




'''simple docstring'''


from __future__ import annotations

from collections.abc import Iterable






class 	lowerCAmelCase_  :







  '''simple docstring'''

  def __init__(   self							:       Dict				,     SCREAMING_SNAKE_CASE_							:       str				,     SCREAMING_SNAKE_CASE_							:       int  ) ->		Optional[Any]:







       '''simple docstring'''



       A    ,						A:		List[Any]        =		float(SCREAMING_SNAKE_CASE_  ), float(SCREAMING_SNAKE_CASE_  )



  def __eq__(   self							:       List[str]				,     SCREAMING_SNAKE_CASE_							:       Any  ) ->		str:







       '''simple docstring'''



       return self.x == other.x and self.y == other.y



  def __ne__(   self							:       int				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]  ) ->		str:







       '''simple docstring'''



       return not self == other



  def __gt__(   self							:       Optional[Any]				,     SCREAMING_SNAKE_CASE_							:       Dict  ) ->		List[str]:







       '''simple docstring'''



       if self.x > other.x:
            return True
       elif self.x == other.x:
            return self.y > other.y
       return False



  def __lt__(   self							:       Union[str, Any]				,     SCREAMING_SNAKE_CASE_							:       int  ) ->		List[str]:







       '''simple docstring'''



       return not self > other



  def __ge__(   self							:       Any				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]  ) ->		int:







       '''simple docstring'''



       if self.x > other.x:
            return True
       elif self.x == other.x:
            return self.y >= other.y
       return False



  def __le__(   self							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       str  ) ->		int:







       '''simple docstring'''



       if self.x < other.x:
            return True
       elif self.x == other.x:
            return self.y <= other.y
       return False



  def __repr__(   self							:       Optional[int]  ) ->		Tuple:







       '''simple docstring'''



       return f"""({self.x}, {self.y})"""




  def __hash__(   self							:       Tuple  ) ->		List[Any]:







       '''simple docstring'''



       return hash(self.x  )






def 				SCREAMING_SNAKE_CASE(      __lowercase		,							) ->   list[Point]:
     A:		list[Point]        =		[]
     if list_of_tuples:
          for p in list_of_tuples:
               if isinstance(__lowercase		,							__lowercase  ):
                    points.append(__lowercase  )
               else:
                    try:
                         points.append(Point(p[0]		,							p[1]  )  )
                    except (IndexError, TypeError):
                         print(
                             F"""Ignoring deformed point {p}. All points"""
                             ''' must have at least 2 coordinates.'''  )
     return points
def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   list[Point]:

     if not hasattr(__lowercase		,							'''__iter__'''  ):
          A:		Union[str, Any]        =		F"""Expecting an iterable object but got an non-iterable type {points}"""
          raise ValueError(__lowercase  )

     if not points:
          A:		str        =		F"""Expecting a list of points but got {points}"""
          raise ValueError(__lowercase  )

     return _construct_points(__lowercase  )
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase		,							__lowercase  ) ->   float:
     A:		Any        =		(a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
     return det
def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   list[Point]:
     A:		Any        =		sorted(_validate_input(__lowercase  )  )
     A:		Tuple        =		len(__lowercase  )
     A:		int        =		set()

     for i in range(n - 1  ):
          for j in range(i + 1		,							__lowercase  ):
               A:		str        =		False
               A:		Any        =		True
               for k in range(__lowercase  ):
                    if k != i and k != j:
                         A:		Any        =		_det(points[i]		,							points[j]		,							points[k]  )

                         if det_k > 0:
                              A:		int        =		True
                         elif det_k < 0:
                              A:		List[str]        =		True
                         else:
                              # point[i], point[j], point[k] all lie on a straight line
                              # if point[k] is to the left of point[i] or it's to the
                              # right of point[j], then point[i], point[j] cannot be
                              # part of the convex hull of A
                              if points[k] < points[i] or points[k] > points[j]:
                                   A:		List[Any]        =		False
                                   break

                    if points_left_of_ij and points_right_of_ij:
                         A:		Dict        =		False
                         break

               if ij_part_of_convex_hull:
                    convex_set.update([points[i], points[j]]  )

     return sorted(__lowercase  )
def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   list[Point]:
     A:		Tuple        =		sorted(_validate_input(__lowercase  )  )
     A:		Any        =		len(__lowercase  )

     # divide all the points into an upper hull and a lower hull
     # the left most point and the right most point are definitely
     # members of the convex hull by definition.
     # use these two anchors to divide all the points into two hulls,
     # an upper hull and a lower hull.

     # all points to the left (above) the line joining the extreme points belong to the
     # upper hull
     # all points to the right (below) the line joining the extreme points below to the
     # lower hull
     # ignore all points on the line joining the extreme points since they cannot be
     # part of the convex hull

     A:		Tuple        =		points[0]
     A:		Optional[Any]        =		points[n - 1]

     A:		Tuple        =		{left_most_point, right_most_point}
     A:		str        =		[]
     A:		Union[str, Any]        =		[]

     for i in range(1		,							n - 1  ):
          A:		str        =		_det(__lowercase		,							__lowercase		,							points[i]  )

          if det > 0:
               upper_hull.append(points[i]  )
          elif det < 0:
               lower_hull.append(points[i]  )

     _construct_hull(__lowercase		,							__lowercase		,							__lowercase		,							__lowercase  )
     _construct_hull(__lowercase		,							__lowercase		,							__lowercase		,							__lowercase  )

     return sorted(__lowercase  )
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase		,							__lowercase		,							__lowercase  ) ->   None:
     if points:
          A:		List[str]        =		None
          A:		int        =		float('''-inf'''  )
          A:		Optional[int]        =		[]

          for p in points:
               A:		Tuple        =		_det(__lowercase		,							__lowercase		,							__lowercase  )

               if det > 0:
                    candidate_points.append(__lowercase  )

                    if det > extreme_point_distance:
                         A:		Tuple        =		det
                         A:		List[str]        =		p

          if extreme_point:
               _construct_hull(__lowercase		,							__lowercase		,							__lowercase		,							__lowercase  )
               convex_set.add(__lowercase  )
               _construct_hull(__lowercase		,							__lowercase		,							__lowercase		,							__lowercase  )
def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   list[Point]:
     A:		str        =		sorted(_validate_input(__lowercase  )  )
     A:		str        =		len(__lowercase  )

     A:		Dict        =		points[:2]
     for i in range(2		,							__lowercase  ):
          A:		List[Any]        =		_det(convex_hull[1]		,							convex_hull[0]		,							points[i]  )
          if det > 0:
               convex_hull.insert(0		,							points[i]  )
               break
          elif det < 0:
               convex_hull.append(points[i]  )
               break
          else:
               A:		List[Any]        =		points[i]
     i += 1

     for j in range(__lowercase		,							__lowercase  ):
          if (
              _det(convex_hull[0]		,							convex_hull[-1]		,							points[j]  ) > 0
              and _det(convex_hull[-1]		,							convex_hull[0]		,							points[1]  ) < 0
          ):
               # The point lies within the convex hull
               continue

          convex_hull.insert(0		,							points[j]  )
          convex_hull.append(points[j]  )
          while _det(convex_hull[0]		,							convex_hull[1]		,							convex_hull[2]  ) >= 0:
               del convex_hull[1]
          while _det(convex_hull[-1]		,							convex_hull[-2]		,							convex_hull[-3]  ) <= 0:
               del convex_hull[-2]

    # `convex_hull` is contains the convex hull in circular order
     return sorted(convex_hull[1:] if len(__lowercase  ) > 3 else convex_hull  )
def 				SCREAMING_SNAKE_CASE(      ) ->   Optional[int]:
     A:		Any        =		[
         (0, 3),
         (2, 2),
         (1, 1),
         (2, 1),
         (3, 0),
         (0, 0),
         (3, 3),
         (2, -1),
         (2, -4),
         (1, -3),
     ]
     # the convex set of points is
     # [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
     A:		List[Any]        =		convex_hull_bf(__lowercase  )

     A:		Dict        =		convex_hull_recursive(__lowercase  )
     assert results_bf == results_recursive

     A:		Union[str, Any]        =		convex_hull_melkman(__lowercase  )
     assert results_bf == results_melkman

     print(__lowercase  )


if __name__ == "__main__":
     main()












'''simple docstring'''


import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
     import torch

     from ..models.auto.modeling_auto import (
         MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
     )

if is_tf_available() and is_tensorflow_probability_available():
     import tensorflow as tf
     import tensorflow_probability as tfp

     from ..models.auto.modeling_tf_auto import (
         TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
     )






class 	lowerCAmelCase_  (					UpperCAmelCase_       ):







  '''simple docstring'''

  def __call__(   self							:       str				,     SCREAMING_SNAKE_CASE_							:       Any=None				,     SCREAMING_SNAKE_CASE_							:       Tuple=None				,     **SCREAMING_SNAKE_CASE_							:       Optional[int]  ) ->		Optional[int]:







       '''simple docstring'''



       requires_backends(self				,     '''pandas'''  )
       import pandas as pd

       if table is None:
            raise ValueError('''Keyword argument `table` cannot be None.'''  )
       elif query is None:
            if isinstance(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  ) and table.get('''query'''  ) is not None and table.get('''table'''  ) is not None:
                 A:		List[Any]        =		[table]
            elif isinstance(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  ) and len(SCREAMING_SNAKE_CASE_  ) > 0:
                 if not all(isinstance(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  ) for d in table  ):
                      raise ValueError(
                          f"""Keyword argument `table` should be a list of dict, but is {(type(SCREAMING_SNAKE_CASE_  ) for d in table)}"""  )

                 if table[0].get('''query'''  ) is not None and table[0].get('''table'''  ) is not None:
                      A:		Optional[Any]        =		table
                 else:
                      raise ValueError(
                          '''If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`'''
                          f""" and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys."""  )
            elif Dataset is not None and isinstance(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  ) or isinstance(SCREAMING_SNAKE_CASE_				,     types.GeneratorType  ):
                 return table
            else:
                 raise ValueError(
                     '''Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but '''
                     f"""is {type(SCREAMING_SNAKE_CASE_  )})"""  )
       else:
            A:		List[str]        =		[{'''table''': table, '''query''': query}]

       for tqa_pipeline_input in tqa_pipeline_inputs:
            if not isinstance(tqa_pipeline_input['''table''']				,     pd.DataFrame  ):
                 if tqa_pipeline_input["table"] is None:
                      raise ValueError('''Table cannot be None.'''  )

                 A:		str        =		pd.DataFrame(tqa_pipeline_input['''table''']  )

       return tqa_pipeline_inputs






@add_end_docstrings(UpperCAmelCase_       )
class 	lowerCAmelCase_  (					UpperCAmelCase_       ):







  '''simple docstring'''

  UpperCamelCase_ :			List[str]        =							"""table,query"""



  def __init__(   self							:       List[str]				,     SCREAMING_SNAKE_CASE_							:       Dict=TableQuestionAnsweringArgumentHandler()				,     *SCREAMING_SNAKE_CASE_							:       Any				,     **SCREAMING_SNAKE_CASE_							:       str  ) ->		Dict:







       '''simple docstring'''



       super().__init__(*SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_  )
       A:		Tuple        =		args_parser

       self.check_model_type(
           dict(
               TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
               + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()  )
           if self.framework == '''tf'''
           else dict(
               MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()  )  )

       A:		Optional[int]        =		bool(getattr(self.model.config				,     '''aggregation_labels'''				,     SCREAMING_SNAKE_CASE_  )  ) and bool(
           getattr(self.model.config				,     '''num_aggregation_labels'''				,     SCREAMING_SNAKE_CASE_  )  )
       A:		Dict        =		'''tapas''' if hasattr(self.model.config				,     '''aggregation_labels'''  ) else None



  def 				_snake_case    (   self							:       Any				,     **SCREAMING_SNAKE_CASE_							:       List[Any]  ) ->		Any:







       '''simple docstring'''



       return self.model(**SCREAMING_SNAKE_CASE_  )



  def 				_snake_case    (   self							:       str				,     **SCREAMING_SNAKE_CASE_							:       List[Any]  ) ->		Any:







       '''simple docstring'''



       if self.framework == "pt":
            A:		Optional[Any]        =		[]
            A:		Any        =		[]
            A:		List[str]        =		None
            A:		List[Any]        =		inputs['''input_ids'''].shape[0]

            A:		int        =		inputs['''input_ids'''].to(self.device  )
            A:		int        =		inputs['''attention_mask'''].to(self.device  )
            A:		List[str]        =		inputs['''token_type_ids'''].to(self.device  )
            A:		List[str]        =		None

            for index in range(SCREAMING_SNAKE_CASE_  ):
                 # If sequences have already been processed, the token type IDs will be created according to the previous
                 # answer.
                 if prev_answers is not None:
                      A:		Any        =		token_type_ids_example[:, 3]  # shape (seq_len,)
                      A:		Union[str, Any]        =		np.zeros_like(prev_labels_example.cpu().numpy()  )  # shape (seq_len,)

                      A:		List[str]        =		token_type_ids[index]  # shape (seq_len, 7)
                      for i in range(model_labels.shape[0]  ):
                           A:		Dict        =		token_type_ids_example[:, 0].tolist()[i]
                           A:		str        =		token_type_ids_example[:, 1].tolist()[i] - 1
                           A:		Tuple        =		token_type_ids_example[:, 2].tolist()[i] - 1

                           if row_id >= 0 and col_id >= 0 and segment_id == 1:
                                A:		Dict        =		int(prev_answers[(col_id, row_id)]  )

                      A:		Optional[int]        =		torch.from_numpy(SCREAMING_SNAKE_CASE_  ).type(torch.long  ).to(self.device  )

                 A:		str        =		input_ids[index]
                 A:		int        =		attention_mask[index]  # shape (seq_len,)
                 A:		Any        =		token_type_ids[index]  # shape (seq_len, 7)
                 A:		Dict        =		self.model(
                     input_ids=input_ids_example.unsqueeze(0  )				,     attention_mask=attention_mask_example.unsqueeze(0  )				,     token_type_ids=token_type_ids_example.unsqueeze(0  )				,     )
                 A:		Optional[Any]        =		outputs.logits

                 if self.aggregate:
                      all_aggregations.append(outputs.logits_aggregation  )

                 all_logits.append(SCREAMING_SNAKE_CASE_  )

                 A:		Optional[Any]        =		torch.distributions.Bernoulli(logits=SCREAMING_SNAKE_CASE_  )
                 A:		List[str]        =		dist_per_token.probs * attention_mask_example.type(torch.floataa  ).to(
                     dist_per_token.probs.device  )

                 A:		str        =		collections.defaultdict(SCREAMING_SNAKE_CASE_  )
                 for i, p in enumerate(probabilities.squeeze().tolist()  ):
                      A:		Tuple        =		token_type_ids_example[:, 0].tolist()[i]
                      A:		int        =		token_type_ids_example[:, 1].tolist()[i] - 1
                      A:		List[Any]        =		token_type_ids_example[:, 2].tolist()[i] - 1
                      if col >= 0 and row >= 0 and segment_id == 1:
                           coords_to_probs[(col, row)].append(SCREAMING_SNAKE_CASE_  )

                 A:		Dict        =		{key: np.array(coords_to_probs[key]  ).mean() > 0.5 for key in coords_to_probs}

            A:		Union[str, Any]        =		torch.cat(tuple(SCREAMING_SNAKE_CASE_  )				,     0  )

            return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(SCREAMING_SNAKE_CASE_  )				,     0  ))
       else:
            A:		List[Any]        =		[]
            A:		Tuple        =		[]
            A:		int        =		None
            A:		Tuple        =		inputs['''input_ids'''].shape[0]

            A:		List[str]        =		inputs['''input_ids''']
            A:		Tuple        =		inputs['''attention_mask''']
            A:		Union[str, Any]        =		inputs['''token_type_ids'''].numpy()
            A:		List[Any]        =		None

            for index in range(SCREAMING_SNAKE_CASE_  ):
                 # If sequences have already been processed, the token type IDs will be created according to the previous
                 # answer.
                 if prev_answers is not None:
                      A:		Dict        =		token_type_ids_example[:, 3]  # shape (seq_len,)
                      A:		Optional[int]        =		np.zeros_like(SCREAMING_SNAKE_CASE_				,     dtype=np.intaa  )  # shape (seq_len,)

                      A:		Dict        =		token_type_ids[index]  # shape (seq_len, 7)
                      for i in range(model_labels.shape[0]  ):
                           A:		str        =		token_type_ids_example[:, 0].tolist()[i]
                           A:		Dict        =		token_type_ids_example[:, 1].tolist()[i] - 1
                           A:		Optional[Any]        =		token_type_ids_example[:, 2].tolist()[i] - 1

                           if row_id >= 0 and col_id >= 0 and segment_id == 1:
                                A:		Union[str, Any]        =		int(prev_answers[(col_id, row_id)]  )

                      A:		Optional[Any]        =		model_labels

                 A:		List[str]        =		input_ids[index]
                 A:		List[str]        =		attention_mask[index]  # shape (seq_len,)
                 A:		Dict        =		token_type_ids[index]  # shape (seq_len, 7)
                 A:		Dict        =		self.model(
                     input_ids=np.expand_dims(SCREAMING_SNAKE_CASE_				,     axis=0  )				,     attention_mask=np.expand_dims(SCREAMING_SNAKE_CASE_				,     axis=0  )				,     token_type_ids=np.expand_dims(SCREAMING_SNAKE_CASE_				,     axis=0  )				,     )
                 A:		List[Any]        =		outputs.logits

                 if self.aggregate:
                      all_aggregations.append(outputs.logits_aggregation  )

                 all_logits.append(SCREAMING_SNAKE_CASE_  )

                 A:		str        =		tfp.distributions.Bernoulli(logits=SCREAMING_SNAKE_CASE_  )
                 A:		int        =		dist_per_token.probs_parameter() * tf.cast(SCREAMING_SNAKE_CASE_				,     tf.floataa  )

                 A:		Optional[Any]        =		collections.defaultdict(SCREAMING_SNAKE_CASE_  )
                 A:		Dict        =		token_type_ids_example
                 for i, p in enumerate(tf.squeeze(SCREAMING_SNAKE_CASE_  ).numpy().tolist()  ):
                      A:		List[Any]        =		token_type_ids_example[:, 0].tolist()[i]
                      A:		Optional[Any]        =		token_type_ids_example[:, 1].tolist()[i] - 1
                      A:		Optional[int]        =		token_type_ids_example[:, 2].tolist()[i] - 1
                      if col >= 0 and row >= 0 and segment_id == 1:
                           coords_to_probs[(col, row)].append(SCREAMING_SNAKE_CASE_  )

                 A:		Dict        =		{key: np.array(coords_to_probs[key]  ).mean() > 0.5 for key in coords_to_probs}

            A:		int        =		tf.concat(tuple(SCREAMING_SNAKE_CASE_  )				,     0  )

            return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(SCREAMING_SNAKE_CASE_  )				,     0  ))



  def __call__(   self							:       Any				,     *SCREAMING_SNAKE_CASE_							:       Any				,     **SCREAMING_SNAKE_CASE_							:       Union[str, Any]  ) ->		str:







       '''simple docstring'''



       A:		List[str]        =		self._args_parser(*SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_  )

       A:		Optional[Any]        =		super().__call__(SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_  )
       if len(SCREAMING_SNAKE_CASE_  ) == 1:
            return results[0]
       return results



  def 				_snake_case    (   self							:       int				,     SCREAMING_SNAKE_CASE_							:       Optional[int]=None				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]=None				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]=None				,     **SCREAMING_SNAKE_CASE_							:       List[Any]  ) ->		Optional[Any]:







       '''simple docstring'''



       A:		Tuple        =		{}
       if padding is not None:
            A:		Union[str, Any]        =		padding
       if truncation is not None:
            A:		Dict        =		truncation

       A:		Optional[Any]        =		{}
       if sequential is not None:
            A:		List[str]        =		sequential
       return preprocess_params, forward_params, {}



  def 				_snake_case    (   self							:       str				,     SCREAMING_SNAKE_CASE_							:       Any				,     SCREAMING_SNAKE_CASE_							:       Optional[int]=None				,     SCREAMING_SNAKE_CASE_							:       List[Any]=True				,     SCREAMING_SNAKE_CASE_							:       int=None  ) ->		str:







       '''simple docstring'''



       if truncation is None:
            if self.type == "tapas":
                 A:		Optional[Any]        =		'''drop_rows_to_fit'''
            else:
                 A:		str        =		'''do_not_truncate'''

       A    ,						A:		List[Any]        =		pipeline_input['''table'''], pipeline_input['''query''']
       if table.empty:
            raise ValueError('''table is empty'''  )
       if query is None or query == "":
            raise ValueError('''query is empty'''  )
       A:		str        =		self.tokenizer(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     return_tensors=self.framework				,     truncation=SCREAMING_SNAKE_CASE_				,     padding=SCREAMING_SNAKE_CASE_  )
       A:		Optional[Any]        =		table
       return inputs



  def 				_snake_case    (   self							:       Optional[Any]				,     SCREAMING_SNAKE_CASE_							:       int				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]=False  ) ->		Tuple:







       '''simple docstring'''



       A:		Optional[int]        =		model_inputs.pop('''table'''  )

       if self.type == "tapas":
            if sequential:
                 A:		List[Any]        =		self.sequential_inference(**SCREAMING_SNAKE_CASE_  )
            else:
                 A:		str        =		self.batch_inference(**SCREAMING_SNAKE_CASE_  )
       else:
            A:		List[str]        =		self.model.generate(**SCREAMING_SNAKE_CASE_  )
       A:		Any        =		{'''model_inputs''': model_inputs, '''table''': table, '''outputs''': outputs}
       return model_outputs




  def 				_snake_case    (   self							:       Optional[int]				,     SCREAMING_SNAKE_CASE_							:       Optional[int]  ) ->		Dict:







       '''simple docstring'''



       A:		Dict        =		model_outputs['''model_inputs''']
       A:		Union[str, Any]        =		model_outputs['''table''']
       A:		Optional[int]        =		model_outputs['''outputs''']
       if self.type == "tapas":
            if self.aggregate:
                 A    ,						A:		Tuple        =		outputs[:2]
                 A:		List[str]        =		self.tokenizer.convert_logits_to_predictions(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )
                 A    ,						A:		Union[str, Any]        =		predictions
                 A:		Optional[Any]        =		{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(SCREAMING_SNAKE_CASE_  )}

                 A:		Union[str, Any]        =		self.model.config.no_aggregation_label_index
                 A:		Optional[Any]        =		{
                     i: aggregators[i] + ''' > ''' for i, pred in enumerate(SCREAMING_SNAKE_CASE_  ) if pred != no_agg_label_index
                 }
            else:
                 A:		Optional[int]        =		outputs[0]
                 A:		Dict        =		self.tokenizer.convert_logits_to_predictions(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )
                 A:		Union[str, Any]        =		predictions[0]
                 A:		Union[str, Any]        =		{}
                 A:		Any        =		{}
            A:		List[str]        =		[]
            for index, coordinates in enumerate(SCREAMING_SNAKE_CASE_  ):
                 A:		List[str]        =		[table.iat[coordinate] for coordinate in coordinates]
                 A:		Optional[int]        =		aggregators.get(SCREAMING_SNAKE_CASE_				,     ''''''  )
                 A:		Any        =		aggregators_prefix.get(SCREAMING_SNAKE_CASE_				,     ''''''  )
                 A:		List[str]        =		{
                     '''answer''': aggregator_prefix + ''', '''.join(SCREAMING_SNAKE_CASE_  ),
                     '''coordinates''': coordinates,
                     '''cells''': [table.iat[coordinate] for coordinate in coordinates],
                 }
                 if aggregator:
                      A:		Dict        =		aggregator

                 answers.append(SCREAMING_SNAKE_CASE_  )
            if len(SCREAMING_SNAKE_CASE_  ) == 0:
                 raise PipelineException('''Empty answer'''  )
       else:
            A:		int        =		[{'''answer''': answer} for answer in self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE_				,     skip_special_tokens=SCREAMING_SNAKE_CASE_  )]

       return answers if len(SCREAMING_SNAKE_CASE_  ) > 1 else answers[0]






