

'''simple docstring'''
from pathlib import Path

import numpy as np
from PIL import Image


def 	_lowerCAmelCase (						_UpperCamelCase						:					np.ndarray    )				->      np.ndarray:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
     return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b


def 	_lowerCAmelCase (						_UpperCamelCase						:					np.ndarray    )				->      np.ndarray:
     """simple docstring"""




     return (gray > 1_27) & (gray <= 2_55)


def 	_lowerCAmelCase (						_UpperCamelCase						:					np.ndarray							,			_UpperCamelCase						:					np.ndarray    )				->      np.ndarray:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =np.zeros_like(_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =np.zeros(
         (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)    )

     # Copy image to padded image
     _SCREAMING_SNAKE_CASE							   =image

     # Iterate over image & apply kernel
     for x in range(image.shape[1]    ):
          for y in range(image.shape[0]    ):
               _SCREAMING_SNAKE_CASE							   =(
                   kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
               ).sum()
               _SCREAMING_SNAKE_CASE							   =int(summation > 0    )
     return output


if __name__ == "__main__":
   # read original image
   lowerCamelCase			:				Optional[Any] 			=					Path(__file__).resolve().parent / "image_data" / "lena.jpg"
   lowerCamelCase			:				List[str] 			=					np.array(Image.open(lena_path))
   # kernel to be applied
   lowerCamelCase			:				int 			=					np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
   lowerCamelCase			:				Dict 			=					dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
   # Save the output image
   lowerCamelCase			:				str 			=					Image.fromarray(output).convert("RGB")
   pil_img.save("result_dilation.png")



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase			:				Tuple 			=					{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				List[Any] 			=					["FNetTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Optional[int] 			=					["FNetTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					[
         "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FNetForMaskedLM",
         "FNetForMultipleChoice",
         "FNetForNextSentencePrediction",
         "FNetForPreTraining",
         "FNetForQuestionAnswering",
         "FNetForSequenceClassification",
         "FNetForTokenClassification",
         "FNetLayer",
         "FNetModel",
         "FNetPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase			:				Dict 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
