


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 + 0.6_5_1j
_A							     =       -2.0
_A							     =       56
_A							     =       2.0
_A							     =       666

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Union[str, Any]     ,					UpperCamelCase		:      Union[str, Any]	)     ->	Union[str, Any]:



	"""simple docstring"""






	return numpy.exp(SCREAMING_SNAKE_CASE_	) + c_parameter

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      int     ,					UpperCamelCase		:      str	)     ->	List[Any]:



	"""simple docstring"""






	return z_values * z_values + c_parameter

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Union[str, Any]     ,					UpperCamelCase		:      int	)     ->	Any:



	"""simple docstring"""






	a_     			=   numpy.linspace(-window_size     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_	)
	a_     			=   x.reshape((nb_pixels, 1)	)
	a_     			=   numpy.linspace(-window_size     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_	)
	a_     			=   y.reshape((1, nb_pixels)	)
	return x + 1.0J * y

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      List[Any]     ,					UpperCamelCase		:      Optional[int]     ,					UpperCamelCase		:      Dict     ,					UpperCamelCase		:      Optional[int]     ,					UpperCamelCase		:      Optional[Any] = None     ,					)     ->	Any:



	"""simple docstring"""






	a_     			=   z_a.astype("""complex64"""	)
	for _ in range(SCREAMING_SNAKE_CASE_	):
		a_     			=   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_	)
			a_     			=   infinity
	return z_n

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str     ,					UpperCamelCase		:      int     ,					UpperCamelCase		:      str     ,					UpperCamelCase		:      str     ,					)     ->	Optional[int]:



	"""simple docstring"""






	a_     			=   (abs(SCREAMING_SNAKE_CASE_	)).transpose()
	a_     			=   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 			__SCREAMING_SNAKE_CASE		(		)     ->	Tuple:



	"""simple docstring"""






	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							     =       24
			_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							     =       64
			_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							     =       161
			_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							     =       12
			_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)




