



from pathlib import Path

import numpy as np
from PIL import Image

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			np.ndarray    ):
				"""simple docstring"""







				__a     ,					__a     ,					__a       =		rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
				return 0.2989 * r + 0.5870 * g + 0.1140 * b

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			np.ndarray    ):
				"""simple docstring"""







				return (gray > 127) & (gray <= 255)

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			np.ndarray				,     _SCREAMING_SNAKE_CASE       :			np.ndarray    ):
				"""simple docstring"""







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

				# Copy image to padded image
				__a       =		image

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


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


lowerCamelCase__			= {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= ["""MBartTokenizer"""]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= ["""MBartTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MBartForCausalLM""",
						    """MBartForConditionalGeneration""",
						    """MBartForQuestionAnswering""",
						    """MBartForSequenceClassification""",
						    """MBartModel""",
						    """MBartPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """TFMBartForConditionalGeneration""",
						    """TFMBartModel""",
						    """TFMBartPreTrainedModel""",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """FlaxMBartForConditionalGeneration""",
						    """FlaxMBartForQuestionAnswering""",
						    """FlaxMBartForSequenceClassification""",
						    """FlaxMBartModel""",
						    """FlaxMBartPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbart import MBartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbart_fast import MBartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mbart import (
												    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MBartForCausalLM,
												    MBartForConditionalGeneration,
												    MBartForQuestionAnswering,
												    MBartForSequenceClassification,
												    MBartModel,
												    MBartPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_mbart import (
												    FlaxMBartForConditionalGeneration,
												    FlaxMBartForQuestionAnswering,
												    FlaxMBartForSequenceClassification,
												    FlaxMBartModel,
												    FlaxMBartPreTrainedModel,
												)

else:
						import sys

						lowerCamelCase__			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
