import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		(EulerAncestralDiscreteScheduler,)
							_lowerCamelCase						=		10



							def        UpperCamelCase       (						self      ,				**lowercase_				):
													_snake_case						:     Any				=    {
													    "num_train_timesteps": 1_100,
													    "beta_start": 0.0_001,
													    "beta_end": 0.02,
													    "beta_schedule": "linear",
													}

													config.update(**lowercase_				)
													return config



							def        UpperCamelCase       (						self				):
													for timesteps in [10, 50, 100, 1_000]:
																			self.check_over_configs(num_train_timesteps=lowercase_				)



							def        UpperCamelCase       (						self				):
													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=lowercase_      ,				beta_end=lowercase_				)



							def        UpperCamelCase       (						self				):
													for schedule in ["linear", "scaled_linear"]:
																			self.check_over_configs(beta_schedule=lowercase_				)



							def        UpperCamelCase       (						self				):
													for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Any				=    self.scheduler_classes[0]
													_snake_case						:     Dict				=    self.get_scheduler_config()
													_snake_case						:     List[Any]				=    scheduler_class(**lowercase_				)

													scheduler.set_timesteps(self.num_inference_steps				)

													_snake_case						:     Dict				=    torch.manual_seed(0				)

													_snake_case						:     str				=    self.dummy_model()
													_snake_case						:     Optional[int]				=    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
													_snake_case						:     Tuple				=    sample.to(lowercase_				)

													for i, t in enumerate(scheduler.timesteps				):
																			_snake_case						:     Dict				=    scheduler.scale_model_input(lowercase_      ,				lowercase_				)

																			_snake_case						:     Dict				=    model(lowercase_      ,				lowercase_				)

																			_snake_case						:     Union[str, Any]				=    scheduler.step(lowercase_      ,				lowercase_      ,				lowercase_      ,				generator=lowercase_				)
																			_snake_case						:     List[Any]				=    output.prev_sample

													_snake_case						:     Any				=    torch.sum(torch.abs(lowercase_				)				)
													_snake_case						:     Any				=    torch.mean(torch.abs(lowercase_				)				)

													assert abs(result_sum.item() - 152.3_192				) < 1e-2
													assert abs(result_mean.item() - 0.1_983				) < 1e-3



							def        UpperCamelCase       (						self				):
													_snake_case						:     Tuple				=    self.scheduler_classes[0]
													_snake_case						:     Optional[Any]				=    self.get_scheduler_config(prediction_type="v_prediction"				)
													_snake_case						:     Optional[int]				=    scheduler_class(**lowercase_				)

													scheduler.set_timesteps(self.num_inference_steps				)

													_snake_case						:     Dict				=    torch.manual_seed(0				)

													_snake_case						:     Any				=    self.dummy_model()
													_snake_case						:     Any				=    self.dummy_sample_deter * scheduler.init_noise_sigma
													_snake_case						:     str				=    sample.to(lowercase_				)

													for i, t in enumerate(scheduler.timesteps				):
																			_snake_case						:     str				=    scheduler.scale_model_input(lowercase_      ,				lowercase_				)

																			_snake_case						:     Tuple				=    model(lowercase_      ,				lowercase_				)

																			_snake_case						:     int				=    scheduler.step(lowercase_      ,				lowercase_      ,				lowercase_      ,				generator=lowercase_				)
																			_snake_case						:     Dict				=    output.prev_sample

													_snake_case						:     Union[str, Any]				=    torch.sum(torch.abs(lowercase_				)				)
													_snake_case						:     int				=    torch.mean(torch.abs(lowercase_				)				)

													assert abs(result_sum.item() - 108.4_439				) < 1e-2
													assert abs(result_mean.item() - 0.1_412				) < 1e-3



							def        UpperCamelCase       (						self				):
													_snake_case						:     Union[str, Any]				=    self.scheduler_classes[0]
													_snake_case						:     Tuple				=    self.get_scheduler_config()
													_snake_case						:     Union[str, Any]				=    scheduler_class(**lowercase_				)

													scheduler.set_timesteps(self.num_inference_steps      ,				device=lowercase_				)
													_snake_case						:     Union[str, Any]				=    torch.manual_seed(0				)

													_snake_case						:     Dict				=    self.dummy_model()
													_snake_case						:     List[str]				=    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
													_snake_case						:     Optional[Any]				=    sample.to(lowercase_				)

													for t in scheduler.timesteps:
																			_snake_case						:     Union[str, Any]				=    scheduler.scale_model_input(lowercase_      ,				lowercase_				)

																			_snake_case						:     int				=    model(lowercase_      ,				lowercase_				)

																			_snake_case						:     List[str]				=    scheduler.step(lowercase_      ,				lowercase_      ,				lowercase_      ,				generator=lowercase_				)
																			_snake_case						:     Optional[Any]				=    output.prev_sample

													_snake_case						:     Optional[int]				=    torch.sum(torch.abs(lowercase_				)				)
													_snake_case						:     Union[str, Any]				=    torch.mean(torch.abs(lowercase_				)				)

													assert abs(result_sum.item() - 152.3_192				) < 1e-2
													assert abs(result_mean.item() - 0.1_983				) < 1e-3
import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE						:    Dict     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    List[Any]     = ['UtteranceLevel']
@torch.no_grad()
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)       ->       Tuple:

						'''simple docstring'''



						_snake_case						:     Optional[int]				=    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}"""						)

						_snake_case						:     List[str]				=    checkpoint["Downstream"]

						_snake_case						:     List[str]				=    HubertConfig.from_pretrained(__lowercase						)
						_snake_case						:     Union[str, Any]				=    HubertForSequenceClassification.from_pretrained(__lowercase					,			config=__lowercase						)
						_snake_case						:     Union[str, Any]				=    WavaVecaFeatureExtractor.from_pretrained(
						    __lowercase					,			return_attention_mask=__lowercase					,			do_normalize=__lowercase						)

						if hf_congfig.use_weighted_layer_sum:
												_snake_case						:     Optional[int]				=    checkpoint["Featurizer"]["weights"]

						_snake_case						:     str				=    downstream_dict["projector.weight"]
						_snake_case						:     Tuple				=    downstream_dict["projector.bias"]
						_snake_case						:     List[str]				=    downstream_dict["model.post_net.linear.weight"]
						_snake_case						:     str				=    downstream_dict["model.post_net.linear.bias"]

						hf_feature_extractor.save_pretrained(__lowercase						)
						hf_model.save_pretrained(__lowercase						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Optional[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.')
				__SCREAMING_SNAKE_CASE						:    str     = parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)