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

import numpy
from matplotlib import pyplot

__lowerCamelCase :     Dict          =  0.25 + 0.0J
__lowerCamelCase :     Optional[Any]          =  -0.4 + 0.6J
__lowerCamelCase :     List[str]          =  -0.1 + 0.651J
__lowerCamelCase :     str          =  -2.0
__lowerCamelCase :     Any          =  56
__lowerCamelCase :     int          =  2.0
__lowerCamelCase :     Dict          =  6_66




def 				_snake_case     (       lowerCAmelCase							:       complex       ,			lowerCAmelCase							:       numpy.ndarray     ):
		"""simple docstring"""







		return numpy.exp(lowerCAmelCase     ) + c_parameter




def 				_snake_case     (       lowerCAmelCase							:       complex       ,			lowerCAmelCase							:       numpy.ndarray     ):
		"""simple docstring"""







		return z_values * z_values + c_parameter




def 				_snake_case     (       lowerCAmelCase							:       float       ,			lowerCAmelCase							:       int     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Dict    				=   numpy.linspace(-window_size       ,			lowerCAmelCase       ,			lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   x.reshape((nb_pixels, 1)     )
		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   numpy.linspace(-window_size       ,			lowerCAmelCase       ,			lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   y.reshape((1, nb_pixels)     )
		return x + 1.0J * y




def 				_snake_case     (       lowerCAmelCase							:       Callable[[Any, numpy.ndarray], numpy.ndarray]       ,			lowerCAmelCase							:       Any       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       numpy.ndarray       ,			lowerCAmelCase							:       float | None = None       ,			):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]    				=   z_a.astype("complex64"     )
		for _ in range(lowerCAmelCase     ):
				SCREAMING_SNAKE_CASE_  :		List[str]    				=   eval_function(lowerCAmelCase       ,			lowerCAmelCase     )
				if infinity is not None:
						numpy.nan_to_num(lowerCAmelCase       ,			copy=lowerCAmelCase       ,			nan=lowerCAmelCase     )
						SCREAMING_SNAKE_CASE_  :		Any    				=   infinity
		return z_n




def 				_snake_case     (       lowerCAmelCase							:       str       ,			lowerCAmelCase							:       Any       ,			lowerCAmelCase							:       float       ,			lowerCAmelCase							:       numpy.ndarray       ,			):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Tuple    				=   (abs(lowerCAmelCase     )).transpose()
		SCREAMING_SNAKE_CASE_  :		int    				=   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 				_snake_case     (       ):
		"""simple docstring"""







		warnings.filterwarnings(
		    "ignore"       ,			category=lowerCAmelCase       ,			message="overflow encountered in multiply"     )
		warnings.filterwarnings(
		    "ignore"       ,			category=lowerCAmelCase       ,			message="invalid value encountered in multiply"       ,			)
		warnings.filterwarnings(
		    "ignore"       ,			category=lowerCAmelCase       ,			message="overflow encountered in absolute"     )
		warnings.filterwarnings(
		    "ignore"       ,			category=lowerCAmelCase       ,			message="overflow encountered in exp"     )


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

		ignore_overflow_warnings()  # See file header for explanations

		__lowerCamelCase :     Any          =  24
		__lowerCamelCase :     Any          =  2 * abs(c_cauliflower) + 1
		__lowerCamelCase :     Dict          =  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)

		__lowerCamelCase :     str          =  64
		__lowerCamelCase :     Any          =  2 * abs(c_polynomial_a) + 1
		__lowerCamelCase :     Dict          =  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)

		__lowerCamelCase :     Dict          =  1_61
		__lowerCamelCase :     List[str]          =  2 * abs(c_polynomial_a) + 1
		__lowerCamelCase :     List[str]          =  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)

		__lowerCamelCase :     Dict          =  12
		__lowerCamelCase :     Tuple          =  10000.0
		__lowerCamelCase :     List[Any]          =  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)

import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()




def 				_snake_case     (       lowerCAmelCase							:       Dict       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       Union[str, Any]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   ConvBertConfig.from_json_file(lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Tuple    				=   ConvBertModel(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Dict    				=   load_tf_weights_in_convbert(lowerCAmelCase       ,			lowerCAmelCase       ,			lowerCAmelCase     )
		model.save_pretrained(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   TFConvBertModel.from_pretrained(lowerCAmelCase       ,			from_pt=lowerCAmelCase     )
		tf_model.save_pretrained(lowerCAmelCase     )


if __name__ == "__main__":
		__lowerCamelCase :     Optional[Any]          =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--convbert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained ConvBERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__lowerCamelCase :     Optional[int]          =  parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
