import cva
import numpy as np






class 		a__						:

					def __init__(    self      :       Any,_A      :       float,_A      :       int    ):







							"""simple docstring"""






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

					def __str__(    self      :       Union[str, Any]    ):







							"""simple docstring"""






							return str(self.k    )






					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       str    ):







							"""simple docstring"""






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

											SCREAMING_SNAKE_CASE_  :		int				      =    (wxx * wyy) - (wxy**2)
											SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    wxx + wyy
											SCREAMING_SNAKE_CASE_  :		List[Any]				      =    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[int]       =  HarrisCorner(0.04, 3)
		__lowerCamelCase   ,						__lowerCamelCase					:	List[Any]       =  edge_detect.detect('''path_to_image''')
		cva.imwrite('''detect.png''', color_img)

import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration




def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   str   , lowerCAmelCase							:   str   , lowerCAmelCase							:   Path   , lowerCAmelCase							:   str = None   , lowerCAmelCase							:   str = None   , lowerCAmelCase							:   str = None   , ):
		"""simple docstring"""







		if config_name_or_path is None:
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

		if generator_tokenizer_name_or_path is None:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    generator_name_or_path

		if question_encoder_tokenizer_name_or_path is None:
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    question_encoder_name_or_path

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

		# Save model.
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    RagConfig.from_pretrained(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    AutoConfig.from_pretrained(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		int				      =    AutoConfig.from_pretrained(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    gen_config
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    question_encoder_config

		SCREAMING_SNAKE_CASE_  :		Dict				      =    model_class.from_pretrained_question_encoder_generator(
		    lowerCAmelCase   , lowerCAmelCase   , config=lowerCAmelCase	)
		rag_model.save_pretrained(lowerCAmelCase	)

		# Sanity check.
		model_class.from_pretrained(lowerCAmelCase	)

		# Save tokenizers.
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    AutoTokenizer.from_pretrained(lowerCAmelCase	)
		gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AutoTokenizer.from_pretrained(lowerCAmelCase	)
		question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"	)


if __name__ == "__main__":
		__lowerCamelCase					:	List[Any]       =  argparse.ArgumentParser()
		parser.add_argument(
		    '''--model_type''',
		    choices=['''rag_sequence''', '''rag_token'''],
		    required=True,
		    type=str,
		    help='''RAG model type: rag_sequence, rag_token''',
		)
		parser.add_argument('''--dest''', type=str, required=True, help='''Path to the output checkpoint directory.''')
		parser.add_argument('''--generator_name_or_path''', type=str, required=True, help='''Generator model identifier''')
		parser.add_argument(
		    '''--question_encoder_name_or_path''', type=str, required=True, help='''Question encoder model identifier'''
		)

		parser.add_argument(
		    '''--generator_tokenizer_name_or_path''',
		    type=str,
		    help='''Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``''',
		)
		parser.add_argument(
		    '''--question_encoder_tokenizer_name_or_path''',
		    type=str,
		    help='''Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``''',
		)
		parser.add_argument(
		    '''--config_name_or_path''',
		    type=str,
		    help=(
		        '''Identifier of the model config to use, if not provided, resolves to a base config for a given'''
		        ''' ``model_type``'''
		    ),
		)

		__lowerCamelCase					:	str       =  parser.parse_args()

		__lowerCamelCase					:	int       =  Path(args.dest)
		dest_dir.mkdir(exist_ok=True)

		consolidate(
		    args.model_type,
		    args.generator_name_or_path,
		    args.question_encoder_name_or_path,
		    dest_dir,
		    args.config_name_or_path,
		    args.generator_tokenizer_name_or_path,
		    args.question_encoder_tokenizer_name_or_path,
		)
