

'''simple docstring'''







import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
						import PIL.Image

						from .features import FeatureType


__lowerCamelCase		       =    None
__lowerCamelCase		       =    '''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__lowerCamelCase		       =    [
    np.dtype('''|b1'''),
    np.dtype('''|u1'''),
    np.dtype('''<u2'''),
    np.dtype('''>u2'''),
    np.dtype('''<i2'''),
    np.dtype('''>i2'''),
    np.dtype('''<u4'''),
    np.dtype('''>u4'''),
    np.dtype('''<i4'''),
    np.dtype('''>i4'''),
    np.dtype('''<f4'''),
    np.dtype('''>f4'''),
    np.dtype('''<f8'''),
    np.dtype('''>f8'''),
]


@dataclass
class 					A__      :
				lowercase						=		True
				lowercase						=		None
				# Automatically constructed
				lowercase						=		"PIL.Image.Image"
				lowercase						=		pa.struct({"bytes": pa.binary(), "path": pa.string()}			)
				lowercase						=		field(default="Image"						,						init=_A						,						repr=_A			)



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





											'''simple docstring'''
											return self.pa_type



				def 					snake_case_			(					self		,				UpperCamelCase__       )						->      dict:





											'''simple docstring'''
											if config.PIL_AVAILABLE:
																		import PIL.Image
											else:
																		raise ImportError("""To support encoding images, please install 'Pillow'."""       )

											if isinstance(__lowerCamelCase		,				__lowerCamelCase       ):
																		A_						=      np.array(__lowerCamelCase       )

											if isinstance(__lowerCamelCase		,				__lowerCamelCase       ):
																		return {"path": value, "bytes": None}
											elif isinstance(__lowerCamelCase		,				__lowerCamelCase       ):
																		return {"path": None, "bytes": value}
											elif isinstance(__lowerCamelCase		,				np.ndarray       ):
																		# convert the image array to PNG/TIFF bytes
																		return encode_np_array(__lowerCamelCase       )
											elif isinstance(__lowerCamelCase		,				PIL.Image.Image       ):
																		# convert the PIL image to bytes (default format is PNG/TIFF)
																		return encode_pil_image(__lowerCamelCase       )
											elif value.get("""path"""       ) is not None and os.path.isfile(value["""path"""]       ):
																		# we set "bytes": None to not duplicate the data if they're already available locally
																		return {"bytes": None, "path": value.get("""path"""       )}
											elif value.get("""bytes"""       ) is not None or value.get("""path"""       ) is not None:
																		# store the image bytes, and path is used to infer the image format using the file extension
																		return {"bytes": value.get("""bytes"""       ), "path": value.get("""path"""       )}
											else:
																		raise ValueError(
																		    f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''       )



				def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__=None       )						->      "PIL.Image.Image":





											'''simple docstring'''
											if not self.decode:
																		raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead."""       )

											if config.PIL_AVAILABLE:
																		import PIL.Image
											else:
																		raise ImportError("""To support decoding images, please install 'Pillow'."""       )

											if token_per_repo_id is None:
																		A_						=      {}

											A_						=      value["path"], value["bytes"]
											if bytes_ is None:
																		if path is None:
																									raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.'''       )
																		else:
																									if is_local_path(__lowerCamelCase       ):
																																A_						=      PIL.Image.open(__lowerCamelCase       )
																									else:
																																A_						=      path.split("""::"""       )[-1]
																																try:
																																							A_						=      string_to_dict(__lowerCamelCase		,				config.HUB_DATASETS_URL       )["repo_id"]
																																							A_						=      token_per_repo_id.get(__lowerCamelCase       )
																																except ValueError:
																																							A_						=      None
																																with xopen(__lowerCamelCase		,				"""rb"""		,				use_auth_token=__lowerCamelCase       ) as f:
																																							A_						=      BytesIO(f.read()       )
																																A_						=      PIL.Image.open(bytes_       )
											else:
																		A_						=      PIL.Image.open(BytesIO(bytes_       )       )
											image.load()  # to avoid "Too many open files" errors
											return image



				def 					snake_case_			(					self       )						->      Union["FeatureType", Dict[str, "FeatureType"]]:





											'''simple docstring'''
											from .features import Value

											return (
											    self
											    if self.decode
											    else {
											        "bytes": Value("""binary"""       ),
											        "path": Value("""string"""       ),
											    }
											)



				def 					snake_case_			(					self		,				UpperCamelCase__       )						->      pa.StructArray:





											'''simple docstring'''
											if pa.types.is_string(storage.type       ):
																		A_						=      pa.array([None] * len(__lowerCamelCase       )		,				type=pa.binary()       )
																		A_						=      pa.StructArray.from_arrays([bytes_array, storage]		,				["""bytes""", """path"""]		,				mask=storage.is_null()       )
											elif pa.types.is_binary(storage.type       ):
																		A_						=      pa.array([None] * len(__lowerCamelCase       )		,				type=pa.string()       )
																		A_						=      pa.StructArray.from_arrays([storage, path_array]		,				["""bytes""", """path"""]		,				mask=storage.is_null()       )
											elif pa.types.is_struct(storage.type       ):
																		if storage.type.get_field_index("""bytes"""       ) >= 0:
																									A_						=      storage.field("""bytes"""       )
																		else:
																									A_						=      pa.array([None] * len(__lowerCamelCase       )		,				type=pa.binary()       )
																		if storage.type.get_field_index("""path"""       ) >= 0:
																									A_						=      storage.field("""path"""       )
																		else:
																									A_						=      pa.array([None] * len(__lowerCamelCase       )		,				type=pa.string()       )
																		A_						=      pa.StructArray.from_arrays([bytes_array, path_array]		,				["""bytes""", """path"""]		,				mask=storage.is_null()       )
											elif pa.types.is_list(storage.type       ):
																		A_						=      pa.array(
																		    [encode_np_array(np.array(__lowerCamelCase       )       )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()]		,				type=pa.binary()		,				)
																		A_						=      pa.array([None] * len(__lowerCamelCase       )		,				type=pa.string()       )
																		A_						=      pa.StructArray.from_arrays(
																		    [bytes_array, path_array]		,				["""bytes""", """path"""]		,				mask=bytes_array.is_null()       )
											return array_cast(__lowerCamelCase		,				self.pa_type       )







				def 					snake_case_			(					self		,				UpperCamelCase__       )						->      pa.StructArray:





											'''simple docstring'''

											@no_op_if_value_is_null
											def path_to_bytes(UpperCamelCase__       ):
																		with xopen(__lowerCamelCase		,				"""rb"""       ) as f:
																									A_						=      f.read()
																		return bytes_

											A_						=      pa.array(
											    [
											        (path_to_bytes(x["""path"""]       ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
											        for x in storage.to_pylist()
											    ]		,				type=pa.binary()		,				)
											A_						=      pa.array(
											    [os.path.basename(__lowerCamelCase       ) if path is not None else None for path in storage.field("""path"""       ).to_pylist()]		,				type=pa.string()		,				)
											A_						=      pa.StructArray.from_arrays([bytes_array, path_array]		,				["""bytes""", """path"""]		,				mask=bytes_array.is_null()       )
											return array_cast(__lowerCamelCase		,				self.pa_type       )

def       UpperCAmelCase__    (      )			->			Optional[int]:
							if config.PIL_AVAILABLE:
														import PIL.Image
							else:
														raise ImportError("""To support encoding images, please install 'Pillow'."""				)

							global _IMAGE_COMPRESSION_FORMATS
							if _IMAGE_COMPRESSION_FORMATS is None:
														PIL.Image.init()
														A_						=      list(set(PIL.Image.OPEN.keys()				) & set(PIL.Image.SAVE.keys()				)				)
							return _IMAGE_COMPRESSION_FORMATS
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			str:
							A_						=      BytesIO()
							if image.format in list_image_compression_formats():
														A_						=      image.format
							else:
														A_						=      "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
							image.save(_lowerCamelCase,       format=_lowerCamelCase				)
							return buffer.getvalue()
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Any:
							if hasattr(_lowerCamelCase,       """filename"""				) and image.filename != "":
														return {"path": image.filename, "bytes": None}
							else:
														return {"path": None, "bytes": image_to_bytes(_lowerCamelCase				)}
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[int]:
							if config.PIL_AVAILABLE:
														import PIL.Image
							else:
														raise ImportError("""To support encoding images, please install 'Pillow'."""				)

							A_						=      array.dtype
							A_						=      dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
							A_						=      dtype.kind
							A_						=      dtype.itemsize

							A_						=      None

							# Multi-channel array case (only np.dtype("|u1") is allowed)
							if array.shape[2:]:
														A_						=      np.dtype("""|u1"""				)
														if dtype_kind not in ["u", "i"]:
																					raise TypeError(
																					    F'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'''				)
														if dtype is not dest_dtype:
																					warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''				)
    # Exact match
							elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
														A_						=      dtype
							else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
														while dtype_itemsize >= 1:
																					A_						=      dtype_byteorder + dtype_kind + str(_lowerCamelCase				)
																					A_						=      np.dtype(_lowerCamelCase				)
																					if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
																												warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''				)
																												break
																					else:
																												dtype_itemsize //= 2

							if dest_dtype is None:
														raise TypeError(
														    F'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'''				)
							A_						=      PIL.Image.fromarray(array.astype(_lowerCamelCase				)				)
							return {"path": None, "bytes": image_to_bytes(_lowerCamelCase				)}







def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[Any]:
							if config.PIL_AVAILABLE:
														import PIL.Image
							else:
														raise ImportError("""To support encoding images, please install 'Pillow'."""				)

							if objs:
														A_						=      first_non_null_value(_lowerCamelCase				)
														if isinstance(_lowerCamelCase,       _lowerCamelCase				):
																					return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
														if isinstance(_lowerCamelCase,       np.ndarray				):
																					A_						=      no_op_if_value_is_null(_lowerCamelCase				)
																					return [obj_to_image_dict_func(_lowerCamelCase				) for obj in objs]
														elif isinstance(_lowerCamelCase,       PIL.Image.Image				):
																					A_						=      no_op_if_value_is_null(_lowerCamelCase				)
																					return [obj_to_image_dict_func(_lowerCamelCase				) for obj in objs]
														else:
																					return objs
							else:
														return objs



'''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

__lowerCamelCase		       =    imread(r'''digital_image_processing/image_data/lena_small.jpg''')
__lowerCamelCase		       =    cvtColor(img, COLOR_BGR2GRAY)
def       UpperCAmelCase__    (      )			->			Dict:
       A_						=      cn.convert_to_negative(UpperCAmelCase__				)
       # assert negative_img array for at least one True
       assert negative_img.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       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       UpperCAmelCase__    (      )			->			str:
       A_						=      canny.gen_gaussian_kernel(9,       sigma=1.4				)
       # Assert ambiguous array
       assert resp.all()
def       UpperCAmelCase__    (      )			->			Union[str, Any]:
       A_						=      imread("""digital_image_processing/image_data/lena_small.jpg""",       0				)
       # assert ambiguous array for all == True
       assert canny_img.all()
       A_						=      canny.canny(UpperCAmelCase__				)
       # assert canny array for at least one True
       assert canny_array.any()
def       UpperCAmelCase__    (      )			->			Dict:
       assert gg.gaussian_filter(UpperCAmelCase__,       5,       sigma=0.9				).all()
def       UpperCAmelCase__    (      )			->			int:
       # laplace diagonals
       A_						=      array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
       A_						=      conv.img_convolve(UpperCAmelCase__,       UpperCAmelCase__				).astype(UpperCAmelCase__				)
       assert res.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       assert med.median_filter(UpperCAmelCase__,       3				).any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       A_					,			A_						=      sob.sobel_filter(UpperCAmelCase__				)
       assert grad.any() and theta.any()
def       UpperCAmelCase__    (      )			->			List[str]:
       A_						=      sp.make_sepia(UpperCAmelCase__,       20				)
       assert sepia.all()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg"				)			->			List[Any]:
       A_						=      bs.Burkes(imread(UpperCAmelCase__,       1				),       1_20				)
       burkes.process()
       assert burkes.output_img.any()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg",       )			->			Optional[int]:
       A_						=      rs.NearestNeighbour(imread(UpperCAmelCase__,       1				),       4_00,       2_00				)
       nn.process()
       assert nn.output.any()







def       UpperCAmelCase__    (      )			->			Optional[int]:
       A_						=      """digital_image_processing/image_data/lena.jpg"""

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

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

       A_						=      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
       A_						=      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]				):
                     A_						=      lbp.local_binary_value(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       assert lbp_image.any()
