'''simple docstring'''




_A										= [0.33, 0.44, 0.55, 0.44, 0.33]
_A										= 3
_A										= 5

# Buffer Space (with Parameters Space)
_A										= []
_A										= []

# Machine Time
_A										= 0

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     global buffer_space, params_space, machine_time, K, m, t

     # Choosing Dynamical Systems (All)
     for key, value in enumerate(SCREAMING_SNAKE_CASE_				):
          # Evolution Parameter
          lowercase_       :							Dict  					=       float(seed / value				)

          # Control Theory: Orbit Change
          lowercase_       :							str  					=       (buffer_space[(key + 1) % m] + e) % 1

          # Control Theory: Trajectory Change
          lowercase_       :							Optional[int]  					=       (params_space[key] + e) % 1 + 3

          # Modification (Transition Function) - Jumps
          lowercase_       :							Optional[int]  					=       round(float(r * value * (1 - value)				)	,							10				)
          lowercase_       :							Tuple  					=       r  # Saving to Parameters Space

     # Logistic Map
     assert max(SCREAMING_SNAKE_CASE_				) < 1
     assert max(SCREAMING_SNAKE_CASE_				) < 4

     # Machine Time
     machine_time += 1

def _UpperCamelCase (							):
     global buffer_space, params_space, machine_time, K, m, t

     # PRNG (Xorshift by George Marsaglia)
     def xorshift(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
          x ^= y >> 13
          y ^= x << 17
          x ^= y >> 5
          return x

     # Choosing Dynamical Systems (Increment)
     lowercase_       :							Tuple  					=       machine_time % m

     # Evolution (Time Length)
     for _ in range(0	,							SCREAMING_SNAKE_CASE_				):
          # Variables (Position + Parameters)
          lowercase_       :							str  					=       params_space[key]
          lowercase_       :							Tuple  					=       buffer_space[key]

          # Modification (Transition Function) - Flow
          lowercase_       :							List[str]  					=       round(float(r * value * (1 - value)				)	,							10				)
          lowercase_       :							Dict  					=       (machine_time * 0.01 + r * 1.01) % 1 + 3

     # Choosing Chaotic Data
     lowercase_       :							Optional[int]  					=       int(buffer_space[(key + 2) % m] * (10**10)				)
     lowercase_       :							int  					=       int(buffer_space[(key - 2) % m] * (10**10)				)

     # Machine Time
     machine_time += 1

     return xorshift(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				) % 0xff_fff_fff

def _UpperCamelCase (							):
     global buffer_space, params_space, machine_time, K, m, t

     lowercase_       :							Union[str, Any]  					=       K
     lowercase_       :							Union[str, Any]  					=       [0] * m
     lowercase_       :							List[str]  					=       0


if __name__ == "__main__":
 # Initialization
 reset()

 # Pushing Data (Input)
 import random

 _A										= random.sample(range(0Xf_f_f_f_f_f_f_f), 1_0_0)
 for chunk in message:
  push(chunk)

 # for controlling
 _A										= ''

 # Pulling Data (Output)
 while inp in ("e", "E"):
  print(F"""{format(pull(), "#04x")}""")
  print(buffer_space)
  print(params_space)
  _A										= input('(e)exit? ').strip()





'''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 _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							List[str]  					=       torch.load(SCREAMING_SNAKE_CASE_	,							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_       :							Dict  					=       checkpoint['Downstream']

     lowercase_       :							List[Any]  					=       HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
     lowercase_       :							List[Any]  					=       HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_	,							config=SCREAMING_SNAKE_CASE_				)
     lowercase_       :							List[str]  					=       WavaVecaFeatureExtractor.from_pretrained(
         SCREAMING_SNAKE_CASE_	,							return_attention_mask=SCREAMING_SNAKE_CASE_	,							do_normalize=SCREAMING_SNAKE_CASE_				)

     if hf_congfig.use_weighted_layer_sum:
          lowercase_       :							List[str]  					=       checkpoint['Featurizer']['weights']

     lowercase_       :							Tuple  					=       downstream_dict['projector.weight']
     lowercase_       :							List[Any]  					=       downstream_dict['projector.bias']
     lowercase_       :							List[Any]  					=       downstream_dict['model.post_net.linear.weight']
     lowercase_       :							str  					=       downstream_dict['model.post_net.linear.bias']

     hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_				)
     hf_model.save_pretrained(SCREAMING_SNAKE_CASE_				)


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)




