




from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
SCREAMING_SNAKE_CASE__ :   List[Any]						=     20

# Time between time steps in seconds
SCREAMING_SNAKE_CASE__ :   str						=     INTERVAL / 1000




class 	UpperCamelCase__			:

    '''simple docstring'''



    def __init__(					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ = 1.0  ,						UpperCamelCase__ = 1.0  ,						UpperCamelCase__ = "blue"  ,						)  ->   None:
           lowerCamelCase     :   Optional[Any]    				=      position_x
           lowerCamelCase     :   str    				=      position_y
           lowerCamelCase     :   Tuple    				=      velocity_x
           lowerCamelCase     :   Any    				=      velocity_y
           lowerCamelCase     :   str    				=      mass
           lowerCamelCase     :   List[Any]    				=      size
           lowerCamelCase     :   int    				=      color







    @property
    def 					_lowercase  (					self  )  ->   tuple[float, float]:
           return self.position_x, self.position_y







    @property
    def 					_lowercase  (					self  )  ->   tuple[float, float]:
           return self.velocity_x, self.velocity_y







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   None:
           self.velocity_x += force_x * delta_time
           self.velocity_y += force_y * delta_time







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   None:
           self.position_x += self.velocity_x * delta_time
           self.position_y += self.velocity_y * delta_time




class 	UpperCamelCase__			:

    '''simple docstring'''



    def __init__(					self  ,						UpperCamelCase__  ,						UpperCamelCase__ = 1.0  ,						UpperCamelCase__ = 1.0  ,						UpperCamelCase__ = 0.0  ,						)  ->   None:
           lowerCamelCase     :   List[Any]    				=      bodies
           lowerCamelCase     :   Optional[int]    				=      gravitation_constant
           lowerCamelCase     :   str    				=      time_factor
           lowerCamelCase     :   Dict    				=      softening_factor







    def __len__(					self  )  ->   int:
           return len(self.bodies  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   None:
           for bodya in self.bodies:
                  lowerCamelCase     :   Tuple    				=      0.0
                  lowerCamelCase     :   Tuple    				=      0.0
                  for bodya in self.bodies:
                         if bodya != bodya:
                                lowerCamelCase     :   Any    				=      bodya.position_x - bodya.position_x
                                lowerCamelCase     :   Dict    				=      bodya.position_y - bodya.position_y

                                # Calculation of the distance using Pythagoras's theorem
                                # Extra factor due to the softening technique
                                lowerCamelCase     :   int    				=      (dif_x**2 + dif_y**2 + self.softening_factor) ** (
                                    1 / 2
                                )

                                # Newton's law of universal gravitation.
                                force_x += (
                                    self.gravitation_constant * bodya.mass * dif_x / distance**3
                                )
                                force_y += (
                                    self.gravitation_constant * bodya.mass * dif_y / distance**3
                                )

            # Update the body's velocity once all the force components have been added
                  bodya.update_velocity(UpperCamelCase__  ,						UpperCamelCase__  ,						delta_time * self.time_factor  )

           # Update the positions only after all the velocities have been updated
           for body in self.bodies:
                  body.update_position(delta_time * self.time_factor  )




def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				None:
       body_system.update_system(_SCREAMING_SNAKE_CASE					)

       # Update the positions of the patches
       for patch, body in zip(_SCREAMING_SNAKE_CASE				,body_system.bodies					):
              lowerCamelCase     :   Tuple    				=      (body.position_x, body.position_y)

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE = -1				,_SCREAMING_SNAKE_CASE = 1				,_SCREAMING_SNAKE_CASE = -1				,_SCREAMING_SNAKE_CASE = 1				,)			->				None:
       lowerCamelCase     :   List[Any]    				=      plt.figure()
       fig.canvas.set_window_title(_SCREAMING_SNAKE_CASE					)
       lowerCamelCase     :   Any    				=      plt.axes(
           xlim=(x_start, x_end)				,ylim=(y_start, y_end)					)  # Set section to be plotted
       plt.gca().set_aspect("equal"					)  # Fix aspect ratio

       # Each body is drawn as a patch by the plt-function
       lowerCamelCase     :   Dict    				=      [
           plt.Circle((body.position_x, body.position_y)				,body.size				,fc=body.color					)
           for body in body_system.bodies
       ]

       for patch in patches:
              ax.add_patch(_SCREAMING_SNAKE_CASE					)

       # Function called at each step of the animation
       def update(_SCREAMING_SNAKE_CASE					) -> list[plt.Circle]:
              update_step(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)
              return patches

       lowerCamelCase     :   int    				=      animation.FuncAnimation(  # noqa: F841
           _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,interval=_SCREAMING_SNAKE_CASE				,blit=_SCREAMING_SNAKE_CASE					)

       plt.show()

def   A   (   )			->				BodySystem:
       lowerCamelCase     :   Optional[Any]    				=      0.9700436
       lowerCamelCase     :   Optional[int]    				=      -0.24308753
       lowerCamelCase     :   Union[str, Any]    				=      0.466203685
       lowerCamelCase     :   List[str]    				=      0.43236573

       lowerCamelCase     :   Tuple    				=      [
           Body(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,size=0.2				,color="red"					),
           Body(-position_x				,-position_y				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,size=0.2				,color="green"					),
           Body(0				,0				,-2 * velocity_x				,-2 * velocity_y				,size=0.2				,color="blue"					),
       ]
       return BodySystem(_SCREAMING_SNAKE_CASE				,time_factor=3					)

def   A   (   )			->				BodySystem:
       lowerCamelCase     :   Any    				=      7.34_76e22
       lowerCamelCase     :   Dict    				=      5.9_72e24
       lowerCamelCase     :   Optional[Any]    				=      1022
       lowerCamelCase     :   Optional[int]    				=      3_8439_9000
       lowerCamelCase     :   int    				=      6.6_74e-11

       # Calculation of the respective velocities so that total impulse is zero,
       # i.e. the two bodies together don't move
       lowerCamelCase     :   List[str]    				=      earth_mass * velocity_dif / (earth_mass + moon_mass)
       lowerCamelCase     :   List[Any]    				=      moon_velocity - velocity_dif

       lowerCamelCase     :   Optional[Any]    				=      Body(-earth_moon_distance				,0				,0				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,1000_0000				,"grey"					)
       lowerCamelCase     :   Tuple    				=      Body(0				,0				,0				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,5000_0000				,"blue"					)
       return BodySystem([earth, moon]				,_SCREAMING_SNAKE_CASE				,time_factor=100_0000					)

def   A   (   )			->				BodySystem:
       lowerCamelCase     :   List[str]    				=      []
       for _ in range(10					):
              lowerCamelCase     :   Tuple    				=      random.uniform(-0.5				,0.5					)
              lowerCamelCase     :   int    				=      random.uniform(-0.5				,0.5					)

              # Bodies are created pairwise with opposite velocities so that the
              # total impulse remains zero
              bodies.append(
                  Body(
                      random.uniform(-0.5				,0.5					)				,random.uniform(-0.5				,0.5					)				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,size=0.05				,)					)
              bodies.append(
                  Body(
                      random.uniform(-0.5				,0.5					)				,random.uniform(-0.5				,0.5					)				,-velocity_x				,-velocity_y				,size=0.05				,)					)
       return BodySystem(_SCREAMING_SNAKE_CASE				,0.01				,10				,0.1					)


if __name__ == "__main__":
       plot('Figure-8 solution to the 3-body-problem', example_a(), -2, 2, -2, 2)
       plot(
           'Moon\'s orbit around the earth',
           example_a(),
           -430000000,
           430000000,
           -430000000,
           430000000,
       )
       plot('Random system with many bodies', example_a(), -1.5, 1.5, -1.5, 1.5)












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 	UpperCamelCase__			(lowerCAmelCase__  ):

    '''simple docstring'''



    def __call__(					self  ,						UpperCamelCase__=None  ,						UpperCamelCase__=None  ,						**UpperCamelCase__  )  ->   List[Any]:
           # Returns tqa_pipeline_inputs of shape:
           # [
           #   {"table": pd.DataFrame, "query": List[str]},
           #   ...,
           #   {"table": pd.DataFrame, "query" : List[str]}
           # ]
           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(UpperCamelCase__  ,						UpperCamelCase__  ) and table.get("query"  ) is not None and table.get("table"  ) is not None:
                         lowerCamelCase     :   Optional[int]    				=      [table]
                  elif isinstance(UpperCamelCase__  ,						UpperCamelCase__  ) and len(UpperCamelCase__  ) > 0:
                         if not all(isinstance(UpperCamelCase__  ,						UpperCamelCase__  ) for d in table  ):
                                raise ValueError(
                                    F'''Keyword argument `table` should be a list of dict, but is {(type(UpperCamelCase__  ) for d in table)}'''  )

                         if table[0].get("query"  ) is not None and table[0].get("table"  ) is not None:
                                lowerCamelCase     :   int    				=      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(UpperCamelCase__  ,						UpperCamelCase__  ) or isinstance(UpperCamelCase__  ,						types.GeneratorType  ):
                         return table
                  else:
                         raise ValueError(
                             "Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but "
                             F'''is {type(UpperCamelCase__  )})'''  )
           else:
                  lowerCamelCase     :   Tuple    				=      [{"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."  )

                         lowerCamelCase     :   Optional[Any]    				=      pd.DataFrame(tqa_pipeline_input["table"]  )

           return tqa_pipeline_inputs




@add_end_docstrings(lowerCAmelCase__  )
class 	UpperCamelCase__			(lowerCAmelCase__  ):

    '''simple docstring'''



    lowerCamelCase_   :			Optional[Any]			  =   """table,query"""







    def __init__(					self  ,						UpperCamelCase__=TableQuestionAnsweringArgumentHandler()  ,						*UpperCamelCase__  ,						**UpperCamelCase__  )  ->   Dict:
           super().__init__(*UpperCamelCase__  ,						**UpperCamelCase__  )
           lowerCamelCase     :   Any    				=      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()  )  )

           lowerCamelCase     :   Any    				=      bool(getattr(self.model.config  ,						"aggregation_labels"  ,						UpperCamelCase__  )  ) and bool(
               getattr(self.model.config  ,						"num_aggregation_labels"  ,						UpperCamelCase__  )  )
           lowerCamelCase     :   List[Any]    				=      "tapas" if hasattr(self.model.config  ,						"aggregation_labels"  ) else None







    def 					_lowercase  (					self  ,						**UpperCamelCase__  )  ->   int:
           return self.model(**UpperCamelCase__  )







    def 					_lowercase  (					self  ,						**UpperCamelCase__  )  ->   Union[str, Any]:
           if self.framework == "pt":
                  lowerCamelCase     :   List[str]    				=      []
                  lowerCamelCase     :   List[Any]    				=      []
                  lowerCamelCase     :   List[Any]    				=      None
                  lowerCamelCase     :   List[str]    				=      inputs["input_ids"].shape[0]

                  lowerCamelCase     :   Optional[int]    				=      inputs["input_ids"].to(self.device  )
                  lowerCamelCase     :   List[str]    				=      inputs["attention_mask"].to(self.device  )
                  lowerCamelCase     :   str    				=      inputs["token_type_ids"].to(self.device  )
                  lowerCamelCase     :   str    				=      None

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

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

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

                                lowerCamelCase     :   Dict    				=      torch.from_numpy(UpperCamelCase__  ).type(torch.long  ).to(self.device  )

                         lowerCamelCase     :   int    				=      input_ids[index]
                         lowerCamelCase     :   Union[str, Any]    				=      attention_mask[index]  # shape (seq_len,)
                         lowerCamelCase     :   Dict    				=      token_type_ids[index]  # shape (seq_len, 7)
                         lowerCamelCase     :   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  )  ,						)
                         lowerCamelCase     :   str    				=      outputs.logits

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

                         all_logits.append(UpperCamelCase__  )

                         lowerCamelCase     :   int    				=      torch.distributions.Bernoulli(logits=UpperCamelCase__  )
                         lowerCamelCase     :   List[Any]    				=      dist_per_token.probs * attention_mask_example.type(torch.floataa  ).to(
                             dist_per_token.probs.device  )

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

                         lowerCamelCase     :   Optional[int]    				=      {key: np.array(coords_to_probs[key]  ).mean() > 0.5 for key in coords_to_probs}

                  lowerCamelCase     :   Optional[Any]    				=      torch.cat(tuple(UpperCamelCase__  )  ,						0  )

                  return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(UpperCamelCase__  )  ,						0  ))
           else:
                  lowerCamelCase     :   Optional[Any]    				=      []
                  lowerCamelCase     :   List[Any]    				=      []
                  lowerCamelCase     :   Union[str, Any]    				=      None
                  lowerCamelCase     :   int    				=      inputs["input_ids"].shape[0]

                  lowerCamelCase     :   Dict    				=      inputs["input_ids"]
                  lowerCamelCase     :   Tuple    				=      inputs["attention_mask"]
                  lowerCamelCase     :   Optional[int]    				=      inputs["token_type_ids"].numpy()
                  lowerCamelCase     :   Union[str, Any]    				=      None

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

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

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

                                lowerCamelCase     :   Any    				=      model_labels

                         lowerCamelCase     :   Optional[int]    				=      input_ids[index]
                         lowerCamelCase     :   Optional[Any]    				=      attention_mask[index]  # shape (seq_len,)
                         lowerCamelCase     :   Optional[int]    				=      token_type_ids[index]  # shape (seq_len, 7)
                         lowerCamelCase     :   Union[str, Any]    				=      self.model(
                             input_ids=np.expand_dims(UpperCamelCase__  ,						axis=0  )  ,						attention_mask=np.expand_dims(UpperCamelCase__  ,						axis=0  )  ,						token_type_ids=np.expand_dims(UpperCamelCase__  ,						axis=0  )  ,						)
                         lowerCamelCase     :   Dict    				=      outputs.logits

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

                         all_logits.append(UpperCamelCase__  )

                         lowerCamelCase     :   Optional[int]    				=      tfp.distributions.Bernoulli(logits=UpperCamelCase__  )
                         lowerCamelCase     :   Any    				=      dist_per_token.probs_parameter() * tf.cast(UpperCamelCase__  ,						tf.floataa  )

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

                         lowerCamelCase     :   Optional[Any]    				=      {key: np.array(coords_to_probs[key]  ).mean() > 0.5 for key in coords_to_probs}

                  lowerCamelCase     :   int    				=      tf.concat(tuple(UpperCamelCase__  )  ,						0  )

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







    def __call__(					self  ,						*UpperCamelCase__  ,						**UpperCamelCase__  )  ->   int:
           lowerCamelCase     :   Union[str, Any]    				=      self._args_parser(*UpperCamelCase__  ,						**UpperCamelCase__  )

           lowerCamelCase     :   Optional[int]    				=      super().__call__(UpperCamelCase__  ,						**UpperCamelCase__  )
           if len(UpperCamelCase__  ) == 1:
                  return results[0]
           return results







    def 					_lowercase  (					self  ,						UpperCamelCase__=None  ,						UpperCamelCase__=None  ,						UpperCamelCase__=None  ,						**UpperCamelCase__  )  ->   Optional[int]:
           lowerCamelCase     :   int    				=      {}
           if padding is not None:
                  lowerCamelCase     :   Dict    				=      padding
           if truncation is not None:
                  lowerCamelCase     :   Tuple    				=      truncation

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







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__=None  ,						UpperCamelCase__=True  ,						UpperCamelCase__=None  )  ->   str:
           if truncation is None:
                  if self.type == "tapas":
                         lowerCamelCase     :   Optional[Any]    				=      "drop_rows_to_fit"
                  else:
                         lowerCamelCase     :   str    				=      "do_not_truncate"

           lowerCamelCase				,   lowerCamelCase     :   Tuple    				=      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"  )
           lowerCamelCase     :   Any    				=      self.tokenizer(UpperCamelCase__  ,						UpperCamelCase__  ,						return_tensors=self.framework  ,						truncation=UpperCamelCase__  ,						padding=UpperCamelCase__  )
           lowerCamelCase     :   str    				=      table
           return inputs







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__=False  )  ->   Dict:
           lowerCamelCase     :   Union[str, Any]    				=      model_inputs.pop("table"  )

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







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

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

                         answers.append(UpperCamelCase__  )
                  if len(UpperCamelCase__  ) == 0:
                         raise PipelineException("Empty answer"  )
           else:
                  lowerCamelCase     :   Optional[Any]    				=      [{"answer": answer} for answer in self.tokenizer.batch_decode(UpperCamelCase__  ,						skip_special_tokens=UpperCamelCase__  )]

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






