





# 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

lowerCAmelCase_        =							get_logger()

lowerCAmelCase_        =							None



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






			def __init__(self   ,				__magic_name__=None   ,				__magic_name__=None   ,				**__magic_name__       )	->  Tuple:


							'''simple docstring'''



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

							if isinstance(__magic_name__   ,				__magic_name__       ):
											raise ValueError(
											    F'''Expected {device} to be a `str` not {type(__magic_name__       )}, 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`.'''       )
							snake_case_		:       Any           =    device if isinstance(__magic_name__   ,				__magic_name__       ) 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:
											snake_case_		:       Dict           =    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]       )}.'''       )
											snake_case_		:       Any           =    str(jax.devices()[0]       )
							snake_case_		:       int           =    jnp_array_kwargs






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


							'''simple docstring'''



							import jax

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






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  List[Any]:


							'''simple docstring'''



							import jax
							import jax.numpy as jnp

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






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Optional[Any]:


							'''simple docstring'''



							import jax
							import jax.numpy as jnp

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

							snake_case_		:       Any           =    {}

							if isinstance(__magic_name__   ,				(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:
															snake_case_		:       List[Any]           =    {'''dtype''': jnp.intaa}
											else:
															snake_case_		:       Dict           =    {'''dtype''': jnp.intaa}
							elif isinstance(__magic_name__   ,				(np.number, np.ndarray)       ) and np.issubdtype(value.dtype   ,				np.floating       ):
											snake_case_		:       Any           =    {'''dtype''': jnp.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
											import PIL.Image

											if isinstance(__magic_name__   ,				PIL.Image.Image       ):
															snake_case_		:       Dict           =    np.asarray(__magic_name__       )

        # 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:
											snake_case_		:       str           =    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(__magic_name__   ,				**{**default_dtype, **self.jnp_array_kwargs}       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  int:


							'''simple docstring'''



							import jax

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

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






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Union[str, Any]:


							'''simple docstring'''



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






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Mapping:


							'''simple docstring'''



							snake_case_		:       Union[str, Any]           =    self.numpy_arrow_extractor().extract_row(__magic_name__       )
							snake_case_		:       str           =    self.python_features_decoder.decode_row(__magic_name__       )
							return self.recursive_tensorize(__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  "jax.Array":


							'''simple docstring'''



							snake_case_		:       Dict           =    self.numpy_arrow_extractor().extract_column(__magic_name__       )
							snake_case_		:       str           =    self.python_features_decoder.decode_column(__magic_name__   ,				pa_table.column_names[0]       )
							snake_case_		:       List[Any]           =    self.recursive_tensorize(__magic_name__       )
							snake_case_		:       Dict           =    self._consolidate(__magic_name__       )
							return column






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Mapping:


							'''simple docstring'''



							snake_case_		:       List[Any]           =    self.numpy_arrow_extractor().extract_batch(__magic_name__       )
							snake_case_		:       Any           =    self.python_features_decoder.decode_batch(__magic_name__       )
							snake_case_		:       Any           =    self.recursive_tensorize(__magic_name__       )
							for column_name in batch:
											snake_case_		:       str           =    self._consolidate(batch[column_name]       )
							return batch








from PIL import Image



def  a__ (__lowercase							:Image					,  __lowercase							:float   )		-> Image:

				def brightness(__lowercase							:int   ) -> float:
								return 128 + level + (c - 128)

				if not -255.0 <= level <= 255.0:
								raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''   )
				return img.point(__lowercase   )


if __name__ == "__main__":
	# Load image
	with Image.open('image_data/lena.jpg') as img:
		# Change brightness to 100
		_UpperCamelCase			:	Any								=change_brightness(img, 100)
		brigt_img.save('image_data/lena_brightness.png', format='png')



