



'''simple docstring'''



from pathlib import Path

import numpy as np
from PIL import Image



def   __lowerCAmelCase					(							lowerCamelCase   :						np.ndarray					):






	'''simple docstring'''




	__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
	return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b



def   __lowerCAmelCase					(							lowerCamelCase   :						np.ndarray					):






	'''simple docstring'''




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



def   __lowerCAmelCase					(							lowerCamelCase   :						np.ndarray				,		lowerCamelCase   :						np.ndarray					):






	'''simple docstring'''




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

	# Copy image to padded image
	__lowerCAmelCase												=  image

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


if __name__ == "__main__":
	# read original image
	lowerCAmelCase	:		Union[str, Any]				=   Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
	lowerCAmelCase	:		Any				=   np.array(Image.open(lena_path))
	# kernel to be applied
	lowerCAmelCase	:		Union[str, Any]				=   np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
	lowerCAmelCase	:		Union[str, Any]				=   dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
	# Save the output image
	lowerCAmelCase	:		Any				=   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	:		Union[str, Any]				=   {
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		Tuple				=   ['''DistilBertTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		Tuple				=   [
	    '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DistilBertForMaskedLM''',
	    '''DistilBertForMultipleChoice''',
	    '''DistilBertForQuestionAnswering''',
	    '''DistilBertForSequenceClassification''',
	    '''DistilBertForTokenClassification''',
	    '''DistilBertModel''',
	    '''DistilBertPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		List[str]				=   [
	    '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFDistilBertForMaskedLM''',
	    '''TFDistilBertForMultipleChoice''',
	    '''TFDistilBertForQuestionAnswering''',
	    '''TFDistilBertForSequenceClassification''',
	    '''TFDistilBertForTokenClassification''',
	    '''TFDistilBertMainLayer''',
	    '''TFDistilBertModel''',
	    '''TFDistilBertPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		Optional[int]				=   [
	    '''FlaxDistilBertForMaskedLM''',
	    '''FlaxDistilBertForMultipleChoice''',
	    '''FlaxDistilBertForQuestionAnswering''',
	    '''FlaxDistilBertForSequenceClassification''',
	    '''FlaxDistilBertForTokenClassification''',
	    '''FlaxDistilBertModel''',
	    '''FlaxDistilBertPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_distilbert import (
	    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DistilBertConfig,
	    DistilBertOnnxConfig,
	)
	from .tokenization_distilbert import DistilBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_distilbert_fast import DistilBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_distilbert import (
		    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DistilBertForMaskedLM,
		    DistilBertForMultipleChoice,
		    DistilBertForQuestionAnswering,
		    DistilBertForSequenceClassification,
		    DistilBertForTokenClassification,
		    DistilBertModel,
		    DistilBertPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_distilbert import (
		    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDistilBertForMaskedLM,
		    TFDistilBertForMultipleChoice,
		    TFDistilBertForQuestionAnswering,
		    TFDistilBertForSequenceClassification,
		    TFDistilBertForTokenClassification,
		    TFDistilBertMainLayer,
		    TFDistilBertModel,
		    TFDistilBertPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_distilbert import (
		    FlaxDistilBertForMaskedLM,
		    FlaxDistilBertForMultipleChoice,
		    FlaxDistilBertForQuestionAnswering,
		    FlaxDistilBertForSequenceClassification,
		    FlaxDistilBertForTokenClassification,
		    FlaxDistilBertModel,
		    FlaxDistilBertPreTrainedModel,
		)

else:
	import sys

	lowerCAmelCase	:		List[str]				=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)