase         =							torch.manual_seed(0 )
								__lowerCAmelCase         =							pipe(
								    image=snake_case__      ,				generator=snake_case__      ,				guidance_scale=7.5      ,				num_inference_steps=50      ,				output_type="numpy"      ,				).images

								__lowerCAmelCase         =							image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								__lowerCAmelCase         =							np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5] )

								assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






