

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__magic_name__            =  {
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BertForMaskedLM',
         'BertForMultipleChoice',
         'BertForNextSentencePrediction',
         'BertForPreTraining',
         'BertForQuestionAnswering',
         'BertForSequenceClassification',
         'BertForTokenClassification',
         'BertLayer',
         'BertLMHeadModel',
         'BertModel',
         'BertPreTrainedModel',
         'load_tf_weights_in_bert',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFBertEmbeddings',
         'TFBertForMaskedLM',
         'TFBertForMultipleChoice',
         'TFBertForNextSentencePrediction',
         'TFBertForPreTraining',
         'TFBertForQuestionAnswering',
         'TFBertForSequenceClassification',
         'TFBertForTokenClassification',
         'TFBertLMHeadModel',
         'TFBertMainLayer',
         'TFBertModel',
         'TFBertPreTrainedModel',
     ]
try:
     if not is_tensorflow_text_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  ['TFBertTokenizer']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         'FlaxBertForCausalLM',
         'FlaxBertForMaskedLM',
         'FlaxBertForMultipleChoice',
         'FlaxBertForNextSentencePrediction',
         'FlaxBertForPreTraining',
         'FlaxBertForQuestionAnswering',
         'FlaxBertForSequenceClassification',
         'FlaxBertForTokenClassification',
         'FlaxBertModel',
         'FlaxBertPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
     from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_bert_fast import BertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_bert import (
              BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BertForMaskedLM,
              BertForMultipleChoice,
              BertForNextSentencePrediction,
              BertForPreTraining,
              BertForQuestionAnswering,
              BertForSequenceClassification,
              BertForTokenClassification,
              BertLayer,
              BertLMHeadModel,
              BertModel,
              BertPreTrainedModel,
              load_tf_weights_in_bert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_bert import (
              TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFBertEmbeddings,
              TFBertForMaskedLM,
              TFBertForMultipleChoice,
              TFBertForNextSentencePrediction,
              TFBertForPreTraining,
              TFBertForQuestionAnswering,
              TFBertForSequenceClassification,
              TFBertForTokenClassification,
              TFBertLMHeadModel,
              TFBertMainLayer,
              TFBertModel,
              TFBertPreTrainedModel,
          )

     try:
          if not is_tensorflow_text_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_bert_tf import TFBertTokenizer

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_bert import (
              FlaxBertForCausalLM,
              FlaxBertForMaskedLM,
              FlaxBertForMultipleChoice,
              FlaxBertForNextSentencePrediction,
              FlaxBertForPreTraining,
              FlaxBertForQuestionAnswering,
              FlaxBertForSequenceClassification,
              FlaxBertForTokenClassification,
              FlaxBertModel,
              FlaxBertPreTrainedModel,
          )

else:
     import sys

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





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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):

    # prepare kernel
    # the kernel size have to be odd
    if (ksize % 2) == 0:
        snake_case__						=				ksize + 1
    snake_case__						=				np.zeros((ksize, ksize)    ,		dtype=np.floataa							)

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

            # degree to radiant
            snake_case__						=				theta / 180 * np.pi
            snake_case__						=				np.cos(_theta							)
            snake_case__						=				np.sin(_theta							)

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

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

            # fill kernel
            snake_case__						=				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
     __magic_name__            =  imread('''../image_data/lena.jpg''')
     # turn image in gray scale value
     __magic_name__            =  cvtColor(img, COLOR_BGR2GRAY)

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

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

     waitKey(0)



