'''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  _lowerCAmelCase				(				__a			,      __a=1_00			,      __a=" "      )  ->      str:
     '''simple docstring'''







     _UpperCamelCase  :int       =text.split(a_      )
     return [character.join(text[i : i + n]      ).strip() for i in range(0			,      len(a_      )			,      a_      )]



def  _lowerCAmelCase				(				__a      )  ->      Optional[int]:
     '''simple docstring'''







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



def  _lowerCAmelCase				(				__a			,      __a			,      __a			,      __a			,      __a			,      __a      )  ->      List[Any]:
     '''simple docstring'''







     _UpperCamelCase  :int       =load_dataset(
         """csv"""			,      data_files=[csv_path]			,      split="""train"""			,      delimiter="""\t"""			,      column_names=["""title""", """text"""]      )
     _UpperCamelCase  :Optional[Any]       =kb_dataset.map(
         a_			,      batched=a_			,      num_proc=1      )  # if you want you can load already splitted csv.
     _UpperCamelCase  :str       =[kb_dataset.shard(a_			,      a_			,      contiguous=a_      ) for i in range(a_      )]
     _UpperCamelCase  :List[Any]       =kb_list[process_num]

     _UpperCamelCase  :Optional[int]       ='data_' + str(a_      )
     _UpperCamelCase  :str       =os.path.join(a_			,      a_      )

     _UpperCamelCase  :Optional[Any]       =DPRContextEncoderTokenizerFast.from_pretrained("""facebook/dpr-ctx_encoder-multiset-base"""      )
     _UpperCamelCase  :List[str]       =ctx_encoder.to(device=a_      )

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

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

     _UpperCamelCase  :str       =data_shrad.map(
         partial(a_			,      ctx_encoder=a_			,      ctx_tokenizer=a_			,      device=a_      )			,      batched=a_			,      batch_size=16			,      features=a_			,      )
     dataset.save_to_disk(a_      )



def  _lowerCAmelCase				(				__a			,      __a      )  ->      Optional[int]:
     '''simple docstring'''







     _UpperCamelCase  :Optional[int]       =[]

     for shard_address in glob(str(a_      ) + """/*/"""      ):
          data_shard_list.append(load_from_disk(a_      )      )

     _UpperCamelCase  :List[Any]       =concatenate_datasets(a_      )
     faiss.omp_set_num_threads(96      )

     _UpperCamelCase  :Any       =faiss.IndexHNSWFlat(7_68			,      1_28			,      faiss.METRIC_INNER_PRODUCT      )
     concat.add_faiss_index("""embeddings"""			,      custom_index=a_      )
     concat.get_index("""embeddings"""      ).save(
         a_      )  # since we load the index in to memory,we can directly update the index in the disk







import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


A_			:Tuple	    =		1000




class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__				,    lowerCamelCase__=1.2				,    lowerCamelCase__="g"				,    lowerCamelCase__=0.5				,    lowerCamelCase__="-"				,    lowerCamelCase__="test_out.jpg"				,    lowerCamelCase__=True				,    lowerCamelCase__=False				,    lowerCamelCase__=None				,    lowerCamelCase__=None				,    lowerCamelCase__=0.7				,    ):




									"""simple docstring"""

									if isinstance(lowerCamelCase__				,    torch.Tensor       ):
														__UpperCamelCase :   Union[str, Any]									=img.numpy().astype('np.uint8'       )
									if isinstance(lowerCamelCase__				,    lowerCamelCase__       ):
														__UpperCamelCase :   List[str]									=img_tensorize(lowerCamelCase__       )
									assert isinstance(lowerCamelCase__				,    np.ndarray       )

									__UpperCamelCase						,							__UpperCamelCase :   Any									=img.shape[1], img.shape[0]
									__UpperCamelCase :   Any									=mplfigure.Figure(frameon=lowerCamelCase__       )
									__UpperCamelCase :   Any									=fig.get_dpi()
									__UpperCamelCase :   Optional[Any]									=(width * scale + 1E-2) / dpi
									__UpperCamelCase :   Optional[Any]									=(height * scale + 1E-2) / dpi
									fig.set_size_inches(lowerCamelCase__				,    lowerCamelCase__       )
									__UpperCamelCase :   Dict									=fig.add_axes([0.0, 0.0, 1.0, 1.0]       )
									ax.axis('off'       )
									ax.set_xlim(0.0				,    lowerCamelCase__       )
									ax.set_ylim(lowerCamelCase__       )

									__UpperCamelCase :   List[Any]									=saveas
									__UpperCamelCase :   Any									=rgb
									__UpperCamelCase :   int									=pynb
									__UpperCamelCase :   Optional[int]									=img
									__UpperCamelCase :   List[Any]									=edgecolor
									__UpperCamelCase :   List[str]									=0.5
									__UpperCamelCase :   Optional[Any]									=linestyle
									__UpperCamelCase :   Dict									=int(np.sqrt(min(lowerCamelCase__				,    lowerCamelCase__       )       ) * scale // 3       )
									__UpperCamelCase :   List[Any]									=width
									__UpperCamelCase :   List[Any]									=height
									__UpperCamelCase :   int									=scale
									__UpperCamelCase :   List[str]									=fig
									__UpperCamelCase :   Union[str, Any]									=ax
									__UpperCamelCase :   Dict									=pad
									__UpperCamelCase :   List[Any]									=idaobj
									__UpperCamelCase :   List[Any]									=idaattr
									__UpperCamelCase :   List[str]									=FigureCanvasAgg(lowerCamelCase__       )


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__=None       ):




									"""simple docstring"""

									if color is None:
														__UpperCamelCase :   Optional[Any]									=self.edgecolor
									((__UpperCamelCase)						,							(__UpperCamelCase)						,							(__UpperCamelCase)						,							(__UpperCamelCase)) :   str									=box
									__UpperCamelCase :   List[Any]									=xa - xa
									__UpperCamelCase :   List[Any]									=ya - ya
									self.ax.add_patch(
									    mpl.patches.Rectangle(
									        (xa, ya)				,    lowerCamelCase__				,    lowerCamelCase__				,    fill=lowerCamelCase__				,    edgecolor=lowerCamelCase__				,    linewidth=self.font_size // 3				,    alpha=self.alpha				,    linestyle=self.linestyle				,    )       )


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__=None				,    lowerCamelCase__=None				,    lowerCamelCase__=None				,    lowerCamelCase__=None       ):




									"""simple docstring"""

									if len(boxes.shape       ) > 2:
														__UpperCamelCase :   List[str]									=boxes[0]
									if len(obj_ids.shape       ) > 1:
														__UpperCamelCase :   Tuple									=obj_ids[0]
									if len(obj_scores.shape       ) > 1:
														__UpperCamelCase :   int									=obj_scores[0]
									if len(attr_ids.shape       ) > 1:
														__UpperCamelCase :   str									=attr_ids[0]
									if len(attr_scores.shape       ) > 1:
														__UpperCamelCase :   Optional[int]									=attr_scores[0]
									if isinstance(lowerCamelCase__				,    torch.Tensor       ):
														__UpperCamelCase :   Dict									=boxes.numpy()
									if isinstance(lowerCamelCase__				,    lowerCamelCase__       ):
														__UpperCamelCase :   Tuple									=np.array(lowerCamelCase__       )
									assert isinstance(lowerCamelCase__				,    np.ndarray       )
									__UpperCamelCase :   Dict									=np.prod(boxes[:, 2:] - boxes[:, :2]				,    axis=1       )
									__UpperCamelCase :   int									=np.argsort(-areas       ).tolist()
									__UpperCamelCase :   Optional[Any]									=boxes[sorted_idxs] if boxes is not None else None
									__UpperCamelCase :   List[Any]									=obj_ids[sorted_idxs] if obj_ids is not None else None
									__UpperCamelCase :   Tuple									=obj_scores[sorted_idxs] if obj_scores is not None else None
									__UpperCamelCase :   Dict									=attr_ids[sorted_idxs] if attr_ids is not None else None
									__UpperCamelCase :   Optional[Any]									=attr_scores[sorted_idxs] if attr_scores is not None else None

									__UpperCamelCase :   int									=[self._random_color(maximum=1       ) for _ in range(len(lowerCamelCase__       )       )]
									__UpperCamelCase :   Tuple									=[assigned_colors[idx] for idx in sorted_idxs]
									if obj_ids is not None:
														__UpperCamelCase :   str									=self._create_text_labels_attr(lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__       )
														for i in range(len(lowerCamelCase__       )       ):
																			__UpperCamelCase :   Dict									=assigned_colors[i]
																			self.add_box(boxes[i]				,    lowerCamelCase__       )
																			self.draw_labels(labels[i]				,    boxes[i]				,    lowerCamelCase__       )


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   Dict									=box
									__UpperCamelCase :   str									=(xa, ya)
									__UpperCamelCase :   Optional[int]									=(ya - ya) * (xa - xa)
									__UpperCamelCase :   Optional[Any]									=_SMALL_OBJ * self.scale
									if instance_area < small or ya - ya < 40 * self.scale:
														if ya >= self.height - 5:
																			__UpperCamelCase :   str									=(xa, ya)
														else:
																			__UpperCamelCase :   str									=(xa, ya)

									__UpperCamelCase :   Tuple									=(ya - ya) / np.sqrt(self.height * self.width       )
									__UpperCamelCase :   List[Any]									=self._change_color_brightness(lowerCamelCase__				,    brightness_factor=0.7       )
									__UpperCamelCase :   Any									=np.clip((height_ratio - 0.02) / 0.08 + 1				,    1.2				,    2       )
									font_size *= 0.75 * self.font_size

									self.draw_text(
									    text=lowerCamelCase__				,    position=lowerCamelCase__				,    color=lowerCamelCase__				,    )


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__="g"				,    lowerCamelCase__="left"				,    ):




									"""simple docstring"""

									__UpperCamelCase :   int									=0
									__UpperCamelCase :   Optional[int]									=self.font_size
									__UpperCamelCase :   Optional[Any]									=np.maximum(list(mplc.to_rgb(lowerCamelCase__       )       )				,    0.2       )
									__UpperCamelCase :   int									=max(0.8				,    np.max(lowerCamelCase__       )       )
									__UpperCamelCase :   str									={
									    'facecolor': 'black',
									    'alpha': self.alpha,
									    'pad': self.pad,
									    'edgecolor': 'none',
									}
									__UpperCamelCase						,							__UpperCamelCase :   Union[str, Any]									=position
									self.ax.text(
									    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    size=font_size * self.scale				,    family='sans-serif'				,    bbox=lowerCamelCase__				,    verticalalignment='top'				,    horizontalalignment=lowerCamelCase__				,    color=lowerCamelCase__				,    zorder=10				,    rotation=lowerCamelCase__				,    )


				def  __lowercase				(					self				,    lowerCamelCase__=None       ):




									"""simple docstring"""

									if saveas is None:
														__UpperCamelCase :   Optional[int]									=self.saveas
									if saveas.lower().endswith('.jpg'       ) or saveas.lower().endswith('.png'       ):
														cva.imwrite(
														    lowerCamelCase__				,    self._get_buffer()[:, :, ::-1]				,    )
									else:
														self.fig.savefig(lowerCamelCase__       )


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[int]									=[self.idaobj[i] for i in classes]
									__UpperCamelCase :   str									=[self.idaattr[i] for i in attr_classes]
									__UpperCamelCase :   Any									=[
									    f'{label} {score:.2f} {attr} {attr_score:.2f}'
									    for label, score, attr, attr_score in zip(lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__       )
									]
									return labels


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Tuple									=[self.idaobj[i] for i in classes]
									if scores is not None:
														if labels is None:
																			__UpperCamelCase :   List[str]									=['{:.0f}%'.format(s * 100       ) for s in scores]
														else:
																			__UpperCamelCase :   Union[str, Any]									=['{} {:.0f}%'.format(lowerCamelCase__				,    s * 100       ) for li, s in zip(lowerCamelCase__				,    lowerCamelCase__       )]
									return labels


				def  __lowercase				(					self				,    lowerCamelCase__=255       ):




									"""simple docstring"""

									__UpperCamelCase :   Any									=np.random.randint(0				,    len(_COLORS       )       )
									__UpperCamelCase :   List[str]									=_COLORS[idx] * maximum
									if not self.rgb:
														__UpperCamelCase :   Optional[Any]									=ret[::-1]
									return ret


				def  __lowercase				(					self       ):




									"""simple docstring"""

									if not self.pynb:
														__UpperCamelCase						,							__UpperCamelCase :   Tuple									=self.canvas.print_to_buffer()
														if (width, height) != (self.width, self.height):
																			__UpperCamelCase :   Optional[int]									=cva.resize(self.img				,    (width, height)       )
														else:
																			__UpperCamelCase :   Tuple									=self.img
									else:
														__UpperCamelCase :   Optional[int]									=io.BytesIO()  # works for cairo backend
														self.canvas.print_rgba(lowerCamelCase__       )
														__UpperCamelCase						,							__UpperCamelCase :   int									=self.width, self.height
														__UpperCamelCase :   Union[str, Any]									=buf.getvalue()
														__UpperCamelCase :   Dict									=self.img

									__UpperCamelCase :   Dict									=np.frombuffer(lowerCamelCase__				,    dtype='uint8'       )
									__UpperCamelCase :   List[str]									=buffer.reshape(lowerCamelCase__				,    lowerCamelCase__				,    4       )
									__UpperCamelCase						,							__UpperCamelCase :   Union[str, Any]									=np.split(lowerCamelCase__				,    [3]				,    axis=2       )

									try:
														import numexpr as ne  # fuse them with numexpr

														__UpperCamelCase :   List[Any]									=ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'       )
									except ImportError:
														__UpperCamelCase :   int									=alpha.astype('float32'       ) / 255.0
														__UpperCamelCase :   Tuple									=img * (1 - alpha) + rgb * alpha

									return visualized_image.astype('uint8'       )







				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									assert brightness_factor >= -1.0 and brightness_factor <= 1.0
									__UpperCamelCase :   List[Any]									=mplc.to_rgb(lowerCamelCase__       )
									__UpperCamelCase :   Dict									=colorsys.rgb_to_hls(*mplc.to_rgb(lowerCamelCase__       )       )
									__UpperCamelCase :   Dict									=polygon_color[1] + (brightness_factor * polygon_color[1])
									__UpperCamelCase :   Tuple									=0.0 if modified_lightness < 0.0 else modified_lightness
									__UpperCamelCase :   str									=1.0 if modified_lightness > 1.0 else modified_lightness
									__UpperCamelCase :   Union[str, Any]									=colorsys.hls_to_rgb(polygon_color[0]				,    lowerCamelCase__				,    polygon_color[2]       )
									return modified_color
A_			:Union[str, Any]	    =		(
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)

