'''simple docstring'''




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

import numpy
from matplotlib import pyplot

_A										= 0.25 + 0.0J
_A										= -0.4 + 0.6J
_A										= -0.1 + 0.651J
_A										= -2.0
_A										= 5_6
_A										= 2.0
_A										= 6_6_6

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     return numpy.exp(SCREAMING_SNAKE_CASE_				) + c_parameter

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     return z_values * z_values + c_parameter

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Tuple  					=       numpy.linspace(-window_size	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				)
     lowercase_       :							List[str]  					=       x.reshape((nb_pixels, 1)				)
     lowercase_       :							List[str]  					=       numpy.linspace(-window_size	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				)
     lowercase_       :							Union[str, Any]  					=       y.reshape((1, nb_pixels)				)
     return x + 1.0J * y

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_ = None	,							):
     lowercase_       :							Union[str, Any]  					=       z_a.astype('complex64'				)
     for _ in range(SCREAMING_SNAKE_CASE_				):
          lowercase_       :							List[Any]  					=       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_				)
               lowercase_       :							List[Any]  					=       infinity
     return z_n

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							):
     lowercase_       :							int  					=       (abs(SCREAMING_SNAKE_CASE_				)).transpose()
     lowercase_       :							Union[str, 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 _UpperCamelCase (							):
     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__":
 _A										= prepare_grid(window_size, nb_pixels)

 ignore_overflow_warnings()  # See file header for explanations

 _A										= 2_4
 _A										= 2 * abs(c_cauliflower) + 1
 _A										= 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)

 _A										= 6_4
 _A										= 2 * abs(c_polynomial_a) + 1
 _A										= 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)

 _A										= 1_6_1
 _A										= 2 * abs(c_polynomial_a) + 1
 _A										= 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)

 _A										= 1_2
 _A										= 1_0000.0
 _A										= iterate_function(
     eval_exponential,
     c_exponential,
     nb_iterations,
     z_a + 2,
     infinity=1.0e10,
 )
 show_results('e^z+c', c_exponential, escape_radius, z_final)





'''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)




