
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros

def        lowerCamelCase_      (			UpperCAmelCase_							:				str							,			UpperCAmelCase_							:				Any       ):
						lowercase				,				lowercase       :    str											=  image.shape
						lowercase       :    Optional[Any]											=  cols - block_size[1] + 1
						lowercase       :    List[str]											=  rows - block_size[0] + 1
						lowercase       :    Union[str, Any]											=  zeros((dst_height * dst_width, block_size[1] * block_size[0])       )
						lowercase       :    Union[str, Any]											=  0
						for i in range(0							,			UpperCAmelCase_       ):
												for j in range(0							,			UpperCAmelCase_       ):
																		lowercase       :    Dict											=  ravel(image[i : i + block_size[0], j : j + block_size[1]]       )
																		lowercase       :    Dict											=  window
																		row += 1

						return image_array

def        lowerCamelCase_      (			UpperCAmelCase_							:				Tuple							,			UpperCAmelCase_							:				List[str]       ):
						lowercase				,				lowercase       :    List[str]											=  image.shape[0], image.shape[1]
						lowercase       :    Tuple											=  filter_kernel.shape[0]
						lowercase       :    Optional[int]											=  k_size // 2
						# Pads image with the edge values of array.
						lowercase       :    int											=  pad(UpperCAmelCase_							,			UpperCAmelCase_							,			mode='''edge'''       )

						# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
						lowercase       :    int											=  imacol(UpperCAmelCase_							,			(k_size, k_size)       )

						#  turn the kernel into shape(k*k, 1)
						lowercase       :    Any											=  ravel(UpperCAmelCase_       )
						# reshape and get the dst image
						lowercase       :    Any											=  dot(UpperCAmelCase_							,			UpperCAmelCase_       ).reshape(UpperCAmelCase_							,			UpperCAmelCase_       )
						return dst


if __name__ == "__main__":
				# read original image
				snake_case__  							=							imread(R"""../image_data/lena.jpg""")
				# turn image in gray scale value
				snake_case__  							=							cvtColor(img, COLOR_BGR2GRAY)
				# Laplace operator
				snake_case__  							=							array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
				snake_case__  							=							img_convolve(gray, Laplace_kernel).astype(uinta)
				imshow("""Laplacian""", out)
				waitKey(0)




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__  							=							{"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

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


