



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

import numpy
from matplotlib import pyplot

_lowercase				:					Tuple     					=0.2_5 + 0.0J
_lowercase				:					List[str]     					=-0.4 + 0.6J
_lowercase				:					Dict     					=-0.1 + 0.6_5_1J
_lowercase				:					Optional[int]     					=-2.0
_lowercase				:					Any     					=56
_lowercase				:					Any     					=2.0
_lowercase				:					List[str]     					=6_66




def    UpperCAmelCase					(  lowercase__						:					complex				,					lowercase__						:					numpy.ndarray	):



			'''simple docstring'''





			return numpy.exp(lowercase__	) + c_parameter




def    UpperCAmelCase					(  lowercase__						:					complex				,					lowercase__						:					numpy.ndarray	):



			'''simple docstring'''





			return z_values * z_values + c_parameter




def    UpperCAmelCase					(  lowercase__						:					float				,					lowercase__						:					int	):



			'''simple docstring'''





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




def    UpperCAmelCase					(  lowercase__						:					Callable[[Any, numpy.ndarray], numpy.ndarray]				,					lowercase__						:					Any				,					lowercase__						:					int				,					lowercase__						:					numpy.ndarray				,					lowercase__						:					float | None = None				,					):



			'''simple docstring'''





			a__					=  z_a.astype("""complex64"""	)
			for _ in range(lowercase__	):
						a__					=  eval_function(lowercase__				,					lowercase__	)
						if infinity is not None:
									numpy.nan_to_num(lowercase__				,					copy=lowercase__				,					nan=lowercase__	)
									a__					=  infinity
			return z_n




def    UpperCAmelCase					(  lowercase__						:					str				,					lowercase__						:					Any				,					lowercase__						:					float				,					lowercase__						:					numpy.ndarray				,					):



			'''simple docstring'''





			a__					=  (abs(lowercase__	)).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    UpperCAmelCase					(  ):



			'''simple docstring'''





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


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

	ignore_overflow_warnings()  # See file header for explanations

	_lowercase				:					Optional[int]     					=24
	_lowercase				:					Any     					=2 * abs(c_cauliflower) + 1
	_lowercase				:					str     					=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)

	_lowercase				:					Union[str, Any]     					=64
	_lowercase				:					str     					=2 * abs(c_polynomial_a) + 1
	_lowercase				:					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)

	_lowercase				:					Union[str, Any]     					=1_61
	_lowercase				:					Optional[int]     					=2 * abs(c_polynomial_a) + 1
	_lowercase				:					Any     					=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)

	_lowercase				:					List[Any]     					=12
	_lowercase				:					Optional[int]     					=1_0_0_0_0.0
	_lowercase				:					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 . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()




def    UpperCAmelCase					(  lowercase__						:					int				,					lowercase__						:					str				,					lowercase__						:					Union[str, Any]				,					lowercase__						:					Optional[int]				,					lowercase__						:					Optional[Any]	):



			'''simple docstring'''





			a__					=  BertConfig.from_pretrained(
			    """bert-large-cased"""				,					vocab_size=lowercase__				,					max_position_embeddings=512				,					is_decoder=lowercase__				,					add_cross_attention=lowercase__				,					)
			a__					=  bert_config.to_dict()
			del bert_config_dict["type_vocab_size"]
			a__					=  BertGenerationConfig(**lowercase__	)
			if is_encoder:
						a__					=  BertGenerationEncoder(lowercase__	)
			else:
						a__					=  BertGenerationDecoder(lowercase__	)
			print(f'Building PyTorch model from configuration: {config}'	)

			# Load weights from tf checkpoint
			load_tf_weights_in_bert_generation(
			    lowercase__				,					lowercase__				,					model_class="""bert"""				,					is_encoder_named_decoder=lowercase__				,					is_encoder=lowercase__				,					)

			# Save pytorch-model
			print(f'Save PyTorch model and config to {pytorch_dump_path}'	)
			model.save_pretrained(lowercase__	)


if __name__ == "__main__":
	_lowercase				:					str     					=argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--tf_hub_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--is_encoder_named_decoder""",
	    action="""store_true""",
	    help="""If decoder has to be renamed to encoder in PyTorch model.""",
	)
	parser.add_argument("""--is_encoder""", action="""store_true""", help="""If model is an encoder.""")
	parser.add_argument("""--vocab_size""", default=5_03_58, type=int, help="""Vocab size of model""")
	_lowercase				:					List[str]     					=parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.tf_hub_path,
	    args.pytorch_dump_path,
	    args.is_encoder_named_decoder,
	    args.vocab_size,
	    is_encoder=args.is_encoder,
	)

