"""simple docstring"""



def 					_lowerCamelCase(					a ,  a							):
       return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2



def 					_lowerCamelCase(					a ,  a=0							):
       return sorted(a ,  key=lambda a							: x[column]							)



def 					_lowerCamelCase(					a ,  a ,  a=float("inf"							)							):

       for i in range(points_counts - 1							):
              for j in range(i + 1 ,  a							):
                     __a     				=  euclidean_distance_sqr(points[i] ,  points[j]							)
                     if current_dis < min_dis:
                            __a     				=  current_dis
       return min_dis



def 					_lowerCamelCase(					a ,  a ,  a=float("inf"							)							):

       for i in range(min(6 ,  points_counts - 1							) ,  a							):
              for j in range(max(0 ,  i - 6							) ,  a							):
                     __a     				=  euclidean_distance_sqr(points[i] ,  points[j]							)
                     if current_dis < min_dis:
                            __a     				=  current_dis
       return min_dis



def 					_lowerCamelCase(					a ,  a ,  a							):

       # base case
       if points_counts <= 3:
              return dis_between_closest_pair(a ,  a							)

       # recursion
       __a     				=  points_counts // 2
       __a     				=  closest_pair_of_points_sqr(
           a ,  points_sorted_on_y[:mid] ,  a							)
       __a     				=  closest_pair_of_points_sqr(
           a ,  points_sorted_on_y[mid:] ,  points_counts - mid							)
       __a     				=  min(a ,  a							)
       __a     				=  []
       for point in points_sorted_on_x:
              if abs(point[0] - points_sorted_on_x[mid][0]							) < closest_pair_dis:
                     cross_strip.append(a							)

       __a     				=  dis_between_closest_in_strip(
           a ,  len(a							) ,  a							)
       return min(a ,  a							)



def 					_lowerCamelCase(					a ,  a							):
       __a     				=  column_based_sort(a ,  column=0							)
       __a     				=  column_based_sort(a ,  column=1							)
       return (
           closest_pair_of_points_sqr(
               a ,  a ,  a							)
       ) ** 0.5


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Tuple			=	[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
   print("""Distance:""", closest_pair_of_points(points, len(points)))

"""simple docstring"""



import argparse

import torch

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


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



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForSequenceClassification.from_pretrained(a ,  config=a							)
       __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 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForAudioFrameClassification.from_pretrained(a ,  config=a							)
       __a     				=  downstream_dict["model.linear.weight"]
       __a     				=  downstream_dict["model.linear.bias"]
       return model



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForXVector.from_pretrained(a ,  config=a							)
       __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 					_lowerCamelCase(					a ,  a ,  a ,  a							):
       __a     				=  torch.load(a ,  map_location="cpu"							)

       __a     				=  checkpoint["Downstream"]

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

       __a     				=  hf_config.architectures[0]
       if arch.endswith("ForSequenceClassification"							):
              __a     				=  convert_classification(a ,  a ,  a							)
       elif arch.endswith("ForAudioFrameClassification"							):
              __a     				=  convert_diarization(a ,  a ,  a							)
       elif arch.endswith("ForXVector"							):
              __a     				=  convert_xvector(a ,  a ,  a							)
       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(a							)
       hf_model.save_pretrained(a							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Union[str, Any]			=	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__:Any			=	parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
