
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_						: Optional[int]         =  logging.get_logger(__name__)

lowerCamelCase_						: str         =  {
    """s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[Any]					      = """open-llama"""

			def __init__(       self :       str						,	snake_case_ :       Optional[Any]=10_0000						,	snake_case_ :       List[str]=4096						,	snake_case_ :       Optional[Any]=1_1008						,	snake_case_ :       Union[str, Any]=32						,	snake_case_ :       Tuple=32						,	snake_case_ :       Dict="silu"						,	snake_case_ :       Optional[int]=2048						,	snake_case_ :       List[str]=0.02						,	snake_case_ :       Optional[Any]=1e-6						,	snake_case_ :       Dict=True						,	snake_case_ :       Union[str, Any]=0						,	snake_case_ :       Tuple=1						,	snake_case_ :       Tuple=2						,	snake_case_ :       Union[str, Any]=False						,	snake_case_ :       Any=True						,	snake_case_ :       List[str]=0.1						,	snake_case_ :       Tuple=0.1						,	snake_case_ :       List[Any]=True						,	snake_case_ :       str=True						,	snake_case_ :       Any=None						,	**snake_case_ :       Optional[int]						,	):
							UpperCamelCase_:							str	=						vocab_size
							UpperCamelCase_:							int	=						max_position_embeddings
							UpperCamelCase_:							Union[str, Any]	=						hidden_size
							UpperCamelCase_:							Union[str, Any]	=						intermediate_size
							UpperCamelCase_:							Dict	=						num_hidden_layers
							UpperCamelCase_:							Any	=						num_attention_heads
							UpperCamelCase_:							Any	=						hidden_act
							UpperCamelCase_:							str	=						initializer_range
							UpperCamelCase_:							Any	=						rms_norm_eps
							UpperCamelCase_:							Any	=						use_cache
							UpperCamelCase_:							Any	=						kwargs.pop(
							    """use_memorry_efficient_attention"""						,	snake_case_      )
							UpperCamelCase_:							Union[str, Any]	=						hidden_dropout_prob
							UpperCamelCase_:							Tuple	=						attention_dropout_prob
							UpperCamelCase_:							Dict	=						use_stable_embedding
							UpperCamelCase_:							Tuple	=						shared_input_output_embedding
							UpperCamelCase_:							str	=						rope_scaling
							self._rope_scaling_validation()

							super().__init__(
							    pad_token_id=snake_case_						,	bos_token_id=snake_case_						,	eos_token_id=snake_case_						,	tie_word_embeddings=snake_case_						,	**snake_case_						,	)




			def       lowerCAmelCase__	(       self :       List[Any]      ):
							if self.rope_scaling is None:
											return

							if not isinstance(self.rope_scaling						,	snake_case_      ) or len(self.rope_scaling      ) != 2:
											raise ValueError(
											    """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
											    f'''got {self.rope_scaling}'''      )
							UpperCamelCase_:							Any	=						self.rope_scaling.get("""type"""						,	snake_case_      )
							UpperCamelCase_:							int	=						self.rope_scaling.get("""factor"""						,	snake_case_      )
							if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
											raise ValueError(
											    f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''      )
							if rope_scaling_factor is None or not isinstance(snake_case_						,	snake_case_      ) or rope_scaling_factor <= 1.0:
											raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''      )








import cva
import numpy as np

class        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       Dict						,	snake_case_ :       float						,	snake_case_ :       int      ):

							if k in (0.04, 0.06):
											UpperCamelCase_:							Union[str, Any]	=						k
											UpperCamelCase_:							Union[str, Any]	=						window_size
							else:
											raise ValueError("""invalid k value"""      )

			def __str__(       self :       int      ):
							return str(self.k      )




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

															UpperCamelCase_:							List[str]	=						(wxx * wyy) - (wxy**2)
															UpperCamelCase_:							Optional[int]	=						wxx + wyy
															UpperCamelCase_:							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)						,	255      )
							return color_img, corner_list


if __name__ == "__main__":
							lowerCamelCase_						: Optional[Any]         =  HarrisCorner(0.04, 3)
							lowerCamelCase_   ,       lowerCamelCase_						: Any         =  edge_detect.detect("""path_to_image""")
							cva.imwrite("""detect.png""", color_img)






