

"""simple docstring"""





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 __UpperCAmelCase			:      Tuple     	=			(EulerAncestralDiscreteScheduler,)
 __UpperCAmelCase			:      Dict     	=			1_0


 def       _lowercase				(    self							:    Optional[int],      **UpperCAmelCase__							:    Union[str, Any]			):
    __lowercase			=		{
        "num_train_timesteps": 1_1_0_0,
        "beta_start": 0.0_001,
        "beta_end": 0.02,
        "beta_schedule": "linear",
    }

    config.update(**UpperCAmelCase__			)
    return config


 def       _lowercase				(    self							:    Optional[Any]			):
    for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=UpperCAmelCase__			)


 def       _lowercase				(    self							:    Optional[int]			):
    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							:    Tuple			):
    for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=UpperCAmelCase__			)


 def       _lowercase				(    self							:    Union[str, Any]			):
    for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=UpperCAmelCase__			)


 def       _lowercase				(    self							:    str			):
    __lowercase			=		self.scheduler_classes[0]
    __lowercase			=		self.get_scheduler_config()
    __lowercase			=		scheduler_class(**UpperCAmelCase__			)

    scheduler.set_timesteps(self.num_inference_steps			)

    __lowercase			=		torch.manual_seed(0			)

    __lowercase			=		self.dummy_model()
    __lowercase			=		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    __lowercase			=		sample.to(UpperCAmelCase__			)

    for i, t in enumerate(scheduler.timesteps			):
       __lowercase			=		scheduler.scale_model_input(UpperCAmelCase__,      UpperCAmelCase__			)

       __lowercase			=		model(UpperCAmelCase__,      UpperCAmelCase__			)

       __lowercase			=		scheduler.step(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      generator=UpperCAmelCase__			)
       __lowercase			=		output.prev_sample

    __lowercase			=		torch.sum(torch.abs(UpperCAmelCase__			)			)
    __lowercase			=		torch.mean(torch.abs(UpperCAmelCase__			)			)

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


 def       _lowercase				(    self							:    List[str]			):
    __lowercase			=		self.scheduler_classes[0]
    __lowercase			=		self.get_scheduler_config(prediction_type="v_prediction"			)
    __lowercase			=		scheduler_class(**UpperCAmelCase__			)

    scheduler.set_timesteps(self.num_inference_steps			)

    __lowercase			=		torch.manual_seed(0			)

    __lowercase			=		self.dummy_model()
    __lowercase			=		self.dummy_sample_deter * scheduler.init_noise_sigma
    __lowercase			=		sample.to(UpperCAmelCase__			)

    for i, t in enumerate(scheduler.timesteps			):
       __lowercase			=		scheduler.scale_model_input(UpperCAmelCase__,      UpperCAmelCase__			)

       __lowercase			=		model(UpperCAmelCase__,      UpperCAmelCase__			)

       __lowercase			=		scheduler.step(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      generator=UpperCAmelCase__			)
       __lowercase			=		output.prev_sample

    __lowercase			=		torch.sum(torch.abs(UpperCAmelCase__			)			)
    __lowercase			=		torch.mean(torch.abs(UpperCAmelCase__			)			)

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



 def       _lowercase				(    self							:    Optional[Any]			):
    __lowercase			=		self.scheduler_classes[0]
    __lowercase			=		self.get_scheduler_config()
    __lowercase			=		scheduler_class(**UpperCAmelCase__			)

    scheduler.set_timesteps(self.num_inference_steps,      device=UpperCAmelCase__			)
    __lowercase			=		torch.manual_seed(0			)

    __lowercase			=		self.dummy_model()
    __lowercase			=		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    __lowercase			=		sample.to(UpperCAmelCase__			)

    for t in scheduler.timesteps:
       __lowercase			=		scheduler.scale_model_input(UpperCAmelCase__,      UpperCAmelCase__			)

       __lowercase			=		model(UpperCAmelCase__,      UpperCAmelCase__			)

       __lowercase			=		scheduler.step(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      generator=UpperCAmelCase__			)
       __lowercase			=		output.prev_sample

    __lowercase			=		torch.sum(torch.abs(UpperCAmelCase__			)			)
    __lowercase			=		torch.mean(torch.abs(UpperCAmelCase__			)			)

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







"""simple docstring"""





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_a				   =							['UtteranceLevel']




@torch.no_grad()
def 						_A						(						UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : int,    UpperCamelCase_  : Optional[int],    UpperCamelCase_  : Union[str, Any])			->		Any:
   '''simple docstring'''


   __lowercase			=		torch.load(UpperCamelCase_,    map_location="cpu")
   if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
      raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}""")

   __lowercase			=		checkpoint["Downstream"]

   __lowercase			=		HubertConfig.from_pretrained(UpperCamelCase_)
   __lowercase			=		HubertForSequenceClassification.from_pretrained(UpperCamelCase_,    config=UpperCamelCase_)
   __lowercase			=		WavaVecaFeatureExtractor.from_pretrained(
       UpperCamelCase_,    return_attention_mask=UpperCamelCase_,    do_normalize=UpperCamelCase_)

   if hf_congfig.use_weighted_layer_sum:
      __lowercase			=		checkpoint["Featurizer"]["weights"]

   __lowercase			=		downstream_dict["projector.weight"]
   __lowercase			=		downstream_dict["projector.bias"]
   __lowercase			=		downstream_dict["model.post_net.linear.weight"]
   __lowercase			=		downstream_dict["model.post_net.linear.bias"]

   hf_feature_extractor.save_pretrained(UpperCamelCase_)
   hf_model.save_pretrained(UpperCamelCase_)


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




