'''simple docstring'''





import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

SCREAMING_SNAKE_CASE_:					Dict  =imread(r'digital_image_processing/image_data/lena_small.jpg')
SCREAMING_SNAKE_CASE_:					Optional[int]  =cvtColor(img, COLOR_BGR2GRAY)





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


     '''simple docstring'''




     UpperCAmelCase_	   =       cn.convert_to_negative(_UpperCamelCase      )
     # assert negative_img array for at least one True
     assert negative_img.any()





def      lowerCAmelCase_    ( )   ->				List[Any]:


     '''simple docstring'''




     with Image.open("digital_image_processing/image_data/lena_small.jpg"      ) as img:
          # Work around assertion for response
          assert str(cc.change_contrast(_UpperCamelCase ,   1_10      )      ).startswith(
              "<PIL.Image.Image image mode=RGB size=100x100 at"      )





def      lowerCAmelCase_    ( )   ->				List[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       canny.gen_gaussian_kernel(9 ,   sigma=1.4      )
     # Assert ambiguous array
     assert resp.all()





def      lowerCAmelCase_    ( )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_	   =       imread("digital_image_processing/image_data/lena_small.jpg" ,   0      )
     # assert ambiguous array for all == True
     assert canny_img.all()
     UpperCAmelCase_	   =       canny.canny(_UpperCamelCase      )
     # assert canny array for at least one True
     assert canny_array.any()





def      lowerCAmelCase_    ( )   ->				Any:


     '''simple docstring'''




     assert gg.gaussian_filter(_UpperCamelCase ,   5 ,   sigma=0.9      ).all()





def      lowerCAmelCase_    ( )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]      )
     UpperCAmelCase_	   =       conv.img_convolve(_UpperCamelCase ,   _UpperCamelCase      ).astype(_UpperCamelCase      )
     assert res.any()





def      lowerCAmelCase_    ( )   ->				List[Any]:


     '''simple docstring'''




     assert med.median_filter(_UpperCamelCase ,   3      ).any()





def      lowerCAmelCase_    ( )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_   ,			UpperCAmelCase_	   =       sob.sobel_filter(_UpperCamelCase      )
     assert grad.any() and theta.any()





def      lowerCAmelCase_    ( )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       sp.make_sepia(_UpperCamelCase ,   20      )
     assert sepia.all()





def      lowerCAmelCase_    ( snake_case_	:			str = "digital_image_processing/image_data/lena_small.jpg"      )   ->				str:


     '''simple docstring'''




     UpperCAmelCase_	   =       bs.Burkes(imread(_UpperCamelCase ,   1      ) ,   1_20      )
     burkes.process()
     assert burkes.output_img.any()





def      lowerCAmelCase_    ( snake_case_	:			str = "digital_image_processing/image_data/lena_small.jpg" ,   )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       rs.NearestNeighbour(imread(_UpperCamelCase ,   1      ) ,   4_00 ,   2_00      )
     nn.process()
     assert nn.output.any()






def      lowerCAmelCase_    ( )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       "digital_image_processing/image_data/lena.jpg"

     # Reading the image and converting it to grayscale.
     UpperCAmelCase_	   =       imread(_UpperCamelCase ,   0      )

     # Test for get_neighbors_pixel function() return not None
     UpperCAmelCase_	   =       0
     UpperCAmelCase_	   =       0
     UpperCAmelCase_	   =       image[x_coordinate][y_coordinate]

     UpperCAmelCase_	   =       lbp.get_neighbors_pixel(
         _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase      )

     assert neighbors_pixels is not None

     # Test for local_binary_pattern function()
     # Create a numpy array as the same height and width of read image
     UpperCAmelCase_	   =       np.zeros((image.shape[0], image.shape[1])      )

     # Iterating through the image and calculating the local binary pattern value
     # for each pixel.
     for i in range(0 ,   image.shape[0]      ):
          for j in range(0 ,   image.shape[1]      ):
               UpperCAmelCase_	   =       lbp.local_binary_value(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase      )

     assert lbp_image.any()





'''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

SCREAMING_SNAKE_CASE_:					List[str]  =get_logger()

SCREAMING_SNAKE_CASE_:					Optional[dict]  =None




class 	__A				(			TensorFormatter[Mapping, """jax.Array""", Mapping]     ):



    def __init__(self   :   List[Any]       ,      __a   :   Optional[int]=None       ,      __a   :   Any=None       ,      **__a   :   Dict			):
         super().__init__(features=__a			)
         import jax
         from jaxlib.xla_client import Device

         if isinstance(__a       ,      __a			):
              raise ValueError(
                  f"""Expected {device} to be a `str` not {type(__a			)}, 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(__a       ,      __a			) 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  _lowercase						():
         import jax

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



    def  _lowercase						(self   :   str       ,      __a   :   Tuple			):
         import jax
         import jax.numpy as jnp

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



    def  _lowercase						(self   :   Any       ,      __a   :   Optional[int]			):
         import jax
         import jax.numpy as jnp

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

         UpperCAmelCase_	   =       {}

         if isinstance(__a       ,      (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(__a       ,      (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(__a       ,      PIL.Image.Image			):
                   UpperCAmelCase_	   =       np.asarray(__a			)

        # 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(__a       ,      **{**default_dtype, **self.jnp_array_kwargs}			)



    def  _lowercase						(self   :   int       ,      __a   :   Any			):
         import jax

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

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



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   dict			):
         return map_nested(self._recursive_tensorize       ,      __a       ,      map_list=__a			)



    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_row(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_row(__a			)
         return self.recursive_tensorize(__a			)



    def  _lowercase						(self   :   Tuple       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_column(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_column(__a       ,      pa_table.column_names[0]			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         UpperCAmelCase_	   =       self._consolidate(__a			)
         return column






    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_batch(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_batch(__a			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         for column_name in batch:
              UpperCAmelCase_	   =       self._consolidate(batch[column_name]			)
         return batch




