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,
)


__lowercase							 =					{'''configuration_mbart''': ['''MBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MBartConfig''', '''MBartOnnxConfig''']}

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

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

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

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

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase							 =					[
        '''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

    __lowercase							 =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







"""simple docstring"""





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		np.ndarray:


					"""simple docstring"""





					if (ksize % 2) == 0:
										_UpperCamelCase							 =  ksize + 1
					_UpperCamelCase							 =  np.zeros((ksize, ksize),     dtype=np.floataa	)

					# each value
					for y in range(__snake_case	):
										for x in range(__snake_case	):
															# distance from center
															_UpperCamelCase							 =  x - ksize // 2
															_UpperCamelCase							 =  y - ksize // 2

															# degree to radiant
															_UpperCamelCase							 =  theta / 1_80 * np.pi
															_UpperCamelCase							 =  np.cos(_theta	)
															_UpperCamelCase							 =  np.sin(_theta	)

															# get kernel x
															_UpperCamelCase							 =  cos_theta * px + sin_theta * py

															# get kernel y
															_UpperCamelCase							 =  -sin_theta * px + cos_theta * py

															# fill kernel
															_UpperCamelCase							 =  np.exp(
															    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)	) * np.cos(2 * np.pi * _x / lambd + psi	)

					return gabor


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# read original image
				_a														=		imread("""../image_data/lena.jpg""")
				# turn image in gray scale value
				_a														=		cvtColor(img, COLOR_BGR2GRAY)

				# Apply multiple Kernel to detect edges
				_a														=		np.zeros(gray.shape[:2])
				for theta in [0, 30, 60, 90, 120, 150]:
								_a														=		gabor_filter_kernel(10, 8, theta, 10, 0, 0)
								out += filteraD(gray, CV_8UC3, kernel_aa)
				_a														=		out / out.max() * 255
				_a														=		out.astype(np.uinta)

				imshow("""Original""", gray)
				imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

				waitKey(0)






