



"""simple docstring"""
from pathlib import Path

import numpy as np
from PIL import Image





def  lowerCamelCase_						(UpperCamelCase__		:		np.ndarray							):
    _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  Dict											=    rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.2989 * r + 0.5870 * g + 0.1140 * b





def  lowerCamelCase_						(UpperCamelCase__		:		np.ndarray							):
    return (gray > 127) & (gray <= 255)





def  lowerCamelCase_						(UpperCamelCase__		:		np.ndarray     ,       UpperCamelCase__		:		np.ndarray							):
    _UpperCAmelCase							:  Tuple											=    np.zeros_like(UpperCamelCase__							)
    _UpperCAmelCase							:  List[str]											=    np.zeros(
        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)							)

    # Copy image to padded image
    _UpperCAmelCase							:  str											=    image

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


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







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase    :List[Any]     =					{'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'OPTForCausalLM',
      'OPTModel',
      'OPTPreTrainedModel',
      'OPTForSequenceClassification',
      'OPTForQuestionAnswering',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'FlaxOPTForCausalLM',
      'FlaxOPTModel',
      'FlaxOPTPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_opt import (
        OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OPTForCausalLM,
        OPTForQuestionAnswering,
        OPTForSequenceClassification,
        OPTModel,
        OPTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
  import sys

  _lowerCAmelCase    :int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


