


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase__   	=				{
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				[
        'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'FlaubertForMultipleChoice',
        'FlaubertForQuestionAnswering',
        'FlaubertForQuestionAnsweringSimple',
        'FlaubertForSequenceClassification',
        'FlaubertForTokenClassification',
        'FlaubertModel',
        'FlaubertWithLMHeadModel',
        'FlaubertPreTrainedModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				[
        'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFFlaubertForMultipleChoice',
        'TFFlaubertForQuestionAnsweringSimple',
        'TFFlaubertForSequenceClassification',
        'TFFlaubertForTokenClassification',
        'TFFlaubertModel',
        'TFFlaubertPreTrainedModel',
        'TFFlaubertWithLMHeadModel',
    ]


if TYPE_CHECKING:
    from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
    from .tokenization_flaubert import FlaubertTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flaubert import (
            FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            FlaubertForMultipleChoice,
            FlaubertForQuestionAnswering,
            FlaubertForQuestionAnsweringSimple,
            FlaubertForSequenceClassification,
            FlaubertForTokenClassification,
            FlaubertModel,
            FlaubertPreTrainedModel,
            FlaubertWithLMHeadModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_flaubert import (
            TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFFlaubertForMultipleChoice,
            TFFlaubertForQuestionAnsweringSimple,
            TFFlaubertForSequenceClassification,
            TFFlaubertForTokenClassification,
            TFFlaubertModel,
            TFFlaubertPreTrainedModel,
            TFFlaubertWithLMHeadModel,
        )

else:
    import sys

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



import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

lowerCAmelCase__   	=				1_80
def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]		):
       _A   : int       					=				k_size // 2
       _A					,    _A   : Tuple       					=				np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       _A   : Union[str, Any]       					=				(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(UpperCamelCase__		) + np.square(UpperCamelCase__		)) / (2 * np.square(UpperCamelCase__		))		)
       )
       return g
def 							_UpperCAmelCase       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Tuple		):
       _A   : Optional[int]       					=				np.zeros(UpperCamelCase__		)

       for row in range(1       ,							image_shape[0] - 1		):
              for col in range(1       ,							image_shape[1] - 1		):
                     _A   : Dict       					=				gradient_direction[row, col]

                     if (
                         0 <= direction < PI / 8
                         or 15 * PI / 8 <= direction <= 2 * PI
                         or 7 * PI / 8 <= direction <= 9 * PI / 8
                     ):
                            _A   : Dict       					=				sobel_grad[row, col - 1]
                            _A   : Any       					=				sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   _A   : List[Any]       					=				sobel_grad[row, col]

                     elif (
                         PI / 8 <= direction < 3 * PI / 8
                         or 9 * PI / 8 <= direction < 11 * PI / 8
                     ):
                            _A   : List[str]       					=				sobel_grad[row + 1, col - 1]
                            _A   : str       					=				sobel_grad[row - 1, col + 1]
                            if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                   _A   : List[Any]       					=				sobel_grad[row, col]

                     elif (
                         3 * PI / 8 <= direction < 5 * PI / 8
                         or 11 * PI / 8 <= direction < 13 * PI / 8
                     ):
                            _A   : Any       					=				sobel_grad[row - 1, col]
                            _A   : int       					=				sobel_grad[row + 1, col]
                            if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                   _A   : Any       					=				sobel_grad[row, col]

                     elif (
                         5 * PI / 8 <= direction < 7 * PI / 8
                         or 13 * PI / 8 <= direction < 15 * PI / 8
                     ):
                            _A   : List[str]       					=				sobel_grad[row - 1, col - 1]
                            _A   : Optional[Any]       					=				sobel_grad[row + 1, col + 1]
                            if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                   _A   : List[Any]       					=				sobel_grad[row, col]

       return destination
def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Any		):
       for row in range(1       ,							image_shape[0] - 1		):
              for col in range(1       ,							image_shape[1] - 1		):
                     if destination[row, col] >= threshold_high:
                            _A   : Optional[int]       					=				strong
                     elif destination[row, col] <= threshold_low:
                            _A   : Optional[int]       					=				0
                     else:
                            _A   : Union[str, Any]       					=				weak
def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Any		):
       for row in range(1       ,							image_shape[0]		):
              for col in range(1       ,							image_shape[1]		):
                     if destination[row, col] == weak:
                            if 255 in (
                                destination[row, col + 1],
                                destination[row, col - 1],
                                destination[row - 1, col],
                                destination[row + 1, col],
                                destination[row - 1, col - 1],
                                destination[row + 1, col - 1],
                                destination[row - 1, col + 1],
                                destination[row + 1, col + 1],
                            ):
                                   _A   : str       					=				strong
                            else:
                                   _A   : Any       					=				0
def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						int=15       ,							UpperCamelCase__  :						Dict=30       ,							UpperCamelCase__  :						int=128       ,							UpperCamelCase__  :						Union[str, Any]=255		):
       # gaussian_filter
       _A   : Union[str, Any]       					=				img_convolve(UpperCamelCase__       ,							gen_gaussian_kernel(9       ,							sigma=1.4		)		)
       # get the gradient and degree by sobel_filter
       _A					,    _A   : Dict       					=				sobel_filter(UpperCamelCase__		)
       _A   : Tuple       					=				PI + np.radadeg(UpperCamelCase__		)

       _A   : Optional[int]       					=				suppress_non_maximum(image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__		)

       detect_high_low_threshold(
           image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)

       track_edge(image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)

       return destination


if __name__ == "__main__":
    # read original image in gray mode
    lowerCAmelCase__   	=				cva.imread(R'../image_data/lena.jpg', 0)
    # canny edge detection
    lowerCAmelCase__   	=				canny(lena)
    cva.imshow('canny', canny_destination)
    cva.waitKey(0)
