


import cva
import numpy as np







class 				snake_case__:







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase							)					->    List[Any]:

										if k in (0.04, 0.06):
														lowerCAmelCase_ :      Tuple    				=				k
														lowerCAmelCase_ :      List[str]    				=				window_size
										else:
														raise ValueError('''invalid k value'''							)




						def __str__(				self							)					->    str:
										return str(self.k							)




						def 						lowercase_ (				self   ,						__lowercase							)					->    tuple[cva.Mat, list[list[int]]]:
										lowerCAmelCase_ :      List[Any]    				=				cva.imread(__lowercase   ,						0							)
										lowerCAmelCase_				,  lowerCAmelCase_ :      Dict    				=				img.shape
										lowerCAmelCase_ :      list[list[int]]    				=				[]
										lowerCAmelCase_ :      Optional[int]    				=				img.copy()
										lowerCAmelCase_ :      int    				=				cva.cvtColor(__lowercase   ,						cva.COLOR_GRAY2RGB							)
										lowerCAmelCase_				,  lowerCAmelCase_ :      Tuple    				=				np.gradient(__lowercase							)
										lowerCAmelCase_ :      Optional[int]    				=				dx**2
										lowerCAmelCase_ :      List[Any]    				=				dy**2
										lowerCAmelCase_ :      Optional[Any]    				=				dx * dy
										lowerCAmelCase_ :      Optional[Any]    				=				0.04
										lowerCAmelCase_ :      int    				=				self.window_size // 2
										for y in range(__lowercase   ,						h - offset							):
														for x in range(__lowercase   ,						w - offset							):
																		lowerCAmelCase_ :      Union[str, Any]    				=				ixx[
																		    y - offset : y + offset + 1, x - offset : x + offset + 1
																		].sum()
																		lowerCAmelCase_ :      Tuple    				=				iyy[
																		    y - offset : y + offset + 1, x - offset : x + offset + 1
																		].sum()
																		lowerCAmelCase_ :      List[str]    				=				ixy[
																		    y - offset : y + offset + 1, x - offset : x + offset + 1
																		].sum()

																		lowerCAmelCase_ :      List[str]    				=				(wxx * wyy) - (wxy**2)
																		lowerCAmelCase_ :      Any    				=				wxx + wyy
																		lowerCAmelCase_ :      Dict    				=				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__":
						_UpperCAmelCase			:  Optional[int]     =HarrisCorner(0.04, 3)
						_UpperCAmelCase     ,					_UpperCAmelCase			:  int     =edge_detect.detect("""path_to_image""")
						cva.imwrite("""detect.png""", color_img)



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCAmelCase			:  Union[str, Any]     ={
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCAmelCase			:  Optional[Any]     =["""LlamaTokenizer"""]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCAmelCase			:  Optional[int]     =["""LlamaTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCAmelCase			:  Tuple     =[
						    """LlamaForCausalLM""",
						    """LlamaModel""",
						    """LlamaPreTrainedModel""",
						    """LlamaForSequenceClassification""",
						]


if TYPE_CHECKING:
						from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_llama import LlamaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_llama_fast import LlamaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
						import sys

						_UpperCAmelCase			:  List[Any]     =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)