


"""simple docstring"""







import cva
import numpy as np




class 			UpperCamelCase_     :

	"""simple docstring"""





	def __init__(			self      :    Tuple      ,      UpperCAmelCase__      :    Dict      ,      UpperCAmelCase__      :    Tuple       )	->     Tuple:

			if k in (0.04, 0.06):
					__SCREAMING_SNAKE_CASE						       =    k
					__SCREAMING_SNAKE_CASE						       =    window_size
			else:
					raise ValueError("invalid k value"       )




	def __str__(			self      :    Optional[Any]       )	->     List[str]:
			return str(self.k       )




	def   UpperCAmelCase_				(			self      :    Tuple      ,      UpperCAmelCase__      :    Dict       )	->     Optional[int]:
			__SCREAMING_SNAKE_CASE						       =    cva.imread(SCREAMING_SNAKE_CASE_      ,      0       )
			__SCREAMING_SNAKE_CASE						       =    img.shape
			__SCREAMING_SNAKE_CASE						       =    []
			__SCREAMING_SNAKE_CASE						       =    img.copy()
			__SCREAMING_SNAKE_CASE						       =    cva.cvtColor(SCREAMING_SNAKE_CASE_      ,      cva.COLOR_GRAY2RGB       )
			__SCREAMING_SNAKE_CASE						       =    np.gradient(SCREAMING_SNAKE_CASE_       )
			__SCREAMING_SNAKE_CASE						       =    dx**2
			__SCREAMING_SNAKE_CASE						       =    dy**2
			__SCREAMING_SNAKE_CASE						       =    dx * dy
			__SCREAMING_SNAKE_CASE						       =    0.04
			__SCREAMING_SNAKE_CASE						       =    self.window_size // 2
			for y in range(SCREAMING_SNAKE_CASE_      ,      h - offset       ):
					for x in range(SCREAMING_SNAKE_CASE_      ,      w - offset       ):
							__SCREAMING_SNAKE_CASE						       =    ixx[
							    y - offset : y + offset + 1, x - offset : x + offset + 1
							].sum()
							__SCREAMING_SNAKE_CASE						       =    iyy[
							    y - offset : y + offset + 1, x - offset : x + offset + 1
							].sum()
							__SCREAMING_SNAKE_CASE						       =    ixy[
							    y - offset : y + offset + 1, x - offset : x + offset + 1
							].sum()

							__SCREAMING_SNAKE_CASE						       =    (wxx * wyy) - (wxy**2)
							__SCREAMING_SNAKE_CASE						       =    wxx + wyy
							__SCREAMING_SNAKE_CASE						       =    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)      ,      2_5_5       )
			return color_img, corner_list


if __name__ == "__main__":
		a__    :							List[Any]            =					HarrisCorner(0.04, 3)
		a__    :							Tuple            =					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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
		from ..ta.tokenization_ta import TaTokenizer
else:
		from ...utils.dummy_sentencepiece_objects import TaTokenizer

a__    :							Dict            =					TaTokenizer

if is_tokenizers_available():
		from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
		from ...utils.dummy_tokenizers_objects import TaTokenizerFast

a__    :							Any            =					TaTokenizerFast

a__    :							Tuple            =					{'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							int            =					[
		    '''MT5EncoderModel''',
		    '''MT5ForConditionalGeneration''',
		    '''MT5ForQuestionAnswering''',
		    '''MT5Model''',
		    '''MT5PreTrainedModel''',
		    '''MT5Stack''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							str            =					['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Union[str, Any]            =					['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
		from .configuration_mta import MTaConfig, MTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mta import (
				    MTaEncoderModel,
				    MTaForConditionalGeneration,
				    MTaForQuestionAnswering,
				    MTaModel,
				    MTaPreTrainedModel,
				    MTaStack,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
		import sys

		a__    :							str            =					_LazyModule(
		    __name__,
		    globals()['''__file__'''],
		    _import_structure,
		    extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
		    module_spec=__spec__,
		)
