




"""simple docstring"""

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest
class      A__   (   lowercase_  ):
	A_				:					List[str]      =   (VQDiffusionScheduler,)




	def 				a_		( self	:						List[Any]   ,     **lowercase	:						List[Any]						)       ->			int:
						__snake_case        =						{
						    """num_vec_classes""": 4_097,
						    """num_train_timesteps""": 100,
						}

						config.update(**lowercase						)
						return config




	def 				a_		( self	:						List[Any]   ,     lowercase	:						Dict						)       ->			List[str]:
						__snake_case        =						4
						__snake_case        =						8
						__snake_case        =						8

						__snake_case        =						torch.randint(0   ,     lowercase   ,     (batch_size, height * width)						)

						return sample




	@property
	def 				a_		( self	:						List[str]						)       ->			Tuple:
						assert False




	def 				a_		( self	:						Any   ,     lowercase	:						Optional[int]						)       ->			Union[str, Any]:
						def model(lowercase	:						Tuple   ,     lowercase	:						str   ,     *lowercase	:						List[Any]						):
											__snake_case   ,   __snake_case        =						sample.shape
											__snake_case        =						torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
											__snake_case        =						F.log_softmax(logits.double()   ,     dim=1						).float()
											return return_value

						return model




	def 				a_		( self	:						Union[str, Any]						)       ->			Tuple:
						for timesteps in [2, 5, 100, 1_000]:
											self.check_over_configs(num_train_timesteps=lowercase						)




	def 				a_		( self	:						Optional[Any]						)       ->			int:
						for num_vec_classes in [5, 100, 1_000, 4_000]:
											self.check_over_configs(num_vec_classes=lowercase						)




	def 				a_		( self	:						Tuple						)       ->			List[Any]:
						for t in [0, 50, 99]:
											self.check_over_forward(time_step=lowercase						)




	def 				a_		( self	:						Optional[int]						)       ->			Any:
						pass








"""simple docstring"""

import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase__			:     Optional[Any]			       =				logging.get_logger(__name__)




def    snake_case__						(							lowerCAmelCase :		str					,       lowerCAmelCase :		Optional[Any]					,       lowerCAmelCase :		Optional[int]		):
					__snake_case        =						WavLMForSequenceClassification.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__snake_case        =						downstream_dict["""projector.weight"""]
					__snake_case        =						downstream_dict["""projector.bias"""]
					__snake_case        =						downstream_dict["""model.post_net.linear.weight"""]
					__snake_case        =						downstream_dict["""model.post_net.linear.bias"""]
					return model




def    snake_case__						(							lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		Dict					,       lowerCAmelCase :		Dict		):
					__snake_case        =						WavLMForAudioFrameClassification.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__snake_case        =						downstream_dict["""model.linear.weight"""]
					__snake_case        =						downstream_dict["""model.linear.bias"""]
					return model




def    snake_case__						(							lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		Dict					,       lowerCAmelCase :		Dict		):
					__snake_case        =						WavLMForXVector.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__snake_case        =						downstream_dict["""connector.weight"""]
					__snake_case        =						downstream_dict["""connector.bias"""]
					for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
										__snake_case        =						downstream_dict[
										    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
										]
										__snake_case        =						downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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




@torch.no_grad()
def    snake_case__						(							lowerCAmelCase :		str					,       lowerCAmelCase :		Tuple					,       lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Optional[int]		):
					__snake_case        =						torch.load(lowerCAmelCase					,       map_location="""cpu"""		)

					__snake_case        =						checkpoint["""Downstream"""]

					__snake_case        =						WavLMConfig.from_pretrained(lowerCAmelCase		)
					__snake_case        =						WavaVecaFeatureExtractor.from_pretrained(
					    lowerCAmelCase					,       return_attention_mask=lowerCAmelCase					,       do_normalize=lowerCAmelCase		)

					__snake_case        =						hf_config.architectures[0]
					if arch.endswith("""ForSequenceClassification"""		):
										__snake_case        =						convert_classification(lowerCAmelCase					,       lowerCAmelCase					,       lowerCAmelCase		)
					elif arch.endswith("""ForAudioFrameClassification"""		):
										__snake_case        =						convert_diarization(lowerCAmelCase					,       lowerCAmelCase					,       lowerCAmelCase		)
					elif arch.endswith("""ForXVector"""		):
										__snake_case        =						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        =						checkpoint["""Featurizer"""]["""weights"""]

					hf_feature_extractor.save_pretrained(lowerCAmelCase		)
					hf_model.save_pretrained(lowerCAmelCase		)


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


