




import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class 		__lowerCAmelCase	(	__magic_name__ ):
							UpperCamelCase__					     =  (DPMSolverSinglestepScheduler,)
							UpperCamelCase__					     =  (('''num_inference_steps''', 25),)
							def       lowerCamelCase__							(						self						:List[str]		,      **__magic_name__						:Union[str, Any]				):







								'''simple docstring'''







								a										=							{
								    """num_train_timesteps""": 1000,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								    """solver_order""": 2,
								    """prediction_type""": """epsilon""",
								    """thresholding""": False,
								    """sample_max_value""": 1.0,
								    """algorithm_type""": """dpmsolver++""",
								    """solver_type""": """midpoint""",
								    """lambda_min_clipped""": -float("""inf"""				),
								    """variance_type""": None,
								}

								config.update(**__magic_name__				)
								return config
							def       lowerCamelCase__							(						self						:Any		,      __magic_name__						:Union[str, Any]=0		,      **__magic_name__						:Union[str, Any]				):







								'''simple docstring'''







								a										=							dict(self.forward_default_kwargs				)
								a										=							kwargs.pop("""num_inference_steps"""		,      __magic_name__				)
								a										=							self.dummy_sample
								a										=							0.1 * sample
								a										=							[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
									a										=							self.get_scheduler_config(**__magic_name__				)
									a										=							scheduler_class(**__magic_name__				)
									scheduler.set_timesteps(__magic_name__				)
									# copy over dummy past residuals
									a										=							dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(__magic_name__				)
										a										=							scheduler_class.from_pretrained(__magic_name__				)
										new_scheduler.set_timesteps(__magic_name__				)
										# copy over dummy past residuals
										a										=							dummy_past_residuals[: new_scheduler.config.solver_order]

									a				, a										=							sample, sample
									for t in range(__magic_name__		,      time_step + scheduler.config.solver_order + 1				):
										a										=							scheduler.step(__magic_name__		,      __magic_name__		,      __magic_name__		,      **__magic_name__				).prev_sample
										a										=							new_scheduler.step(__magic_name__		,      __magic_name__		,      __magic_name__		,      **__magic_name__				).prev_sample

										assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								pass
							def       lowerCamelCase__							(						self						:Optional[Any]		,      __magic_name__						:str=0		,      **__magic_name__						:int				):







								'''simple docstring'''







								a										=							dict(self.forward_default_kwargs				)
								a										=							kwargs.pop("""num_inference_steps"""		,      __magic_name__				)
								a										=							self.dummy_sample
								a										=							0.1 * sample
								a										=							[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
									a										=							self.get_scheduler_config()
									a										=							scheduler_class(**__magic_name__				)
									scheduler.set_timesteps(__magic_name__				)

									# copy over dummy past residuals (must be after setting timesteps)
									a										=							dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(__magic_name__				)
										a										=							scheduler_class.from_pretrained(__magic_name__				)
										# copy over dummy past residuals
										new_scheduler.set_timesteps(__magic_name__				)

										# copy over dummy past residual (must be after setting timesteps)
										a										=							dummy_past_residuals[: new_scheduler.config.solver_order]

									a										=							scheduler.step(__magic_name__		,      __magic_name__		,      __magic_name__		,      **__magic_name__				).prev_sample
									a										=							new_scheduler.step(__magic_name__		,      __magic_name__		,      __magic_name__		,      **__magic_name__				).prev_sample

									assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"
							def       lowerCamelCase__							(						self						:Optional[int]		,      __magic_name__						:Optional[int]=None		,      **__magic_name__						:Union[str, Any]				):







								'''simple docstring'''







								if scheduler is None:
									a										=							self.scheduler_classes[0]
									a										=							self.get_scheduler_config(**__magic_name__				)
									a										=							scheduler_class(**__magic_name__				)

								a										=							self.scheduler_classes[0]
								a										=							self.get_scheduler_config(**__magic_name__				)
								a										=							scheduler_class(**__magic_name__				)

								a										=							10
								a										=							self.dummy_model()
								a										=							self.dummy_sample_deter
								scheduler.set_timesteps(__magic_name__				)

								for i, t in enumerate(scheduler.timesteps				):
									a										=							model(__magic_name__		,      __magic_name__				)
									a										=							scheduler.step(__magic_name__		,      __magic_name__		,      __magic_name__				).prev_sample

								return sample
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							DPMSolverSinglestepScheduler(**self.get_scheduler_config()				)
								a										=							50
								a										=							self.dummy_model()
								a										=							self.dummy_sample_deter
								scheduler.set_timesteps(__magic_name__				)

								# make sure that the first t is uneven
								for i, t in enumerate(scheduler.timesteps[3:]				):
									a										=							model(__magic_name__		,      __magic_name__				)
									a										=							scheduler.step(__magic_name__		,      __magic_name__		,      __magic_name__				).prev_sample

								a										=							torch.mean(torch.abs(__magic_name__				)				)

								assert abs(result_mean.item() - 0.2574				) < 1E-3
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								for timesteps in [25, 50, 100, 999, 1000]:
									self.check_over_configs(num_train_timesteps=__magic_name__				)
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								a										=							DPMSolverSinglestepScheduler(**self.get_scheduler_config()				)
								a										=							self.full_loop(scheduler=__magic_name__				)
								a										=							torch.mean(torch.abs(__magic_name__				)				)

								assert abs(result_mean.item() - 0.2791				) < 1E-3

								a										=							DEISMultistepScheduler.from_config(scheduler.config				)
								a										=							DPMSolverMultistepScheduler.from_config(scheduler.config				)
								a										=							UniPCMultistepScheduler.from_config(scheduler.config				)
								a										=							DPMSolverSinglestepScheduler.from_config(scheduler.config				)

								a										=							self.full_loop(scheduler=__magic_name__				)
								a										=							torch.mean(torch.abs(__magic_name__				)				)

								assert abs(result_mean.item() - 0.2791				) < 1E-3
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								self.check_over_configs(thresholding=__magic_name__				)
								for order in [1, 2, 3]:
									for solver_type in ["midpoint", "heun"]:
										for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample"]:
												self.check_over_configs(
												    thresholding=__magic_name__		,      prediction_type=__magic_name__		,      sample_max_value=__magic_name__		,      algorithm_type="""dpmsolver++"""		,      solver_order=__magic_name__		,      solver_type=__magic_name__		,      )
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=__magic_name__				)
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								for algorithm_type in ["dpmsolver", "dpmsolver++"]:
									for solver_type in ["midpoint", "heun"]:
										for order in [1, 2, 3]:
											for prediction_type in ["epsilon", "sample"]:
												self.check_over_configs(
												    solver_order=__magic_name__		,      solver_type=__magic_name__		,      prediction_type=__magic_name__		,      algorithm_type=__magic_name__		,      )
												a										=							self.full_loop(
												    solver_order=__magic_name__		,      solver_type=__magic_name__		,      prediction_type=__magic_name__		,      algorithm_type=__magic_name__		,      )
												assert not torch.isnan(__magic_name__				).any(), "Samples have nan numbers"
							def       lowerCamelCase__							(						self						:Optional[Any]				):







								'''simple docstring'''







								self.check_over_configs(lower_order_final=__magic_name__				)
								self.check_over_configs(lower_order_final=__magic_name__				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								self.check_over_configs(lambda_min_clipped=-float("""inf"""				)				)
								self.check_over_configs(lambda_min_clipped=-5.1				)
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								self.check_over_configs(variance_type=__magic_name__				)
								self.check_over_configs(variance_type="""learned_range"""				)
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
									self.check_over_forward(num_inference_steps=__magic_name__		,      time_step=0				)
							def       lowerCamelCase__							(						self						:Optional[Any]				):







								'''simple docstring'''







								a										=							self.full_loop()
								a										=							torch.mean(torch.abs(__magic_name__				)				)

								assert abs(result_mean.item() - 0.2791				) < 1E-3
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								a										=							self.full_loop(use_karras_sigmas=__magic_name__				)
								a										=							torch.mean(torch.abs(__magic_name__				)				)

								assert abs(result_mean.item() - 0.2248				) < 1E-3
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							self.full_loop(prediction_type="""v_prediction"""				)
								a										=							torch.mean(torch.abs(__magic_name__				)				)

								assert abs(result_mean.item() - 0.1453				) < 1E-3
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								a										=							self.full_loop(prediction_type="""v_prediction"""		,      use_karras_sigmas=__magic_name__				)
								a										=							torch.mean(torch.abs(__magic_name__				)				)

								assert abs(result_mean.item() - 0.0649				) < 1E-3





							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							self.scheduler_classes[0]
								a										=							self.get_scheduler_config(thresholding=__magic_name__		,      dynamic_thresholding_ratio=0				)
								a										=							scheduler_class(**__magic_name__				)

								a										=							10
								a										=							self.dummy_model()
								a										=							self.dummy_sample_deter.half()
								scheduler.set_timesteps(__magic_name__				)

								for i, t in enumerate(scheduler.timesteps				):
									a										=							model(__magic_name__		,      __magic_name__				)
									a										=							scheduler.step(__magic_name__		,      __magic_name__		,      __magic_name__				).prev_sample

								assert sample.dtype == torch.floataa







import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase					:       List[str]    					=      logging.get_logger(__name__)





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							WavaVecaForSequenceClassification.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	a										=							downstream_dict["""projector.weight"""]
	a										=							downstream_dict["""projector.bias"""]
	a										=							downstream_dict["""model.post_net.linear.weight"""]
	a										=							downstream_dict["""model.post_net.linear.bias"""]
	return model





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							WavaVecaForAudioFrameClassification.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	a										=							downstream_dict["""model.linear.weight"""]
	a										=							downstream_dict["""model.linear.bias"""]
	return model





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Optional[Any]:
	a										=							WavaVecaForXVector.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	a										=							downstream_dict["""connector.weight"""]
	a										=							downstream_dict["""connector.bias"""]
	for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
		a										=							downstream_dict[
		    f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
		]
		a										=							downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

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





@torch.no_grad()
def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							torch.load(__lowerCamelCase							,			map_location="""cpu"""    )

	a										=							checkpoint["""Downstream"""]

	a										=							WavaVecaConfig.from_pretrained(__lowerCamelCase    )
	a										=							WavaVecaFeatureExtractor.from_pretrained(
	    __lowerCamelCase							,			return_attention_mask=__lowerCamelCase							,			do_normalize=__lowerCamelCase    )

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

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

	hf_feature_extractor.save_pretrained(__lowerCamelCase    )
	hf_model.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
		__UpperCamelCase					:       List[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					:       List[Any]    					=      parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

