'''simple docstring'''


import cva
import numpy as np






class 						A   :





							def __init__(      self				:							Union[str, Any]     ,		__a				:							float     ,		__a				:							int	)							->	List[str]:

								if k in (0.0_4, 0.0_6):
									__UpperCAmelCase							=			k
									__UpperCAmelCase							=			window_size
								else:
									raise ValueError('''invalid k value'''	)





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






							def       snake_case__						(      self				:							List[str]     ,		__a				:							str	)							->	tuple[cva.Mat, list[list[int]]]:
								__UpperCAmelCase							=			cva.imread(__a     ,		0	)
								__UpperCAmelCase				,			__UpperCAmelCase							=			img.shape
								__UpperCAmelCase							=			[]
								__UpperCAmelCase							=			img.copy()
								__UpperCAmelCase							=			cva.cvtColor(__a     ,		cva.COLOR_GRAY2RGB	)
								__UpperCAmelCase				,			__UpperCAmelCase							=			np.gradient(__a	)
								__UpperCAmelCase							=			dx**2
								__UpperCAmelCase							=			dy**2
								__UpperCAmelCase							=			dx * dy
								__UpperCAmelCase							=			0.0_4
								__UpperCAmelCase							=			self.window_size // 2
								for y in range(__a     ,		h - offset	):
									for x in range(__a     ,		w - offset	):
										__UpperCAmelCase							=			ixx[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()
										__UpperCAmelCase							=			iyy[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()
										__UpperCAmelCase							=			ixy[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()

										__UpperCAmelCase							=			(wxx * wyy) - (wxy**2)
										__UpperCAmelCase							=			wxx + wyy
										__UpperCAmelCase							=			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__":
					__lowerCAmelCase	:       List[str]						       =	HarrisCorner(0.0_4, 3)
					__lowerCAmelCase       ,	__lowerCAmelCase	:       Dict						       =	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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase	:       Optional[Any]						       =	{
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       str						       =	["LlamaTokenizer"]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       str						       =	["LlamaTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       Union[str, Any]						       =	[
					    "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

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