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


lowerCamelCase_						: Tuple         =  logging.get_logger(__name__)

lowerCamelCase_						: Dict         =  {
    """speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json""",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						str					      = """mctct"""

			def __init__(       self :       Dict						,	snake_case_ :       str=8065						,	snake_case_ :       int=1536						,	snake_case_ :       Dict=36						,	snake_case_ :       Optional[int]=6144						,	snake_case_ :       Any=4						,	snake_case_ :       Tuple=384						,	snake_case_ :       Optional[Any]=920						,	snake_case_ :       Optional[Any]=1e-5						,	snake_case_ :       Optional[int]=0.3						,	snake_case_ :       int="relu"						,	snake_case_ :       int=0.02						,	snake_case_ :       str=0.3						,	snake_case_ :       Dict=0.3						,	snake_case_ :       int=1						,	snake_case_ :       List[str]=0						,	snake_case_ :       Tuple=2						,	snake_case_ :       List[str]=1						,	snake_case_ :       Tuple=0.3						,	snake_case_ :       List[Any]=1						,	snake_case_ :       Optional[Any]=(7,)						,	snake_case_ :       Optional[int]=(3,)						,	snake_case_ :       Union[str, Any]=80						,	snake_case_ :       Dict=1						,	snake_case_ :       Dict=None						,	snake_case_ :       int="sum"						,	snake_case_ :       Union[str, Any]=False						,	**snake_case_ :       List[str]						,	):
							super().__init__(**snake_case_						,	pad_token_id=snake_case_						,	bos_token_id=snake_case_						,	eos_token_id=snake_case_      )
							UpperCamelCase_:							str	=						vocab_size
							UpperCamelCase_:							Optional[int]	=						hidden_size
							UpperCamelCase_:							int	=						num_hidden_layers
							UpperCamelCase_:							Any	=						intermediate_size
							UpperCamelCase_:							Dict	=						num_attention_heads
							UpperCamelCase_:							Dict	=						attention_head_dim
							UpperCamelCase_:							Optional[int]	=						max_position_embeddings
							UpperCamelCase_:							Optional[Any]	=						layer_norm_eps
							UpperCamelCase_:							Any	=						layerdrop
							UpperCamelCase_:							List[Any]	=						hidden_act
							UpperCamelCase_:							Tuple	=						initializer_range
							UpperCamelCase_:							int	=						hidden_dropout_prob
							UpperCamelCase_:							List[str]	=						attention_probs_dropout_prob
							UpperCamelCase_:							Tuple	=						pad_token_id
							UpperCamelCase_:							List[str]	=						bos_token_id
							UpperCamelCase_:							Tuple	=						eos_token_id
							UpperCamelCase_:							str	=						conv_glu_dim
							UpperCamelCase_:							Optional[Any]	=						conv_dropout
							UpperCamelCase_:							int	=						num_conv_layers
							UpperCamelCase_:							Optional[Any]	=						input_feat_per_channel
							UpperCamelCase_:							str	=						input_channels
							UpperCamelCase_:							Any	=						conv_channels
							UpperCamelCase_:							List[Any]	=						ctc_loss_reduction
							UpperCamelCase_:							List[str]	=						ctc_zero_infinity

							# prevents config testing fail with exporting to json
							UpperCamelCase_:							Optional[Any]	=						list(snake_case_      )
							UpperCamelCase_:							Optional[int]	=						list(snake_case_      )

							if len(self.conv_kernel      ) != self.num_conv_layers:
											raise ValueError(
											    """Configuration for convolutional module is incorrect. """
											    """It is required that `len(config.conv_kernel)` == `config.num_conv_layers` """
											    f'''but is `len(config.conv_kernel) = {len(self.conv_kernel      )}`, '''
											    f'''`config.num_conv_layers = {self.num_conv_layers}`.'''      )








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)






