


import cva
import numpy as np






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_					,   A_    )  ->       Optional[Any]:

         if k in (0.04, 0.06):
              __UpperCamelCase    							=k
              __UpperCamelCase    							=window_size
         else:
              raise ValueError('invalid k value'    )






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






    def _a					(			self					,   A_    )  ->       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    							=0.04
         __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)					,   255    )
         return color_img, corner_list


if __name__ == "__main__":
   _A		       =						HarrisCorner(0.04, 3)
   _A       ,     _A		       =						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,
		)
