






"""simple docstring"""






import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


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




def 						_UpperCamelCase			(  A    ,       A    ,       A			):
   UpperCamelCase_     =WavLMForSequenceClassification.from_pretrained(A    ,       config=A			)
   UpperCamelCase_     =downstream_dict["projector.weight"]
   UpperCamelCase_     =downstream_dict["projector.bias"]
   UpperCamelCase_     =downstream_dict["model.post_net.linear.weight"]
   UpperCamelCase_     =downstream_dict["model.post_net.linear.bias"]
   return model




def 						_UpperCamelCase			(  A    ,       A    ,       A			):
   UpperCamelCase_     =WavLMForAudioFrameClassification.from_pretrained(A    ,       config=A			)
   UpperCamelCase_     =downstream_dict["model.linear.weight"]
   UpperCamelCase_     =downstream_dict["model.linear.bias"]
   return model




def 						_UpperCamelCase			(  A    ,       A    ,       A			):
   UpperCamelCase_     =WavLMForXVector.from_pretrained(A    ,       config=A			)
   UpperCamelCase_     =downstream_dict["connector.weight"]
   UpperCamelCase_     =downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
      UpperCamelCase_     =downstream_dict[
          f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      UpperCamelCase_     =downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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




@torch.no_grad()
def 						_UpperCamelCase			(  A    ,       A    ,       A    ,       A			):
   UpperCamelCase_     =torch.load(A    ,       map_location="cpu"			)

   UpperCamelCase_     =checkpoint["Downstream"]

   UpperCamelCase_     =WavLMConfig.from_pretrained(A			)
   UpperCamelCase_     =WavaVecaFeatureExtractor.from_pretrained(
       A    ,       return_attention_mask=A    ,       do_normalize=A			)

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

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

   hf_feature_extractor.save_pretrained(A			)
   hf_model.save_pretrained(A			)


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)








"""simple docstring"""






from math import radians as angle_to_radians
from math import sin

# Acceleration Constant on Earth (unit m/s^2)
A_			  =    9.80665




def 						_UpperCamelCase			(  A    ,       A			):

   # Ensure valid instance
   if not isinstance(A    ,       (int, float)			):
      raise TypeError("Invalid velocity. Should be a positive number."			)

   if not isinstance(A    ,       (int, float)			):
      raise TypeError("Invalid angle. Range is 1-90 degrees."			)

   # Ensure valid angle
   if angle > 90 or angle < 1:
      raise ValueError("Invalid angle. Range is 1-90 degrees."			)

   # Ensure valid velocity
   if init_velocity < 0:
      raise ValueError("Invalid velocity. Should be a positive number."			)




def 						_UpperCamelCase			(  A    ,       A			):
   check_args(A    ,       A			)
   UpperCamelCase_     =angle_to_radians(2 * angle			)
   return round(init_velocity**2 * sin(A			) / g    ,       2			)




def 						_UpperCamelCase			(  A    ,       A			):
   check_args(A    ,       A			)
   UpperCamelCase_     =angle_to_radians(A			)
   return round(init_velocity**2 * sin(A			) ** 2 / (2 * g)    ,       2			)




def 						_UpperCamelCase			(  A    ,       A			):
   check_args(A    ,       A			)
   UpperCamelCase_     =angle_to_radians(A			)
   return round(2 * init_velocity * sin(A			) / g    ,       2			)




def 						_UpperCamelCase			(  ):
   UpperCamelCase_    ,							UpperCamelCase_     =25, 20
   assert horizontal_distance(A    ,       A			) == 40.97
   assert max_height(A    ,       A			) == 3.73
   assert total_time(A    ,       A			) == 1.74


if __name__ == "__main__":
    from doctest import testmod

    testmod()

    # Get input from user
    A_			  =    float(input("Initial Velocity: ").strip())

    # Get input from user
    A_			  =    float(input("angle: ").strip())

    # Print results
    print()
    print("Results: ")
    print(f'''Horizontal Distance: {horizontal_distance(init_vel, angle)!s} [m]''')
    print(f'''Maximum Height: {max_height(init_vel, angle)!s} [m]''')
    print(f'''Total Time: {total_time(init_vel, angle)!s} [s]''')
