




'''simple docstring'''





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


lowercase__			=	1_0_0_0






class 						lowercase     :






      def __init__(self ,  a__ ,  a__=1.2 ,  a__="g" ,  a__=0.5 ,  a__="-" ,  a__="test_out.jpg" ,  a__=True ,  a__=False ,  a__=None ,  a__=None ,  a__=0.7 ,  )       ->   int:
         if isinstance(a__ ,  torch.Tensor					):
            SCREAMING_SNAKE_CASE__			:				str       						= img.numpy().astype('''np.uint8'''					)
         if isinstance(a__ ,  a__					):
            SCREAMING_SNAKE_CASE__			:				str       						= img_tensorize(a__					)
         assert isinstance(a__ ,  np.ndarray					)

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

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






      def    A__(self ,  a__ ,  a__=None					)       ->   Optional[int]:
         if color is None:
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.edgecolor
         ((SCREAMING_SNAKE_CASE__)						,				(SCREAMING_SNAKE_CASE__)						,				(SCREAMING_SNAKE_CASE__)						,				(SCREAMING_SNAKE_CASE__))			:				Optional[int]       						= box
         SCREAMING_SNAKE_CASE__			:				List[str]       						= xa - xa
         SCREAMING_SNAKE_CASE__			:				Tuple       						= ya - ya
         self.ax.add_patch(
             mpl.patches.Rectangle(
                 (xa, ya) ,  a__ ,  a__ ,  fill=a__ ,  edgecolor=a__ ,  linewidth=self.font_size // 3 ,  alpha=self.alpha ,  linestyle=self.linestyle ,  )					)






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

         SCREAMING_SNAKE_CASE__			:				str       						= [self._random_color(maximum=1					) for _ in range(len(a__					)					)]
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= [assigned_colors[idx] for idx in sorted_idxs]
         if obj_ids is not None:
            SCREAMING_SNAKE_CASE__			:				List[Any]       						= self._create_text_labels_attr(a__ ,  a__ ,  a__ ,  a__					)
            for i in range(len(a__					)					):
               SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= assigned_colors[i]
               self.add_box(boxes[i] ,  a__					)
               self.draw_labels(labels[i] ,  boxes[i] ,  a__					)






      def    A__(self ,  a__ ,  a__ ,  a__					)       ->   str:
         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				str       						= box
         SCREAMING_SNAKE_CASE__			:				Any       						= (xa, ya)
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= (ya - ya) * (xa - xa)
         SCREAMING_SNAKE_CASE__			:				Tuple       						= _SMALL_OBJ * self.scale
         if instance_area < small or ya - ya < 4_0 * self.scale:
            if ya >= self.height - 5:
               SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= (xa, ya)
            else:
               SCREAMING_SNAKE_CASE__			:				Optional[int]       						= (xa, ya)

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

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






      def    A__(self ,  a__ ,  a__ ,  a__="g" ,  a__="left" ,  )       ->   Optional[int]:
         SCREAMING_SNAKE_CASE__			:				str       						= 0
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self.font_size
         SCREAMING_SNAKE_CASE__			:				Dict       						= np.maximum(list(mplc.to_rgb(a__					)					) ,  0.2					)
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= max(0.8 ,  np.max(a__					)					)
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= {
             '''facecolor''': '''black''',
             '''alpha''': self.alpha,
             '''pad''': self.pad,
             '''edgecolor''': '''none''',
         }
         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= position
         self.ax.text(
             a__ ,  a__ ,  a__ ,  size=font_size * self.scale ,  family='''sans-serif''' ,  bbox=a__ ,  verticalalignment='''top''' ,  horizontalalignment=a__ ,  color=a__ ,  zorder=1_0 ,  rotation=a__ ,  )






      def    A__(self ,  a__=None					)       ->   Union[str, Any]:
         if saveas is None:
            SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.saveas
         if saveas.lower().endswith('''.jpg'''					) or saveas.lower().endswith('''.png'''					):
            cva.imwrite(
                a__ ,  self._get_buffer()[:, :, ::-1] ,  )
         else:
            self.fig.savefig(a__					)






      def    A__(self ,  a__ ,  a__ ,  a__ ,  a__					)       ->   Optional[int]:
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= [self.idaobj[i] for i in classes]
         SCREAMING_SNAKE_CASE__			:				str       						= [self.idaattr[i] for i in attr_classes]
         SCREAMING_SNAKE_CASE__			:				Any       						= [
             f"""{label} {score:.2f} {attr} {attr_score:.2f}"""
             for label, score, attr, attr_score in zip(a__ ,  a__ ,  a__ ,  a__					)
         ]
         return labels






      def    A__(self ,  a__ ,  a__					)       ->   Optional[int]:
         SCREAMING_SNAKE_CASE__			:				Dict       						= [self.idaobj[i] for i in classes]
         if scores is not None:
            if labels is None:
               SCREAMING_SNAKE_CASE__			:				int       						= ['''{:.0f}%'''.format(s * 1_0_0					) for s in scores]
            else:
               SCREAMING_SNAKE_CASE__			:				str       						= ['''{} {:.0f}%'''.format(a__ ,  s * 1_0_0					) for li, s in zip(a__ ,  a__					)]
         return labels






      def    A__(self ,  a__=2_5_5					)       ->   str:
         SCREAMING_SNAKE_CASE__			:				str       						= np.random.randint(0 ,  len(_COLORS					)					)
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= _COLORS[idx] * maximum
         if not self.rgb:
            SCREAMING_SNAKE_CASE__			:				Tuple       						= ret[::-1]
         return ret






      def    A__(self					)       ->   Union[str, Any]:
         if not self.pynb:
            SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self.canvas.print_to_buffer()
            if (width, height) != (self.width, self.height):
               SCREAMING_SNAKE_CASE__			:				List[str]       						= cva.resize(self.img ,  (width, height)					)
            else:
               SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.img
         else:
            SCREAMING_SNAKE_CASE__			:				Optional[int]       						= io.BytesIO()  # works for cairo backend
            self.canvas.print_rgba(a__					)
            SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self.width, self.height
            SCREAMING_SNAKE_CASE__			:				Dict       						= buf.getvalue()
            SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self.img

         SCREAMING_SNAKE_CASE__			:				str       						= np.frombuffer(a__ ,  dtype='''uint8'''					)
         SCREAMING_SNAKE_CASE__			:				int       						= buffer.reshape(a__ ,  a__ ,  4					)
         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Any       						= np.split(a__ ,  [3] ,  axis=2					)

         try:
            import numexpr as ne  # fuse them with numexpr

            SCREAMING_SNAKE_CASE__			:				Dict       						= ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''					)
         except ImportError:
            SCREAMING_SNAKE_CASE__			:				int       						= alpha.astype('''float32'''					) / 2_55.0
            SCREAMING_SNAKE_CASE__			:				Optional[int]       						= img * (1 - alpha) + rgb * alpha

         return visualized_image.astype('''uint8'''					)






      def    A__(self ,  a__ ,  a__					)       ->   Tuple:
         assert brightness_factor >= -1.0 and brightness_factor <= 1.0
         SCREAMING_SNAKE_CASE__			:				List[str]       						= mplc.to_rgb(a__					)
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= colorsys.rgb_to_hls(*mplc.to_rgb(a__					)					)
         SCREAMING_SNAKE_CASE__			:				List[str]       						= polygon_color[1] + (brightness_factor * polygon_color[1])
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= 0.0 if modified_lightness < 0.0 else modified_lightness
         SCREAMING_SNAKE_CASE__			:				Dict       						= 1.0 if modified_lightness > 1.0 else modified_lightness
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= colorsys.hls_to_rgb(polygon_color[0] ,  a__ ,  polygon_color[2]					)
         return modified_color

lowercase__			=	(
    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)
)







'''simple docstring'''





import os
import re
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__			=	logging.get_logger(__name__)
lowercase__			=	{'''vocab_file''': '''spiece.model'''}

lowercase__			=	{
    '''vocab_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
    },
}

lowercase__			=	{
    '''google/fnet-base''': 5_1_2,
    '''google/fnet-large''': 5_1_2,
}

lowercase__			=	'''▁'''






class 						lowercase     (   UpperCAmelCase_):
      __lowercase 		=  VOCAB_FILES_NAMES
      __lowercase 		=  PRETRAINED_VOCAB_FILES_MAP
      __lowercase 		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __lowercase 		=  ['''input_ids''', '''token_type_ids''']






      def __init__(self ,  a__ ,  a__=False ,  a__=True ,  a__=True ,  a__="<unk>" ,  a__="[SEP]" ,  a__="<pad>" ,  a__="[CLS]" ,  a__="[MASK]" ,  a__ = None ,  **a__ ,  )       ->   None:
         # Mask token behave like a normal word, i.e. include the space before it and
         # is included in the raw text, there should be a match in a non-normalized sentence.
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= (
             AddedToken(a__ ,  lstrip=a__ ,  rstrip=a__ ,  normalized=a__					)
             if isinstance(a__ ,  a__					)
             else mask_token
         )

         SCREAMING_SNAKE_CASE__			:				Any       						= {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             do_lower_case=a__ ,  remove_space=a__ ,  keep_accents=a__ ,  unk_token=a__ ,  sep_token=a__ ,  pad_token=a__ ,  cls_token=a__ ,  mask_token=a__ ,  sp_model_kwargs=self.sp_model_kwargs ,  **a__ ,  )

         SCREAMING_SNAKE_CASE__			:				Dict       						= do_lower_case
         SCREAMING_SNAKE_CASE__			:				Tuple       						= remove_space
         SCREAMING_SNAKE_CASE__			:				List[str]       						= keep_accents
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= vocab_file

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
         self.sp_model.Load(a__					)






      @property
      def    A__(self					)       ->   Union[str, Any]:
         return len(self.sp_model					)






      def    A__(self					)       ->   Any:
         SCREAMING_SNAKE_CASE__			:				int       						= {self.convert_ids_to_tokens(a__					): i for i in range(self.vocab_size					)}
         vocab.update(self.added_tokens_encoder					)
         return vocab






      def __getstate__(self					)       ->   Any:
         SCREAMING_SNAKE_CASE__			:				List[str]       						= self.__dict__.copy()
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= None
         return state






      def __setstate__(self ,  a__					)       ->   int:
         SCREAMING_SNAKE_CASE__			:				Any       						= d

         # for backward compatibility
         if not hasattr(self ,  '''sp_model_kwargs'''					):
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= {}

         SCREAMING_SNAKE_CASE__			:				int       						= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
         self.sp_model.Load(self.vocab_file					)






      def    A__(self ,  a__					)       ->   str:
         if self.remove_space:
            SCREAMING_SNAKE_CASE__			:				List[str]       						= ''' '''.join(inputs.strip().split()					)
         else:
            SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= inputs
         SCREAMING_SNAKE_CASE__			:				Dict       						= outputs.replace('''``''' ,  '''"'''					).replace('''\'\'''' ,  '''"'''					)

         if not self.keep_accents:
            SCREAMING_SNAKE_CASE__			:				Dict       						= unicodedata.normalize('''NFKD''' ,  a__					)
            SCREAMING_SNAKE_CASE__			:				List[Any]       						= ''''''.join([c for c in outputs if not unicodedata.combining(a__					)]					)
         if self.do_lower_case:
            SCREAMING_SNAKE_CASE__			:				str       						= outputs.lower()

         return outputs






      def    A__(self ,  a__					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				Dict       						= self.preprocess_text(a__					)
         SCREAMING_SNAKE_CASE__			:				List[str]       						= self.sp_model.encode(a__ ,  out_type=a__					)
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= []
         for piece in pieces:
            if len(a__					) > 1 and piece[-1] == str(''','''					) and piece[-2].isdigit():
               SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.sp_model.EncodeAsPieces(piece[:-1].replace(a__ ,  ''''''					)					)
               if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                  if len(cur_pieces[0]					) == 1:
                     SCREAMING_SNAKE_CASE__			:				List[str]       						= cur_pieces[1:]
                  else:
                     SCREAMING_SNAKE_CASE__			:				str       						= cur_pieces[0][1:]
               cur_pieces.append(piece[-1]					)
               new_pieces.extend(a__					)
            else:
               new_pieces.append(a__					)

         return new_pieces






      def    A__(self ,  a__					)       ->   str:
         return self.sp_model.PieceToId(a__					)






      def    A__(self ,  a__					)       ->   Optional[int]:
         return self.sp_model.IdToPiece(a__					)






      def    A__(self ,  a__					)       ->   Optional[Any]:
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= []
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= ''''''
         SCREAMING_SNAKE_CASE__			:				Any       						= False
         for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
               if not prev_is_special:
                  out_string += " "
               out_string += self.sp_model.decode(a__					) + token
               SCREAMING_SNAKE_CASE__			:				List[Any]       						= True
               SCREAMING_SNAKE_CASE__			:				int       						= []
            else:
               current_sub_tokens.append(a__					)
               SCREAMING_SNAKE_CASE__			:				Dict       						= False
         out_string += self.sp_model.decode(a__					)
         return out_string.strip()






      def    A__(self ,  a__ ,  a__ = False ,  a__ = None ,  a__ = True ,  **a__ ,  )       ->   str:
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= kwargs.pop('''use_source_tokenizer''' ,  a__					)

         SCREAMING_SNAKE_CASE__			:				int       						= self.convert_ids_to_tokens(a__ ,  skip_special_tokens=a__					)

         # To avoid mixing byte-level and unicode for byte-level BPT
         # we need to build string separately for added tokens and byte-level tokens
         # cf. https://github.com/huggingface/transformers/issues/1133
         SCREAMING_SNAKE_CASE__			:				Dict       						= []
         SCREAMING_SNAKE_CASE__			:				List[str]       						= []
         for token in filtered_tokens:
            if skip_special_tokens and token in self.all_special_ids:
               continue
            if token in self.added_tokens_encoder:
               if current_sub_text:
                  sub_texts.append(self.convert_tokens_to_string(a__					)					)
                  SCREAMING_SNAKE_CASE__			:				List[str]       						= []
               sub_texts.append(a__					)
            else:
               current_sub_text.append(a__					)
         if current_sub_text:
            sub_texts.append(self.convert_tokens_to_string(a__					)					)

         # Mimic the behavior of the Rust tokenizer:
         # No space after <unk>
         if spaces_between_special_tokens:
            SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= re.sub(R'''(<unk>) ''' ,  R'''\1''' ,  ''' '''.join(a__					)					)
         else:
            SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= ''''''.join(a__					)

         SCREAMING_SNAKE_CASE__			:				int       						= (
             clean_up_tokenization_spaces
             if clean_up_tokenization_spaces is not None
             else self.clean_up_tokenization_spaces
         )
         if clean_up_tokenization_spaces:
            SCREAMING_SNAKE_CASE__			:				str       						= self.clean_up_tokenization(a__					)
            return clean_text
         else:
            return text






      def    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
         SCREAMING_SNAKE_CASE__			:				str       						= [self.sep_token_id]
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= [self.cls_token_id]
         if token_ids_a is None:
            return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep






      def    A__(self ,  a__ ,  a__ = None ,  a__ = False					)       ->   List[int]:

         if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=a__ ,  token_ids_a=a__ ,  already_has_special_tokens=a__					)

         if token_ids_a is not None:
            return [1] + ([0] * len(a__					)) + [1] + ([0] * len(a__					)) + [1]
         return [1] + ([0] * len(a__					)) + [1]






      def    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
         SCREAMING_SNAKE_CASE__			:				List[str]       						= [self.sep_token_id]
         SCREAMING_SNAKE_CASE__			:				Tuple       						= [self.cls_token_id]

         if token_ids_a is None:
            return len(cls + token_ids_a + sep					) * [0]
         return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






      def    A__(self ,  a__ ,  a__ = None					)       ->   Tuple[str]:
         if not os.path.isdir(a__					):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
            return
         SCREAMING_SNAKE_CASE__			:				str       						= os.path.join(
             a__ ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

         if os.path.abspath(self.vocab_file					) != os.path.abspath(a__					) and os.path.isfile(self.vocab_file					):
            copyfile(self.vocab_file ,  a__					)
         elif not os.path.isfile(self.vocab_file					):
            with open(a__ ,  '''wb'''					) as fi:
               SCREAMING_SNAKE_CASE__			:				int       						= self.sp_model.serialized_model_proto()
               fi.write(a__					)

         return (out_vocab_file,)

