
"""simple docstring"""



import colorsys

from PIL import Image  # type: ignore


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  float:





    """simple docstring"""

    _UpperCAmelCase          =   x
    _UpperCAmelCase          =   y
    for step in range(_A      ):  # noqa: B007
        _UpperCAmelCase          =   a * a - b * b + x
        _UpperCAmelCase          =   2 * a * b + y
        _UpperCAmelCase          =   a_new

        # divergence happens for all complex number with an absolute value
        # greater than 4
        if a * a + b * b > 4:
            break
    return step / (max_step - 1)


def  _UpperCamelCase	(						_A      )    ->  tuple:





    """simple docstring"""

    if distance == 1:
        return (0, 0, 0)
    else:
        return (2_5_5, 2_5_5, 2_5_5)


def  _UpperCamelCase	(						_A      )    ->  tuple:





    """simple docstring"""

    if distance == 1:
        return (0, 0, 0)
    else:
        return tuple(round(i * 2_5_5      ) for i in colorsys.hsv_to_rgb(_A					,      1					,      1      )      )


def  _UpperCamelCase	(						_A = 8_0_0					,      _A = 6_0_0					,      _A = -0.6					,      _A = 0					,      _A = 3.2					,      _A = 5_0					,      _A = True					,      )    ->  Image.Image:





    """simple docstring"""

    _UpperCAmelCase          =   Image.new("""RGB"""					,      (image_width, image_height)      )
    _UpperCAmelCase          =   img.load()

    # loop through the image-coordinates
    for image_x in range(_A      ):
        for image_y in range(_A      ):
            # determine the figure-coordinates based on the image-coordinates
            _UpperCAmelCase          =   figure_width / image_width * image_height
            _UpperCAmelCase          =   figure_center_x + (image_x / image_width - 0.5) * figure_width
            _UpperCAmelCase          =   figure_center_y + (image_y / image_height - 0.5) * figure_height

            _UpperCAmelCase          =   get_distance(_A					,      _A					,      _A      )

            # color the corresponding pixel based on the selected coloring-function
            if use_distance_color_coding:
                _UpperCAmelCase          =   get_color_coded_rgb(_A      )
            else:
                _UpperCAmelCase          =   get_black_and_white_rgb(_A      )

    return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 a					:	List[str]							=		get_image()

 # uncomment for colored version, different section, zoomed in
 # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
 # figure_width = 0.8)

 # uncomment for black and white version, full figure
 # img = get_image(use_distance_color_coding = False)

 # uncomment to save the image
 # img.save("mandelbrot.png")

 img.show()

"""simple docstring"""



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
 import jax
 import jaxlib

a					:	List[Any]							=		get_logger()

a					:	Optional[dict]							=		None




class 		a_						( TensorFormatter[Mapping, 'jax.Array', Mapping]	):







    def __init__(			self			:       int     ,					__UpperCamelCase			:       List[str]=None     ,					__UpperCamelCase			:       Optional[int]=None     ,					**__UpperCamelCase			:       int     )		->Tuple:






        '''simple docstring'''






        super().__init__(features=__UpperCamelCase     )
        import jax
        from jaxlib.xla_client import Device

        if isinstance(__UpperCamelCase     ,					__UpperCamelCase     ):
            raise ValueError(
                f"""Expected {device} to be a `str` not {type(__UpperCamelCase     )}, as `jaxlib.xla_extension.Device` """
                """is not serializable neither with `pickle` nor with `dill`. Instead you can surround """
                """the device with `str()` to get its string identifier that will be internally mapped """
                """to the actual `jaxlib.xla_extension.Device`."""     )
        _UpperCAmelCase          =   device if isinstance(__UpperCamelCase     ,					__UpperCamelCase     ) else str(jax.devices()[0]     )
        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
        global DEVICE_MAPPING
        if DEVICE_MAPPING is None:
            _UpperCAmelCase          =   self._map_devices_to_str()
        if self.device not in list(DEVICE_MAPPING.keys()     ):
            logger.warning(
                f"""Device with string identifier {self.device} not listed among the available """
                f"""devices: {list(DEVICE_MAPPING.keys()     )}, so falling back to the default """
                f"""device: {str(jax.devices()[0]     )}."""     )
            _UpperCAmelCase          =   str(jax.devices()[0]     )
        _UpperCAmelCase          =   jnp_array_kwargs







    @staticmethod
    def      _snake_case					(			)		->Dict[str, "jaxlib.xla_extension.Device"]:






        '''simple docstring'''






        import jax

        return {str(__UpperCamelCase     ): device for device in jax.devices()}







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       Any     )		->Union[str, Any]:






        '''simple docstring'''






        import jax
        import jax.numpy as jnp

        if isinstance(__UpperCamelCase     ,					__UpperCamelCase     ) and column:
            if all(
                isinstance(__UpperCamelCase     ,					jax.Array     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
                return jnp.stack(__UpperCamelCase     ,					axis=0     )
        return column







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       Any     )		->Optional[int]:






        '''simple docstring'''






        import jax
        import jax.numpy as jnp

        if isinstance(__UpperCamelCase     ,					(str, bytes, type(__UpperCamelCase     ))     ):
            return value
        elif isinstance(__UpperCamelCase     ,					(np.character, np.ndarray)     ) and np.issubdtype(value.dtype     ,					np.character     ):
            return value.tolist()

        _UpperCAmelCase          =   {}

        if isinstance(__UpperCamelCase     ,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype     ,					np.integer     ):
            # the default int precision depends on the jax config
            # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
            if jax.config.jax_enable_xaa:
                _UpperCAmelCase          =   {"""dtype""": jnp.intaa}
            else:
                _UpperCAmelCase          =   {"""dtype""": jnp.intaa}
        elif isinstance(__UpperCamelCase     ,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype     ,					np.floating     ):
            _UpperCAmelCase          =   {"""dtype""": jnp.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
            import PIL.Image

            if isinstance(__UpperCamelCase     ,					PIL.Image.Image     ):
                _UpperCAmelCase          =   np.asarray(__UpperCamelCase     )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
        global DEVICE_MAPPING
        if DEVICE_MAPPING is None:
            _UpperCAmelCase          =   self._map_devices_to_str()

        with jax.default_device(DEVICE_MAPPING[self.device]     ):
            # calling jnp.array on a np.ndarray does copy the data
            # see https://github.com/google/jax/issues/4486
            return jnp.array(__UpperCamelCase     ,					**{**default_dtype, **self.jnp_array_kwargs}     )







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       List[str]     )		->Any:






        '''simple docstring'''






        import jax

        # support for torch, tf, jax etc.
        if config.TORCH_AVAILABLE and "torch" in sys.modules:
            import torch

            if isinstance(__UpperCamelCase     ,					torch.Tensor     ):
                return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
        if hasattr(__UpperCamelCase     ,					"""__array__"""     ) and not isinstance(__UpperCamelCase     ,					jax.Array     ):
            _UpperCAmelCase          =   data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(__UpperCamelCase     ,					np.ndarray     ):
            if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                return self._consolidate([self.recursive_tensorize(__UpperCamelCase     ) for substruct in data_struct]     )
        elif isinstance(__UpperCamelCase     ,					(list, tuple)     ):
            return self._consolidate([self.recursive_tensorize(__UpperCamelCase     ) for substruct in data_struct]     )
        return self._tensorize(__UpperCamelCase     )







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       dict     )		->int:






        '''simple docstring'''






        return map_nested(self._recursive_tensorize     ,					__UpperCamelCase     ,					map_list=__UpperCamelCase     )







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       pa.Table     )		->Mapping:






        '''simple docstring'''






        _UpperCAmelCase          =   self.numpy_arrow_extractor().extract_row(__UpperCamelCase     )
        _UpperCAmelCase          =   self.python_features_decoder.decode_row(__UpperCamelCase     )
        return self.recursive_tensorize(__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       pa.Table     )		->"jax.Array":






        '''simple docstring'''






        _UpperCAmelCase          =   self.numpy_arrow_extractor().extract_column(__UpperCamelCase     )
        _UpperCAmelCase          =   self.python_features_decoder.decode_column(__UpperCamelCase     ,					pa_table.column_names[0]     )
        _UpperCAmelCase          =   self.recursive_tensorize(__UpperCamelCase     )
        _UpperCAmelCase          =   self._consolidate(__UpperCamelCase     )
        return column







    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       pa.Table     )		->Mapping:






        '''simple docstring'''






        _UpperCAmelCase          =   self.numpy_arrow_extractor().extract_batch(__UpperCamelCase     )
        _UpperCAmelCase          =   self.python_features_decoder.decode_batch(__UpperCamelCase     )
        _UpperCAmelCase          =   self.recursive_tensorize(__UpperCamelCase     )
        for column_name in batch:
            _UpperCAmelCase          =   self._consolidate(batch[column_name]     )
        return batch