


"""simple docstring"""



import argparse

import torch

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


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







def 						__lowerCAmelCase		(		lowercase						:						List[str]		,							lowercase						:						List[str]		,							lowercase						:						List[str]				)			->		Tuple:


    """simple docstring"""





    snake_case					:   str         =					UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase_		,							config=lowerCamelCase_				)
    snake_case					:   List[Any]         =					downstream_dict["projector.weight"]
    snake_case					:   Union[str, Any]         =					downstream_dict["projector.bias"]
    snake_case					:   List[str]         =					downstream_dict["model.post_net.linear.weight"]
    snake_case					:   Optional[Any]         =					downstream_dict["model.post_net.linear.bias"]
    return model







def 						__lowerCAmelCase		(		lowercase						:						str		,							lowercase						:						Optional[int]		,							lowercase						:						Optional[Any]				)			->		List[Any]:


    """simple docstring"""





    snake_case					:   Tuple         =					UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase_		,							config=lowerCamelCase_				)
    snake_case					:   Any         =					downstream_dict["model.linear.weight"]
    snake_case					:   List[Any]         =					downstream_dict["model.linear.bias"]
    return model







def 						__lowerCAmelCase		(		lowercase						:						Optional[Any]		,							lowercase						:						Dict		,							lowercase						:						Optional[int]				)			->		List[str]:


    """simple docstring"""





    snake_case					:   Tuple         =					UniSpeechSatForXVector.from_pretrained(lowerCamelCase_		,							config=lowerCamelCase_				)
    snake_case					:   Any         =					downstream_dict["connector.weight"]
    snake_case					:   List[Any]         =					downstream_dict["connector.bias"]
    for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
        snake_case					:   Tuple         =					downstream_dict[
            F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
        ]
        snake_case					:   Dict         =					downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

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







@torch.no_grad()
def 						__lowerCAmelCase		(		lowercase						:						Tuple		,							lowercase						:						List[str]		,							lowercase						:						Dict		,							lowercase						:						Optional[int]				)			->		Any:


    """simple docstring"""





    snake_case					:   Any         =					torch.load(lowerCamelCase_		,							map_location="cpu"				)

    snake_case					:   Optional[int]         =					checkpoint["Downstream"]

    snake_case					:   List[Any]         =					UniSpeechSatConfig.from_pretrained(lowerCamelCase_				)
    snake_case					:   Any         =					WavaVecaFeatureExtractor.from_pretrained(
        lowerCamelCase_		,							return_attention_mask=lowerCamelCase_		,							do_normalize=lowerCamelCase_				)

    snake_case					:   List[str]         =					hf_config.architectures[0]
    if arch.endswith("ForSequenceClassification"				):
        snake_case					:   Tuple         =					convert_classification(lowerCamelCase_		,							lowerCamelCase_		,							lowerCamelCase_				)
    elif arch.endswith("ForAudioFrameClassification"				):
        snake_case					:   int         =					convert_diarization(lowerCamelCase_		,							lowerCamelCase_		,							lowerCamelCase_				)
    elif arch.endswith("ForXVector"				):
        snake_case					:   Optional[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:
        snake_case					:   List[str]         =					checkpoint["Featurizer"]["weights"]

    hf_feature_extractor.save_pretrained(lowerCamelCase_				)
    hf_model.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
   __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.""")
   __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 numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class _lowerCAmelCase       (   unittest.TestCase			):







   @property
   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       torch.manual_seed(0		)
       snake_case					:   Tuple         =					UNetaDModel(
           block_out_channels=(32, 64)		,			layers_per_block=2		,			sample_size=32		,			in_channels=3		,			out_channels=3		,			down_block_types=("DownBlock2D", "AttnDownBlock2D")		,			up_block_types=("AttnUpBlock2D", "UpBlock2D")		,			)
       return model







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					self.dummy_uncond_unet
       snake_case					:   str         =					PNDMScheduler()

       snake_case					:   List[Any]         =					PNDMPipeline(unet=UpperCamelCase__		,			scheduler=UpperCamelCase__		)
       pndm.to(UpperCamelCase__		)
       pndm.set_progress_bar_config(disable=UpperCamelCase__		)

       snake_case					:   str         =					torch.manual_seed(0		)
       snake_case					:   Union[str, Any]         =					pndm(generator=UpperCamelCase__		,			num_inference_steps=20		,			output_type="numpy"		).images

       snake_case					:   Optional[int]         =					torch.manual_seed(0		)
       snake_case					:   Any         =					pndm(generator=UpperCamelCase__		,			num_inference_steps=20		,			output_type="numpy"		,			return_dict=UpperCamelCase__		)[0]

       snake_case					:   Optional[Any]         =					image[0, -3:, -3:, -1]
       snake_case					:   List[str]         =					image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case					:   int         =					np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]		)

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







@slow
@require_torch
class _lowerCAmelCase       (   unittest.TestCase			):







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Dict         =					"google/ddpm-cifar10-32"

       snake_case					:   Optional[int]         =					UNetaDModel.from_pretrained(UpperCamelCase__		)
       snake_case					:   List[str]         =					PNDMScheduler()

       snake_case					:   Union[str, Any]         =					PNDMPipeline(unet=UpperCamelCase__		,			scheduler=UpperCamelCase__		)
       pndm.to(UpperCamelCase__		)
       pndm.set_progress_bar_config(disable=UpperCamelCase__		)
       snake_case					:   int         =					torch.manual_seed(0		)
       snake_case					:   str         =					pndm(generator=UpperCamelCase__		,			output_type="numpy"		).images

       snake_case					:   Union[str, Any]         =					image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case					:   Optional[Any]         =					np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]		)

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





