




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


UpperCAmelCase__				     = logging.get_logger(__name__)

UpperCAmelCase__				     = {
    "microsoft/trocr-base-handwritten": (
        "https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       Dict       	=		'trocr'
						UpperCamelCase_	:       Any       	=		['past_key_values']
						UpperCamelCase_	:       str       	=		{
						    'num_attention_heads': 'decoder_attention_heads',
						    'hidden_size': 'd_model',
						    'num_hidden_layers': 'decoder_layers',
						}






						def __init__( self :     int				,							lowerCamelCase__ :     Any=50_265				,							lowerCamelCase__ :     Union[str, Any]=1_024				,							lowerCamelCase__ :     Optional[int]=12				,							lowerCamelCase__ :     Dict=16				,							lowerCamelCase__ :     Optional[Any]=4_096				,							lowerCamelCase__ :     Tuple="gelu"				,							lowerCamelCase__ :     List[str]=512				,							lowerCamelCase__ :     Optional[int]=0.1				,							lowerCamelCase__ :     Union[str, Any]=0.0				,							lowerCamelCase__ :     Any=0.0				,							lowerCamelCase__ :     int=2				,							lowerCamelCase__ :     Any=0.0_2				,							lowerCamelCase__ :     Union[str, Any]=0.0				,							lowerCamelCase__ :     Dict=True				,							lowerCamelCase__ :     List[Any]=False				,							lowerCamelCase__ :     int=True				,							lowerCamelCase__ :     str=True				,							lowerCamelCase__ :     Dict=1				,							lowerCamelCase__ :     int=0				,							lowerCamelCase__ :     int=2				,							**lowerCamelCase__ :     int				,							)    ->						Any:
													"""simple docstring"""


													__lowercase      =    vocab_size
													__lowercase      =    d_model
													__lowercase      =    decoder_layers
													__lowercase      =    decoder_attention_heads
													__lowercase      =    decoder_ffn_dim
													__lowercase      =    activation_function
													__lowercase      =    max_position_embeddings
													__lowercase      =    dropout
													__lowercase      =    attention_dropout
													__lowercase      =    activation_dropout
													__lowercase      =    init_std
													__lowercase      =    decoder_layerdrop
													__lowercase      =    use_cache
													__lowercase      =    scale_embedding
													__lowercase      =    use_learned_position_embeddings
													__lowercase      =    layernorm_embedding

													super().__init__(
													    pad_token_id=lowerCamelCase__				,							bos_token_id=lowerCamelCase__				,							eos_token_id=lowerCamelCase__				,							decoder_start_token_id=lowerCamelCase__				,							**lowerCamelCase__				,							)







import cva
import numpy as np







class a :
						"""simple docstring"""

						def __init__( self :     Dict				,							lowerCamelCase__ :     float				,							lowerCamelCase__ :     int				)    ->						Dict:
													"""simple docstring"""


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






						def __str__( self :     int				)    ->						str:
													"""simple docstring"""


													return str(self.k				)






						def     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     str				)    ->						tuple[cva.Mat, list[list[int]]]:
													"""simple docstring"""


													__lowercase      =    cva.imread(lowerCamelCase__				,							0				)
													__lowercase     ,     __lowercase      =    img.shape
													__lowercase      =    []
													__lowercase      =    img.copy()
													__lowercase      =    cva.cvtColor(lowerCamelCase__				,							cva.COLOR_GRAY2RGB				)
													__lowercase     ,     __lowercase      =    np.gradient(lowerCamelCase__				)
													__lowercase      =    dx**2
													__lowercase      =    dy**2
													__lowercase      =    dx * dy
													__lowercase      =    0.0_4
													__lowercase      =    self.window_size // 2
													for y in range(lowerCamelCase__				,							h - offset				):
																				for x in range(lowerCamelCase__				,							w - offset				):
																											__lowercase      =    ixx[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()
																											__lowercase      =    iyy[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()
																											__lowercase      =    ixy[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()

																											__lowercase      =    (wxx * wyy) - (wxy**2)
																											__lowercase      =    wxx + wyy
																											__lowercase      =    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__":
			UpperCAmelCase__				     = HarrisCorner(0.04, 3)
			UpperCAmelCase__     ,							UpperCAmelCase__				     = edge_detect.detect("path_to_image")
			cva.imwrite("detect.png", color_img)






