






import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

__snake_case							 =  ["""UtteranceLevel"""]


@torch.no_grad()
def   _A       (    SCREAMING_SNAKE_CASE__  :     List[Any]					,  SCREAMING_SNAKE_CASE__  :     Optional[int]					,  SCREAMING_SNAKE_CASE__  :     Optional[Any]					,  SCREAMING_SNAKE_CASE__  :     Union[str, Any]	):
      UpperCamelCase	:Tuple     							=       torch.load(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}'''	)

      UpperCamelCase	:Dict     							=       checkpoint["Downstream"]

      UpperCamelCase	:Optional[Any]     							=       HubertConfig.from_pretrained(snake_case__	)
      UpperCamelCase	:Tuple     							=       HubertForSequenceClassification.from_pretrained(snake_case__					,  config=snake_case__	)
      UpperCamelCase	:Optional[Any]     							=       WavaVecaFeatureExtractor.from_pretrained(
          snake_case__					,  return_attention_mask=snake_case__					,  do_normalize=snake_case__	)

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

      UpperCamelCase	:List[str]     							=       downstream_dict["projector.weight"]
      UpperCamelCase	:Union[str, Any]     							=       downstream_dict["projector.bias"]
      UpperCamelCase	:Any     							=       downstream_dict["model.post_net.linear.weight"]
      UpperCamelCase	:str     							=       downstream_dict["model.post_net.linear.bias"]

      hf_feature_extractor.save_pretrained(snake_case__	)
      hf_model.save_pretrained(snake_case__	)


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













import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

__snake_case							 =  0.2_5 + 0.0j
__snake_case							 =  -0.4 + 0.6j
__snake_case							 =  -0.1 + 0.651j
__snake_case							 =  -2.0
__snake_case							 =  56
__snake_case							 =  2.0
__snake_case							 =  6_66


def   _A       (    SCREAMING_SNAKE_CASE__  :     complex					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray	):
    return numpy.exp(SCREAMING_SNAKE_CASE__	) + c_parameter


def   _A       (    SCREAMING_SNAKE_CASE__  :     complex					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray	):
    return z_values * z_values + c_parameter


def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     int	):
    UpperCamelCase	:List[Any]     							=       numpy.linspace(-window_size					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Any     							=       x.reshape((nb_pixels, 1)	)
    UpperCamelCase	:Tuple     							=       numpy.linspace(-window_size					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:int     							=       y.reshape((1, nb_pixels)	)
    return x + 1.0J * y


def   _A       (    SCREAMING_SNAKE_CASE__  :     Callable[[Any, numpy.ndarray], numpy.ndarray]					,  SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray					,  SCREAMING_SNAKE_CASE__  :     float | None = None					,  ):
    UpperCamelCase	:Dict     							=       z_a.astype('''complex64'''	)
    for _ in range(SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:int     							=       eval_function(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
        if infinity is not None:
            numpy.nan_to_num(SCREAMING_SNAKE_CASE__					,  copy=SCREAMING_SNAKE_CASE__					,  nan=SCREAMING_SNAKE_CASE__	)
            UpperCamelCase	:Tuple     							=       infinity
    return z_n


def   _A       (    SCREAMING_SNAKE_CASE__  :     str					,  SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray					,  ):
    UpperCamelCase	:Any     							=       (abs(SCREAMING_SNAKE_CASE__	)).transpose()
    UpperCamelCase	:Any     							=       abs_z_final[::-1, :]
    pyplot.matshow(abs_z_final < escape_radius	)
    pyplot.title(F'''Julia set of ${function_label}$, $c={function_params}$'''	)
    pyplot.show()


def   _A       (    ):
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in multiply'''	)
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''invalid value encountered in multiply'''					,  )
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in absolute'''	)
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in exp'''	)


if __name__ == "__main__":
 __snake_case							 =  prepare_grid(window_size, nb_pixels)

 ignore_overflow_warnings()  # See file header for explanations

 __snake_case							 =  24
 __snake_case							 =  2 * abs(c_cauliflower) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_cauliflower,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_cauliflower, escape_radius, z_final)

 __snake_case							 =  64
 __snake_case							 =  2 * abs(c_polynomial_a) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

 __snake_case							 =  1_61
 __snake_case							 =  2 * abs(c_polynomial_a) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

 __snake_case							 =  12
 __snake_case							 =  1_0_0_0_0.0
 __snake_case							 =  iterate_function(
     eval_exponential,
     c_exponential,
     nb_iterations,
     z_a + 2,
     infinity=1.0E1_0,
 )
 show_results("""e^z+c""", c_exponential, escape_radius, z_final)





