

"""simple docstring"""
import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast







def       UpperCamelCase_					(     lowerCamelCase							:					int     , lowerCamelCase							:					str=100     , lowerCamelCase							:					Dict=" "   )						->     str:



       """simple docstring"""







       __magic_name__			:		Optional[Any]        =     text.split(lowerCamelCase   )
       return [character.join(text[i : i + n]   ).strip() for i in range(0     , len(lowerCamelCase   )     , lowerCamelCase   )]







def       UpperCamelCase_					(     lowerCamelCase							:					str   )						->     Tuple:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		str        =     [], []
       for title, text in zip(documents['''title''']     , documents['''text''']   ):
              if text is not None:
                     for passage in split_text(lowerCamelCase   ):
                            titles.append(title if title is not None else ''''''   )
                            texts.append(lowerCamelCase   )
       return {"title": titles, "text": texts}







def       UpperCamelCase_					(     lowerCamelCase							:					Dict     , lowerCamelCase							:					Dict     , lowerCamelCase							:					Dict     , lowerCamelCase							:					List[str]     , lowerCamelCase							:					Optional[Any]     , lowerCamelCase							:					List[str]   )						->     str:



       """simple docstring"""







       __magic_name__			:		List[Any]        =     load_dataset(
           '''csv'''     , data_files=[csv_path]     , split='''train'''     , delimiter='''\t'''     , column_names=['''title''', '''text''']   )
       __magic_name__			:		Optional[Any]        =     kb_dataset.map(
           lowerCamelCase     , batched=lowerCamelCase     , num_proc=1   )  # if you want you can load already splitted csv.
       __magic_name__			:		str        =     [kb_dataset.shard(lowerCamelCase     , lowerCamelCase     , contiguous=lowerCamelCase   ) for i in range(lowerCamelCase   )]
       __magic_name__			:		Optional[Any]        =     kb_list[process_num]

       __magic_name__			:		Optional[int]        =     '''data_''' + str(lowerCamelCase   )
       __magic_name__			:		Any        =     os.path.join(lowerCamelCase     , lowerCamelCase   )

       __magic_name__			:		List[Any]        =     DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''   )
       __magic_name__			:		List[str]        =     ctx_encoder.to(device=lowerCamelCase   )

       def embed(
           lowerCamelCase							:					dict     , lowerCamelCase							:					DPRContextEncoder     , lowerCamelCase							:					DPRContextEncoderTokenizerFast     , lowerCamelCase							:					Optional[int]   ) -> dict:
              __magic_name__			:		Union[str, Any]        =     ctx_tokenizer(
                  documents['''title''']     , documents['''text''']     , truncation=lowerCamelCase     , padding='''longest'''     , return_tensors='''pt'''   )['''input_ids''']
              __magic_name__			:		int        =     ctx_encoder(input_ids.to(device=lowerCamelCase   )     , return_dict=lowerCamelCase   ).pooler_output
              return {"embeddings": embeddings.detach().cpu().numpy()}

       __magic_name__			:		Dict        =     Features(
           {'''text''': Value('''string'''   ), '''title''': Value('''string'''   ), '''embeddings''': Sequence(Value('''float32'''   )   )}   )  # optional, save as float32 instead of float64 to save space

       __magic_name__			:		Dict        =     data_shrad.map(
           partial(lowerCamelCase     , ctx_encoder=lowerCamelCase     , ctx_tokenizer=lowerCamelCase     , device=lowerCamelCase   )     , batched=lowerCamelCase     , batch_size=16     , features=lowerCamelCase     , )
       dataset.save_to_disk(lowerCamelCase   )







def       UpperCamelCase_					(     lowerCamelCase							:					Any     , lowerCamelCase							:					Tuple   )						->     List[Any]:



       """simple docstring"""







       __magic_name__			:		Optional[int]        =     []

       for shard_address in glob(str(lowerCamelCase   ) + '''/*/'''   ):
              data_shard_list.append(load_from_disk(lowerCamelCase   )   )

       __magic_name__			:		int        =     concatenate_datasets(lowerCamelCase   )
       faiss.omp_set_num_threads(96   )

       __magic_name__			:		List[Any]        =     faiss.IndexHNSWFlat(768     , 128     , faiss.METRIC_INNER_PRODUCT   )
       concat.add_faiss_index('''embeddings'''     , custom_index=lowerCamelCase   )
       concat.get_index('''embeddings'''   ).save(
           lowerCamelCase   )  # since we load the index in to memory,we can directly update the index in the disk



"""simple docstring"""
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros







def       UpperCamelCase_					(     lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					Tuple   )						->     str:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		Optional[Any]        =     image.shape
       __magic_name__			:		str        =     cols - block_size[1] + 1
       __magic_name__			:		int        =     rows - block_size[0] + 1
       __magic_name__			:		Optional[Any]        =     zeros((dst_height * dst_width, block_size[1] * block_size[0])   )
       __magic_name__			:		List[Any]        =     0
       for i in range(0     , lowerCamelCase   ):
              for j in range(0     , lowerCamelCase   ):
                     __magic_name__			:		List[Any]        =     ravel(image[i : i + block_size[0], j : j + block_size[1]]   )
                     __magic_name__			:		str        =     window
                     row += 1

       return image_array







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					List[str]   )						->     Tuple:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		List[str]        =     image.shape[0], image.shape[1]
       __magic_name__			:		Any        =     filter_kernel.shape[0]
       __magic_name__			:		Optional[Any]        =     k_size // 2
       # Pads image with the edge values of array.
       __magic_name__			:		Union[str, Any]        =     pad(lowerCamelCase     , lowerCamelCase     , mode='''edge'''   )

       # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
       __magic_name__			:		List[Any]        =     imacol(lowerCamelCase     , (k_size, k_size)   )

       #  turn the kernel into shape(k*k, 1)
       __magic_name__			:		Tuple        =     ravel(lowerCamelCase   )
       # reshape and get the dst image
       __magic_name__			:		int        =     dot(lowerCamelCase     , lowerCamelCase   ).reshape(lowerCamelCase     , lowerCamelCase   )
       return dst


if __name__ == "__main__":
 # read original image
 A									=						imread(R"""../image_data/lena.jpg""")
 # turn image in gray scale value
 A									=						cvtColor(img, COLOR_BGR2GRAY)
 # Laplace operator
 A									=						array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
 A									=						img_convolve(gray, Laplace_kernel).astype(uinta)
 imshow("""Laplacian""", out)
 waitKey(0)
