


'''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


A   :  Any       		=				1000


class 				__lowerCamelCase				:
    """simple docstring"""


    def __init__(						self    :							Dict   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							List[Any]=1.2   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]="g"   ,							SCREAMING_SNAKE_CASE    :							List[str]=0.5   ,							SCREAMING_SNAKE_CASE    :							Optional[int]="-"   ,							SCREAMING_SNAKE_CASE    :							Dict="test_out.jpg"   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=True   ,							SCREAMING_SNAKE_CASE    :							Any=False   ,							SCREAMING_SNAKE_CASE    :							int=None   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=None   ,							SCREAMING_SNAKE_CASE    :							Dict=0.7   ,							):
           if isinstance(SCREAMING_SNAKE_CASE   ,							torch.Tensor):
                  _A  :			Optional[int]											=      img.numpy().astype('np.uint8')
           if isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE):
                  _A  :			List[str]											=      img_tensorize(SCREAMING_SNAKE_CASE)
           assert isinstance(SCREAMING_SNAKE_CASE   ,							np.ndarray)

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

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


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							Any   ,							SCREAMING_SNAKE_CASE    :							int=None):
           if color is None:
                  _A  :			Union[str, Any]											=      self.edgecolor
           ((_A)						,				(_A)						,				(_A)						,				(_A))  :			int											=      box
           _A  :			List[Any]											=      xa - xa
           _A  :			List[str]											=      ya - ya
           self.ax.add_patch(
               mpl.patches.Rectangle(
                   (xa, ya)   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							fill=SCREAMING_SNAKE_CASE   ,							edgecolor=SCREAMING_SNAKE_CASE   ,							linewidth=self.font_size // 3   ,							alpha=self.alpha   ,							linestyle=self.linestyle   ,							))


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

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


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

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

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


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							str   ,							SCREAMING_SNAKE_CASE    :							Tuple="g"   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]="left"   ,							):
           _A  :			int											=      0
           _A  :			int											=      self.font_size
           _A  :			str											=      np.maximum(list(mplc.to_rgb(SCREAMING_SNAKE_CASE))   ,							0.2)
           _A  :			Dict											=      max(0.8   ,							np.max(SCREAMING_SNAKE_CASE))
           _A  :			str											=      {
               'facecolor': 'black',
               'alpha': self.alpha,
               'pad': self.pad,
               'edgecolor': 'none',
           }
           _A						,				_A  :			Union[str, Any]											=      position
           self.ax.text(
               SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							size=font_size * self.scale   ,							family='sans-serif'   ,							bbox=SCREAMING_SNAKE_CASE   ,							verticalalignment='top'   ,							horizontalalignment=SCREAMING_SNAKE_CASE   ,							color=SCREAMING_SNAKE_CASE   ,							zorder=10   ,							rotation=SCREAMING_SNAKE_CASE   ,							)


    def      A					(						self    :							Dict   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=None):
           if saveas is None:
                  _A  :			Tuple											=      self.saveas
           if saveas.lower().endswith('.jpg') or saveas.lower().endswith('.png'):
                  cva.imwrite(
                      SCREAMING_SNAKE_CASE   ,							self._get_buffer()[:, :, ::-1]   ,							)
           else:
                  self.fig.savefig(SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							int):
           _A  :			Optional[int]											=      [self.idaobj[i] for i in classes]
           _A  :			Dict											=      [self.idaattr[i] for i in attr_classes]
           _A  :			Any											=      [
               F'{label} {score:.2f} {attr} {attr_score:.2f}'
               for label, score, attr, attr_score in zip(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)
           ]
           return labels


    def      A					(						self    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Dict   ,							SCREAMING_SNAKE_CASE    :							Any):
           _A  :			Union[str, Any]											=      [self.idaobj[i] for i in classes]
           if scores is not None:
                  if labels is None:
                         _A  :			int											=      ['{:.0f}%'.format(s * 100) for s in scores]
                  else:
                         _A  :			int											=      ['{} {:.0f}%'.format(SCREAMING_SNAKE_CASE   ,							s * 100) for li, s in zip(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)]
           return labels


    def      A					(						self    :							Any   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]=255):
           _A  :			Optional[Any]											=      np.random.randint(0   ,							len(_COLORS))
           _A  :			int											=      _COLORS[idx] * maximum
           if not self.rgb:
                  _A  :			Optional[int]											=      ret[::-1]
           return ret


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

           _A  :			int											=      np.frombuffer(SCREAMING_SNAKE_CASE   ,							dtype='uint8')
           _A  :			List[str]											=      buffer.reshape(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							4)
           _A						,				_A  :			Optional[int]											=      np.split(SCREAMING_SNAKE_CASE   ,							[3]   ,							axis=2)

           try:
                  import numexpr as ne  # fuse them with numexpr

                  _A  :			Tuple											=      ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)')
           except ImportError:
                  _A  :			Tuple											=      alpha.astype('float32') / 255.0
                  _A  :			str											=      img * (1 - alpha) + rgb * alpha

           return visualized_image.astype('uint8')


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




A   :  Tuple       		=				(
    np.array(
        [
            0.0_00,
            0.4_47,
            0.7_41,
            0.8_50,
            0.3_25,
            0.0_98,
            0.9_29,
            0.6_94,
            0.1_25,
            0.4_94,
            0.1_84,
            0.5_56,
            0.4_66,
            0.6_74,
            0.1_88,
            0.3_01,
            0.7_45,
            0.9_33,
            0.6_35,
            0.0_78,
            0.1_84,
            0.3_00,
            0.3_00,
            0.3_00,
            0.6_00,
            0.6_00,
            0.6_00,
            1.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.7_49,
            0.7_49,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            0.0_00,
            0.3_33,
            0.6_67,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            0.3_33,
            0.0_00,
            0.6_67,
            0.6_67,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.3_33,
            0.5_00,
            0.0_00,
            0.6_67,
            0.5_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.3_33,
            0.0_00,
            0.5_00,
            0.3_33,
            0.3_33,
            0.5_00,
            0.3_33,
            0.6_67,
            0.5_00,
            0.3_33,
            1.0_00,
            0.5_00,
            0.6_67,
            0.0_00,
            0.5_00,
            0.6_67,
            0.3_33,
            0.5_00,
            0.6_67,
            0.6_67,
            0.5_00,
            0.6_67,
            1.0_00,
            0.5_00,
            1.0_00,
            0.0_00,
            0.5_00,
            1.0_00,
            0.3_33,
            0.5_00,
            1.0_00,
            0.6_67,
            0.5_00,
            1.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            1.0_00,
            0.3_33,
            0.6_67,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.6_67,
            0.3_33,
            1.0_00,
            0.6_67,
            0.6_67,
            1.0_00,
            0.6_67,
            1.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            1.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_43,
            0.1_43,
            0.1_43,
            0.8_57,
            0.8_57,
            0.8_57,
            1.0_00,
            1.0_00,
            1.0_00,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)










'''simple docstring'''







import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


A   :  List[Any]       		=				{0: '''null''', 1: '''short''', 2: '''long''', 3: '''yes''', 4: '''no'''}
A   :  Optional[int]       		=				set(''''''.join(['''‘''', '''’''', '''´''', '''`''', '''.''', ''',''', '''-''', '''"''']))






def    lowerCAmelCase__ (       lowerCamelCase  :    str						,lowerCamelCase  :    Dict=0						,lowerCamelCase  :    List[Any]=None     ):
       return [" ".join(x.split(' '     )[begin:end]     ) for x in answers if len(x.split(' '     )     ) > 1]






def    lowerCAmelCase__ (       lowerCamelCase  :    Dict						,lowerCamelCase  :    Tuple=False     ):
       if make_sub_answers:
              # if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
              # *e.g.* if the correct answer contains a prefix such as "the", or "a"
              _A  :			str											=      (
                  given_answers + get_sub_answers(lowerCamelCase						,begin=1     ) + get_sub_answers(lowerCamelCase						,end=-1     )
              )
       _A  :			List[Any]											=      []
       for answer in given_answers:
              _A  :			List[str]											=      answer.replace('_'						,' '     ).lower()
              _A  :			Any											=      ''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ' ' for c in alias     )
              answers.append(' '.join(alias.split()     ).strip()     )
       return set(lowerCamelCase     )






def    lowerCAmelCase__ (       lowerCamelCase  :    List[Any]						,lowerCamelCase  :    Optional[int]						,lowerCamelCase  :    List[str]=1						,lowerCamelCase  :    Optional[int]=100     ):
       _A						,				_A  :			Dict											=      jax.lax.top_k(lowerCamelCase						,lowerCamelCase     )
       _A						,				_A  :			Dict											=      jax.lax.top_k(lowerCamelCase						,lowerCamelCase     )

       _A  :			List[str]											=      best_end_idx[:, None] - best_start_idx[None, :]
       _A  :			Any											=      jnp.logical_or(widths < 0						,widths > max_size     )
       _A  :			Any											=      (best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
       _A  :			List[Any]											=      jnp.argmax(lowerCamelCase     ).item()

       return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]






def    lowerCAmelCase__ (       lowerCamelCase  :    Dict     ):
       _A  :			Optional[Any]											=      sample['question']['text']
       _A  :			Any											=      sample['document']['tokens']['token']
       _A  :			List[str]											=      sample['document']['tokens']['is_html']
       _A  :			str											=      sample['annotations']['long_answer']
       _A  :			int											=      sample['annotations']['short_answers']

       _A  :			Optional[int]											=      ' '.join([context[i] for i in range(len(lowerCamelCase     )     ) if not is_html[i]]     )

       # 0 - No ; 1 - Yes
       for answer in sample["annotations"]["yes_no_answer"]:
              if answer == 0 or answer == 1:
                     return {
                         "question": question,
                         "context": context_string,
                         "short": [],
                         "long": [],
                         "category": "no" if answer == 0 else "yes",
                     }

       _A  :			Tuple											=      []
       for s in short_answers:
              short_targets.extend(s['text']     )
       _A  :			Dict											=      list(set(lowerCamelCase     )     )

       _A  :			List[str]											=      []
       for s in long_answers:
              if s["start_token"] == -1:
                     continue
              _A  :			Tuple											=      context[s['start_token'] : s['end_token']]
              _A  :			Optional[Any]											=      is_html[s['start_token'] : s['end_token']]
              _A  :			Optional[int]											=      ' '.join([answer[i] for i in range(len(lowerCamelCase     )     ) if not html[i]]     )
              if new_answer not in long_targets:
                     long_targets.append(lowerCamelCase     )

       _A  :			List[str]											=      'long_short' if len(short_targets + long_targets     ) > 0 else 'null'

       return {
           "question": question,
           "context": context_string,
           "short": short_targets,
           "long": long_targets,
           "category": category,
       }






def    lowerCAmelCase__ (       ):
       _A  :			Optional[Any]											=      load_from_disk('natural-questions-validation'     )
       _A  :			int											=      dataset.map(lowerCamelCase     ).remove_columns(['annotations', 'document', 'id']     )
       print(lowerCamelCase     )

       _A  :			int											=      dataset.filter(lambda lowerCamelCase     : (len(x['question']     ) + len(x['context']     )) < 4 * 4096     )
       _A  :			Dict											=      short_validation_dataset.filter(lambda lowerCamelCase     : x["category"] != "null"     )
       short_validation_dataset

       _A  :			Tuple											=      'vasudevgupta/flax-bigbird-natural-questions'
       _A  :			List[str]											=      FlaxBigBirdForNaturalQuestions.from_pretrained(lowerCamelCase     )
       _A  :			str											=      BigBirdTokenizerFast.from_pretrained(lowerCamelCase     )

       @jax.jit
       def forward(*lowerCamelCase  :    Optional[Any]						,**lowerCamelCase  :    Any     ):
              _A						,				_A						,				_A  :			Tuple											=      model(*lowerCamelCase						,**lowerCamelCase     )
              return start_logits, end_logits, jnp.argmax(lowerCamelCase						,axis=-1     )

       def evaluate(lowerCamelCase  :    Union[str, Any]     ):
              # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
              _A  :			List[str]											=      tokenizer(
                  example['question']						,example['context']						,return_tensors='np'						,max_length=4096						,padding='max_length'						,truncation=lowerCamelCase						,)

              _A						,				_A						,				_A  :			Tuple											=      forward(**lowerCamelCase     )

              _A  :			List[Any]											=      CATEGORY_MAPPING[category.item()]

              _A  :			List[str]											=      example['long'] + example['short']
              if example["category"] in ["yes", "no", "null"]:
                     _A  :			Optional[int]											=      [example['category']]
              _A  :			List[Any]											=      example['category'] != 'null'
              # Now target can be: "yes", "no", "null", "list of long & short answers"

              if predicted_category in ["yes", "no", "null"]:
                     _A  :			Tuple											=      [predicted_category]
                     _A  :			Union[str, Any]											=      example['output'] == example['targets']
                     _A  :			Tuple											=      predicted_category != 'null'
                     return example

              _A  :			Dict											=      38 if predicted_category == 'short' else 1024
              _A						,				_A  :			List[Any]											=      get_best_valid_start_end_idx(
                  start_scores[0]						,end_scores[0]						,top_k=8						,max_size=lowerCamelCase     )

              _A  :			Optional[Any]											=      inputs['input_ids'][0].tolist()
              _A  :			Union[str, Any]											=      [tokenizer.decode(input_ids[start_score : end_score + 1]     )]

              _A  :			Union[str, Any]											=      expand_to_aliases(example['targets']						,make_sub_answers=lowerCamelCase     )
              _A  :			str											=      expand_to_aliases(example['output']     )

              # some preprocessing to both prediction and answer
              _A  :			Tuple											=      {''.join(a.split()     ) for a in answers}
              _A  :			Optional[Any]											=      {''.join(p.split()     ) for p in predictions}
              _A  :			Tuple											=      {s for s in predictions if s not in ['``', '\'\'', '`', '\'']}

              # if there is a common element, it's a exact match
              _A  :			Tuple											=      len(list(answers & predictions     )     ) > 0
              _A  :			Any											=      predicted_category != 'null' and len(lowerCamelCase     ) > 0

              return example

       _A  :			str											=      short_validation_dataset.map(lowerCamelCase     )

       _A  :			Optional[int]											=      len(lowerCamelCase     )
       _A  :			str											=      len(short_validation_dataset.filter(lambda lowerCamelCase     : x["match"] == 1     )     )
       print('EM score:'						,(matched / total) * 100						,'%'     )


if __name__ == "__main__":
   main()






