




'''simple docstring'''





import numpy as np
from PIL import Image





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							int							       =      rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.2989 * r + 0.5870 * g + 0.1140 * b





def UpperCAmelCase    (       UpperCAmelCase_						):
    return (gray > 127) & (gray <= 255)





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_						):
    _a				:							List[Any]							       =      np.zeros_like(snake_case_						)
    _a				:							Dict							       =      np.zeros(
        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)						)

    # Copy image to padded image
    _a				:							Union[str, Any]							       =      image

    # Iterate over image & apply kernel
    for x in range(image.shape[1]						):
        for y in range(image.shape[0]						):
            _a				:							Optional[int]							       =      (
                kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
            ).sum()
            _a				:							List[str]							       =      int(summation == 5						)
    return output


# kernel to be applied
a		:	str       =      np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
  # read original image
  a		:	List[str]       =      np.array(Image.open(R'''..\image_data\lena.jpg'''))
  # Apply erosion operation to a binary image
  a		:	Optional[Any]       =      erosion(grayabinary(rgbagray(image)), structuring_element)
  # Save the output image
  a		:	Optional[int]       =      Image.fromarray(output).convert('''RGB''')
  pil_img.save('''result_erosion.png''')




from typing import TYPE_CHECKING

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


snake_case_      :					Dict				      =     {
    'configuration_big_bird': ['BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BigBirdConfig', 'BigBirdOnnxConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					str				      =     ['BigBirdTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Any				      =     ['BigBirdTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BigBirdForCausalLM',
	    'BigBirdForMaskedLM',
	    'BigBirdForMultipleChoice',
	    'BigBirdForPreTraining',
	    'BigBirdForQuestionAnswering',
	    'BigBirdForSequenceClassification',
	    'BigBirdForTokenClassification',
	    'BigBirdLayer',
	    'BigBirdModel',
	    'BigBirdPreTrainedModel',
	    'load_tf_weights_in_big_bird',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Any				      =     [
	    'FlaxBigBirdForCausalLM',
	    'FlaxBigBirdForMaskedLM',
	    'FlaxBigBirdForMultipleChoice',
	    'FlaxBigBirdForPreTraining',
	    'FlaxBigBirdForQuestionAnswering',
	    'FlaxBigBirdForSequenceClassification',
	    'FlaxBigBirdForTokenClassification',
	    'FlaxBigBirdModel',
	    'FlaxBigBirdPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_big_bird import BigBirdTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_big_bird_fast import BigBirdTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_big_bird import (
		    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BigBirdForCausalLM,
		    BigBirdForMaskedLM,
		    BigBirdForMultipleChoice,
		    BigBirdForPreTraining,
		    BigBirdForQuestionAnswering,
		    BigBirdForSequenceClassification,
		    BigBirdForTokenClassification,
		    BigBirdLayer,
		    BigBirdModel,
		    BigBirdPreTrainedModel,
		    load_tf_weights_in_big_bird,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_big_bird import (
		    FlaxBigBirdForCausalLM,
		    FlaxBigBirdForMaskedLM,
		    FlaxBigBirdForMultipleChoice,
		    FlaxBigBirdForPreTraining,
		    FlaxBigBirdForQuestionAnswering,
		    FlaxBigBirdForSequenceClassification,
		    FlaxBigBirdForTokenClassification,
		    FlaxBigBirdModel,
		    FlaxBigBirdPreTrainedModel,
		)

else:
	import sys

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