


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


__UpperCAmelCase : Optional[List[str]] 			=				None
__UpperCAmelCase : List[Any] 			=				"<" 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
__UpperCAmelCase : Optional[int] 			=				[
    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    UpperCAmelCase_	:
       '''simple docstring'''
       __UpperCamelCase      :     bool							=					True
       __UpperCamelCase      :     Optional[str]							=					None
       # Automatically constructed
       __UpperCamelCase      :     ClassVar[str]							=					"PIL.Image.Image"
       __UpperCamelCase      :     ClassVar[Any]							=					pa.struct({"bytes": pa.binary(), "path": pa.string()})
       __UpperCamelCase      :     str							=					field(default="Image",     init=_a,     repr=_a)






       def __call__(     self						):




              """simple docstring"""




              return self.pa_type






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




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

              if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	List[str]   	=		np.array(__SCREAMING_SNAKE_CASE						)

              if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     return {"path": value, "bytes": None}
              elif isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     return {"path": None, "bytes": value}
              elif isinstance(__SCREAMING_SNAKE_CASE ,					np.ndarray						):
                     # convert the image array to PNG/TIFF bytes
                     return encode_np_array(__SCREAMING_SNAKE_CASE						)
              elif isinstance(__SCREAMING_SNAKE_CASE ,					PIL.Image.Image						):
                     # convert the PIL image to bytes (default format is PNG/TIFF)
                     return encode_pil_image(__SCREAMING_SNAKE_CASE						)
              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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=None						):




              """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:
                     UpperCamelCase    :	Union[str, Any]   	=		{}

              UpperCamelCase	, UpperCamelCase    :	Union[str, Any]   	=		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(__SCREAMING_SNAKE_CASE						):
                                   UpperCamelCase    :	str   	=		PIL.Image.open(__SCREAMING_SNAKE_CASE						)
                            else:
                                   UpperCamelCase    :	Dict   	=		path.split('''::'''						)[-1]
                                   try:
                                          UpperCamelCase    :	List[Any]   	=		string_to_dict(__SCREAMING_SNAKE_CASE ,					config.HUB_DATASETS_URL						)['''repo_id''']
                                          UpperCamelCase    :	List[str]   	=		token_per_repo_id.get(__SCREAMING_SNAKE_CASE						)
                                   except ValueError:
                                          UpperCamelCase    :	List[str]   	=		None
                                   with xopen(__SCREAMING_SNAKE_CASE ,					'''rb''' ,					use_auth_token=__SCREAMING_SNAKE_CASE						) as f:
                                          UpperCamelCase    :	Optional[Any]   	=		BytesIO(f.read()						)
                                   UpperCamelCase    :	Tuple   	=		PIL.Image.open(bytes_						)
              else:
                     UpperCamelCase    :	List[Any]   	=		PIL.Image.open(BytesIO(bytes_						)						)
              image.load()  # to avoid "Too many open files" errors
              return image






       def  _lowercase   (     self						):




              """simple docstring"""




              from .features import Value

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






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if pa.types.is_string(storage.type						):
                     UpperCamelCase    :	List[str]   	=		pa.array([None] * len(__SCREAMING_SNAKE_CASE						) ,					type=pa.binary()						)
                     UpperCamelCase    :	Dict   	=		pa.StructArray.from_arrays([bytes_array, storage] ,					['''bytes''', '''path'''] ,					mask=storage.is_null()						)
              elif pa.types.is_binary(storage.type						):
                     UpperCamelCase    :	List[str]   	=		pa.array([None] * len(__SCREAMING_SNAKE_CASE						) ,					type=pa.string()						)
                     UpperCamelCase    :	List[str]   	=		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:
                            UpperCamelCase    :	Optional[int]   	=		storage.field('''bytes'''						)
                     else:
                            UpperCamelCase    :	Dict   	=		pa.array([None] * len(__SCREAMING_SNAKE_CASE						) ,					type=pa.binary()						)
                     if storage.type.get_field_index('''path'''						) >= 0:
                            UpperCamelCase    :	str   	=		storage.field('''path'''						)
                     else:
                            UpperCamelCase    :	List[str]   	=		pa.array([None] * len(__SCREAMING_SNAKE_CASE						) ,					type=pa.string()						)
                     UpperCamelCase    :	Union[str, Any]   	=		pa.StructArray.from_arrays([bytes_array, path_array] ,					['''bytes''', '''path'''] ,					mask=storage.is_null()						)
              elif pa.types.is_list(storage.type						):
                     UpperCamelCase    :	str   	=		pa.array(
                         [encode_np_array(np.array(__SCREAMING_SNAKE_CASE						)						)['''bytes'''] if arr is not None else None for arr in storage.to_pylist()] ,					type=pa.binary() ,					)
                     UpperCamelCase    :	Dict   	=		pa.array([None] * len(__SCREAMING_SNAKE_CASE						) ,					type=pa.string()						)
                     UpperCamelCase    :	Any   	=		pa.StructArray.from_arrays(
                         [bytes_array, path_array] ,					['''bytes''', '''path'''] ,					mask=bytes_array.is_null()						)
              return array_cast(__SCREAMING_SNAKE_CASE ,					self.pa_type						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              @no_op_if_value_is_null
              def path_to_bytes(__SCREAMING_SNAKE_CASE						):
                     with xopen(__SCREAMING_SNAKE_CASE ,					'''rb'''						) as f:
                            UpperCamelCase    :	Optional[Any]   	=		f.read()
                     return bytes_

              UpperCamelCase    :	List[Any]   	=		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() ,					)
              UpperCamelCase    :	List[Any]   	=		pa.array(
                  [os.path.basename(__SCREAMING_SNAKE_CASE						) if path is not None else None for path in storage.field('''path'''						).to_pylist()] ,					type=pa.string() ,					)
              UpperCamelCase    :	List[str]   	=		pa.StructArray.from_arrays([bytes_array, path_array] ,					['''bytes''', '''path'''] ,					mask=bytes_array.is_null()						)
              return array_cast(__SCREAMING_SNAKE_CASE ,					self.pa_type						)




def      a	(							):






       """simple docstring"""





       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()
              UpperCamelCase    :	Union[str, Any]   	=		list(set(PIL.Image.OPEN.keys()       ) & set(PIL.Image.SAVE.keys()       )       )
       return _IMAGE_COMPRESSION_FORMATS


def      a	(							SCREAMING_SNAKE_CASE_      :		"PIL.Image.Image"       ):






       """simple docstring"""





       UpperCamelCase    :	Union[str, Any]   	=		BytesIO()
       if image.format in list_image_compression_formats():
              UpperCamelCase    :	List[Any]   	=		image.format
       else:
              UpperCamelCase    :	str   	=		'''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
       image.save(SCREAMING_SNAKE_CASE_							,			format=SCREAMING_SNAKE_CASE_       )
       return buffer.getvalue()


def      a	(							SCREAMING_SNAKE_CASE_      :		"PIL.Image.Image"       ):






       """simple docstring"""





       if hasattr(SCREAMING_SNAKE_CASE_							,			'''filename'''       ) and image.filename != "":
              return {"path": image.filename, "bytes": None}
       else:
              return {"path": None, "bytes": image_to_bytes(SCREAMING_SNAKE_CASE_       )}


def      a	(							SCREAMING_SNAKE_CASE_      :		np.ndarray       ):






       """simple docstring"""





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

       UpperCamelCase    :	Dict   	=		array.dtype
       UpperCamelCase    :	List[Any]   	=		dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
       UpperCamelCase    :	List[Any]   	=		dtype.kind
       UpperCamelCase    :	int   	=		dtype.itemsize

       UpperCamelCase    :	Any   	=		None

       # Multi-channel array case (only np.dtype("|u1") is allowed)
       if array.shape[2:]:
              UpperCamelCase    :	Optional[int]   	=		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:
              UpperCamelCase    :	str   	=		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:
                     UpperCamelCase    :	Optional[int]   	=		dtype_byteorder + dtype_kind + str(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	Union[str, Any]   	=		np.dtype(SCREAMING_SNAKE_CASE_       )
                     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}"""       )
       UpperCamelCase    :	Tuple   	=		PIL.Image.fromarray(array.astype(SCREAMING_SNAKE_CASE_       )       )
       return {"path": None, "bytes": image_to_bytes(SCREAMING_SNAKE_CASE_       )}


def      a	(							SCREAMING_SNAKE_CASE_      :		Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]       ):






       """simple docstring"""





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

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





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def      a	(							SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		k_size // 2
       UpperCamelCase	, UpperCamelCase    :	Optional[int]   	=		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       UpperCamelCase    :	Dict   	=		1 / (2 * pi * sigma) * exp(-(square(SCREAMING_SNAKE_CASE_       ) + square(SCREAMING_SNAKE_CASE_       )) / (2 * square(SCREAMING_SNAKE_CASE_       ))       )
       return g


def      a	(							SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase	, UpperCamelCase    :	Tuple   	=		image.shape[0], image.shape[1]
       # dst image height and width
       UpperCamelCase    :	str   	=		height - k_size + 1
       UpperCamelCase    :	Optional[int]   	=		width - k_size + 1

       # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
       UpperCamelCase    :	List[Any]   	=		zeros((dst_height * dst_width, k_size * k_size)       )
       UpperCamelCase    :	Tuple   	=		0
       for i, j in product(range(SCREAMING_SNAKE_CASE_       )							,			range(SCREAMING_SNAKE_CASE_       )       ):
              UpperCamelCase    :	Dict   	=		ravel(image[i : i + k_size, j : j + k_size]       )
              UpperCamelCase    :	Dict   	=		window
              row += 1

       #  turn the kernel into shape(k*k, 1)
       UpperCamelCase    :	Optional[int]   	=		gen_gaussian_kernel(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	List[Any]   	=		ravel(SCREAMING_SNAKE_CASE_       )

       # reshape and get the dst image
       UpperCamelCase    :	Optional[int]   	=		dot(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ).reshape(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ).astype(SCREAMING_SNAKE_CASE_       )

       return dst


if __name__ == "__main__":
  # read original image
  __UpperCAmelCase : Union[str, Any] 			=				imread(r"../image_data/lena.jpg")
  # turn image in gray scale value
  __UpperCAmelCase : Optional[Any] 			=				cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  __UpperCAmelCase : Optional[int] 			=				gaussian_filter(gray, 3, sigma=1)
  __UpperCAmelCase : List[Any] 			=				gaussian_filter(gray, 5, sigma=0.8)

  # show result images
  imshow("gaussian filter with 3x3 mask", gaussianaxa)
  imshow("gaussian filter with 5x5 mask", gaussianaxa)
  waitKey()

