"""simple docstring"""





import cva
import numpy as np
class  _snake_case     :






      def __init__(       self      :			List[Any]							,					UpperCAmelCase      :			float							,					UpperCAmelCase      :			int    ):

             if k in (0.0_4, 0.0_6):
                    __lowerCamelCase					:		Any		   =     k
                    __lowerCamelCase					:		List[str]		   =     window_size
             else:
                    raise ValueError("invalid k value"    )






      def __str__(       self      :			List[str]    ):
             return str(self.k    )







      def       lowerCamelCase__     (       self      :			Optional[int]							,					UpperCAmelCase      :			str    ):
             __lowerCamelCase					:		Union[str, Any]		   =     cva.imread(UpperCAmelCase							,					0    )
             __lowerCamelCase   ,  __lowerCamelCase					:		Tuple		   =     img.shape
             __lowerCamelCase					:		int		   =     []
             __lowerCamelCase					:		Dict		   =     img.copy()
             __lowerCamelCase					:		str		   =     cva.cvtColor(UpperCAmelCase							,					cva.COLOR_GRAY2RGB    )
             __lowerCamelCase   ,  __lowerCamelCase					:		List[Any]		   =     np.gradient(UpperCAmelCase    )
             __lowerCamelCase					:		Tuple		   =     dx**2
             __lowerCamelCase					:		str		   =     dy**2
             __lowerCamelCase					:		Tuple		   =     dx * dy
             __lowerCamelCase					:		int		   =     0.0_4
             __lowerCamelCase					:		Any		   =     self.window_size // 2
             for y in range(UpperCAmelCase							,					h - offset    ):
                    for x in range(UpperCAmelCase							,					w - offset    ):
                           __lowerCamelCase					:		int		   =     ixx[
                               y - offset : y + offset + 1, x - offset : x + offset + 1
                           ].sum()
                           __lowerCamelCase					:		Dict		   =     iyy[
                               y - offset : y + offset + 1, x - offset : x + offset + 1
                           ].sum()
                           __lowerCamelCase					:		str		   =     ixy[
                               y - offset : y + offset + 1, x - offset : x + offset + 1
                           ].sum()

                           __lowerCamelCase					:		Any		   =     (wxx * wyy) - (wxy**2)
                           __lowerCamelCase					:		Optional[int]		   =     wxx + wyy
                           __lowerCamelCase					:		int		   =     det - k * (trace**2)
                           # Can change the value
                           if r > 0.5:
                                  corner_list.append([x, y, r]    )
                                  color_img.itemset((y, x, 0)							,					0    )
                                  color_img.itemset((y, x, 1)							,					0    )
                                  color_img.itemset((y, x, 2)							,					255    )
             return color_img, corner_list


if __name__ == "__main__":
    __A  =				HarrisCorner(0.04, 3)
    __A,		__A  =				edge_detect.detect('''path_to_image''')
    cva.imwrite('''detect.png''', color_img)
"""simple docstring"""





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,
)


__A  =				{
    '''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:
				__A  =				['''BertTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''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:
				__A  =				[
				    '''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:
				__A  =				['''TFBertTokenizer''']

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

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