"""simple docstring"""




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase							):
				UpperCAmelCase	=      (HeunDiscreteScheduler,)
				UpperCAmelCase	=      10







				def   lowercase_		(    self    :Optional[Any]      ,**__UpperCAmelCase    :List[str]  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					{
									    '''num_train_timesteps''': 11_00,
									    '''beta_start''': 0.0_001,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									}

									config.update(**__UpperCAmelCase  )
									return config







				def   lowercase_		(    self    :Tuple  )      ->					Tuple:




									"""simple docstring"""



									for timesteps in [10, 50, 1_00, 10_00]:
														self.check_over_configs(num_train_timesteps=__UpperCAmelCase  )







				def   lowercase_		(    self    :Dict  )      ->					Union[str, Any]:




									"""simple docstring"""



									for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]      ,[0.0_002, 0.002, 0.02]  ):
														self.check_over_configs(beta_start=__UpperCAmelCase      ,beta_end=__UpperCAmelCase  )







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




									"""simple docstring"""



									for schedule in ["linear", "scaled_linear", "exp"]:
														self.check_over_configs(beta_schedule=__UpperCAmelCase  )







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




									"""simple docstring"""



									for clip_sample_range in [1.0, 2.0, 3.0]:
														self.check_over_configs(clip_sample_range=__UpperCAmelCase      ,clip_sample=__UpperCAmelCase  )







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




									"""simple docstring"""



									for prediction_type in ["epsilon", "v_prediction", "sample"]:
														self.check_over_configs(prediction_type=__UpperCAmelCase  )







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




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					self.scheduler_classes[0]
									lowerCamelCase__		:   Union[str, Any]  				=					self.get_scheduler_config()
									lowerCamelCase__		:   List[str]  				=					scheduler_class(**__UpperCAmelCase  )

									scheduler.set_timesteps(self.num_inference_steps  )

									lowerCamelCase__		:   Any  				=					self.dummy_model()
									lowerCamelCase__		:   List[str]  				=					self.dummy_sample_deter * scheduler.init_noise_sigma
									lowerCamelCase__		:   int  				=					sample.to(__UpperCAmelCase  )

									for i, t in enumerate(scheduler.timesteps  ):
														lowerCamelCase__		:   str  				=					scheduler.scale_model_input(__UpperCAmelCase      ,__UpperCAmelCase  )

														lowerCamelCase__		:   Tuple  				=					model(__UpperCAmelCase      ,__UpperCAmelCase  )

														lowerCamelCase__		:   List[Any]  				=					scheduler.step(__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase  )
														lowerCamelCase__		:   Optional[Any]  				=					output.prev_sample

									lowerCamelCase__		:   Union[str, Any]  				=					torch.sum(torch.abs(__UpperCAmelCase  )  )
									lowerCamelCase__		:   Union[str, Any]  				=					torch.mean(torch.abs(__UpperCAmelCase  )  )

									if torch_device in ["cpu", "mps"]:
														assert abs(result_sum.item() - 0.1_233  ) < 1E-2
														assert abs(result_mean.item() - 0.0_002  ) < 1E-3
									else:
														# CUDA
														assert abs(result_sum.item() - 0.1_233  ) < 1E-2
														assert abs(result_mean.item() - 0.0_002  ) < 1E-3







				def   lowercase_		(    self    :Dict  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					self.scheduler_classes[0]
									lowerCamelCase__		:   Any  				=					self.get_scheduler_config(prediction_type='''v_prediction'''  )
									lowerCamelCase__		:   str  				=					scheduler_class(**__UpperCAmelCase  )

									scheduler.set_timesteps(self.num_inference_steps  )

									lowerCamelCase__		:   int  				=					self.dummy_model()
									lowerCamelCase__		:   Union[str, Any]  				=					self.dummy_sample_deter * scheduler.init_noise_sigma
									lowerCamelCase__		:   Optional[Any]  				=					sample.to(__UpperCAmelCase  )

									for i, t in enumerate(scheduler.timesteps  ):
														lowerCamelCase__		:   Dict  				=					scheduler.scale_model_input(__UpperCAmelCase      ,__UpperCAmelCase  )

														lowerCamelCase__		:   int  				=					model(__UpperCAmelCase      ,__UpperCAmelCase  )

														lowerCamelCase__		:   Optional[int]  				=					scheduler.step(__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase  )
														lowerCamelCase__		:   Dict  				=					output.prev_sample

									lowerCamelCase__		:   Any  				=					torch.sum(torch.abs(__UpperCAmelCase  )  )
									lowerCamelCase__		:   List[str]  				=					torch.mean(torch.abs(__UpperCAmelCase  )  )

									if torch_device in ["cpu", "mps"]:
														assert abs(result_sum.item() - 4.6934E-07  ) < 1E-2
														assert abs(result_mean.item() - 6.1112E-10  ) < 1E-3
									else:
														# CUDA
														assert abs(result_sum.item() - 4.693428650170972E-07  ) < 1E-2
														assert abs(result_mean.item() - 0.0_002  ) < 1E-3







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




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					self.scheduler_classes[0]
									lowerCamelCase__		:   str  				=					self.get_scheduler_config()
									lowerCamelCase__		:   int  				=					scheduler_class(**__UpperCAmelCase  )

									scheduler.set_timesteps(self.num_inference_steps      ,device=__UpperCAmelCase  )

									lowerCamelCase__		:   str  				=					self.dummy_model()
									lowerCamelCase__		:   Union[str, Any]  				=					self.dummy_sample_deter.to(__UpperCAmelCase  ) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
														lowerCamelCase__		:   Tuple  				=					scheduler.scale_model_input(__UpperCAmelCase      ,__UpperCAmelCase  )

														lowerCamelCase__		:   Any  				=					model(__UpperCAmelCase      ,__UpperCAmelCase  )

														lowerCamelCase__		:   str  				=					scheduler.step(__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase  )
														lowerCamelCase__		:   List[Any]  				=					output.prev_sample

									lowerCamelCase__		:   str  				=					torch.sum(torch.abs(__UpperCAmelCase  )  )
									lowerCamelCase__		:   Tuple  				=					torch.mean(torch.abs(__UpperCAmelCase  )  )

									if str(__UpperCAmelCase  ).startswith('''cpu'''  ):
														# The following sum varies between 148 and 156 on mps. Why?
														assert abs(result_sum.item() - 0.1_233  ) < 1E-2
														assert abs(result_mean.item() - 0.0_002  ) < 1E-3
									elif str(__UpperCAmelCase  ).startswith('''mps'''  ):
														# Larger tolerance on mps
														assert abs(result_mean.item() - 0.0_002  ) < 1E-2
									else:
														# CUDA
														assert abs(result_sum.item() - 0.1_233  ) < 1E-2
														assert abs(result_mean.item() - 0.0_002  ) < 1E-3







				def   lowercase_		(    self    :Any  )      ->					Dict:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					self.scheduler_classes[0]
									lowerCamelCase__		:   Any  				=					self.get_scheduler_config()
									lowerCamelCase__		:   int  				=					scheduler_class(**__UpperCAmelCase      ,use_karras_sigmas=__UpperCAmelCase  )

									scheduler.set_timesteps(self.num_inference_steps      ,device=__UpperCAmelCase  )

									lowerCamelCase__		:   Optional[Any]  				=					self.dummy_model()
									lowerCamelCase__		:   List[str]  				=					self.dummy_sample_deter.to(__UpperCAmelCase  ) * scheduler.init_noise_sigma
									lowerCamelCase__		:   Optional[int]  				=					sample.to(__UpperCAmelCase  )

									for t in scheduler.timesteps:
														lowerCamelCase__		:   List[Any]  				=					scheduler.scale_model_input(__UpperCAmelCase      ,__UpperCAmelCase  )

														lowerCamelCase__		:   Union[str, Any]  				=					model(__UpperCAmelCase      ,__UpperCAmelCase  )

														lowerCamelCase__		:   int  				=					scheduler.step(__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase  )
														lowerCamelCase__		:   Optional[int]  				=					output.prev_sample

									lowerCamelCase__		:   List[Any]  				=					torch.sum(torch.abs(__UpperCAmelCase  )  )
									lowerCamelCase__		:   Optional[Any]  				=					torch.mean(torch.abs(__UpperCAmelCase  )  )

									assert abs(result_sum.item() - 0.00_015  ) < 1E-2
									assert abs(result_mean.item() - 1.9869554535034695E-07  ) < 1E-2







"""simple docstring"""




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase       :    int									=			logging.get_logger(__name__)

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



@torch.no_grad()
def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   List[str]  				=					torch.load(_lowercase							, 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__		:   List[Any]  				=					checkpoint['''Downstream''']

					lowerCamelCase__		:   int  				=					HubertConfig.from_pretrained(_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					HubertForSequenceClassification.from_pretrained(_lowercase							, config=_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					WavaVecaFeatureExtractor.from_pretrained(
					    _lowercase							, return_attention_mask=_lowercase							, do_normalize=_lowercase						)

					if hf_congfig.use_weighted_layer_sum:
										lowerCamelCase__		:   Dict  				=					checkpoint['''Featurizer''']['''weights''']

					lowerCamelCase__		:   Any  				=					downstream_dict['''projector.weight''']
					lowerCamelCase__		:   Optional[int]  				=					downstream_dict['''projector.bias''']
					lowerCamelCase__		:   Dict  				=					downstream_dict['''model.post_net.linear.weight''']
					lowerCamelCase__		:   str  				=					downstream_dict['''model.post_net.linear.bias''']

					hf_feature_extractor.save_pretrained(_lowercase						)
					hf_model.save_pretrained(_lowercase						)


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






