

'''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      a__						(							lowerCamelCase_						):


			def __call__(				self    ,       _UpperCamelCase=None    ,       _UpperCamelCase=None    ,       **_UpperCamelCase						):





						"""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(_UpperCamelCase    ,       _UpperCamelCase						) and table.get("query"						) is not None and table.get("table"						) is not None:
												_lowercase    :   List[str]      =     [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:
															_lowercase    :   Union[str, 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(_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:
									_lowercase    :   Union[str, Any]      =     [{"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."						)

												_lowercase    :   str      =     pd.DataFrame(tqa_pipeline_input["table"]						)

						return tqa_pipeline_inputs




@add_end_docstrings(lowerCamelCase_						)
class      a__						(							lowerCamelCase_						):
			_SCREAMING_SNAKE_CASE					:  Dict			 =							'table,query'


			def __init__(				self    ,       _UpperCamelCase=TableQuestionAnsweringArgumentHandler()    ,       *_UpperCamelCase    ,       **_UpperCamelCase						):





						"""simple docstring"""





						super().__init__(*_UpperCamelCase    ,       **_UpperCamelCase						)
						_lowercase    :   List[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()						)						)

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


			def 		_lowerCamelCase     (				self    ,       **_UpperCamelCase						):





						"""simple docstring"""





						return self.model(**_UpperCamelCase						)


			def 		_lowerCamelCase     (				self    ,       **_UpperCamelCase						):





						"""simple docstring"""





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

									_lowercase    :   int      =     inputs["input_ids"].to(self.device						)
									_lowercase    :   Dict      =     inputs["attention_mask"].to(self.device						)
									_lowercase    :   Tuple      =     inputs["token_type_ids"].to(self.device						)
									_lowercase    :   Optional[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:
															_lowercase    :   Optional[Any]      =     token_type_ids_example[:, 3]  # shape (seq_len,)
															_lowercase    :   Any      =     np.zeros_like(prev_labels_example.cpu().numpy()						)  # shape (seq_len,)

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

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

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

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

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

												all_logits.append(_UpperCamelCase						)

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

												_lowercase    :   Any      =     collections.defaultdict(_UpperCamelCase						)
												for i, p in enumerate(probabilities.squeeze().tolist()						):
															_lowercase    :   Optional[int]      =     token_type_ids_example[:, 0].tolist()[i]
															_lowercase    :   Optional[Any]      =     token_type_ids_example[:, 1].tolist()[i] - 1
															_lowercase    :   Tuple      =     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						)

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

									_lowercase    :   Union[str, Any]      =     torch.cat(tuple(_UpperCamelCase						)    ,       0						)

									return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(_UpperCamelCase						)    ,       0						))
						else:
									_lowercase    :   List[Any]      =     []
									_lowercase    :   Tuple      =     []
									_lowercase    :   Tuple      =     None
									_lowercase    :   Dict      =     inputs["input_ids"].shape[0]

									_lowercase    :   int      =     inputs["input_ids"]
									_lowercase    :   int      =     inputs["attention_mask"]
									_lowercase    :   Optional[Any]      =     inputs["token_type_ids"].numpy()
									_lowercase    :   Optional[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:
															_lowercase    :   Optional[Any]      =     token_type_ids_example[:, 3]  # shape (seq_len,)
															_lowercase    :   str      =     np.zeros_like(_UpperCamelCase    ,       dtype=np.intaa						)  # shape (seq_len,)

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

																		if row_id >= 0 and col_id >= 0 and segment_id == 1:
																					_lowercase    :   Optional[Any]      =     int(prev_answers[(col_id, row_id)]						)

															_lowercase    :   List[Any]      =     model_labels

												_lowercase    :   int      =     input_ids[index]
												_lowercase    :   int      =     attention_mask[index]  # shape (seq_len,)
												_lowercase    :   Tuple      =     token_type_ids[index]  # shape (seq_len, 7)
												_lowercase    :   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						)    ,       )
												_lowercase    :   Tuple      =     outputs.logits

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

												all_logits.append(_UpperCamelCase						)

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

												_lowercase    :   Optional[int]      =     collections.defaultdict(_UpperCamelCase						)
												_lowercase    :   str      =     token_type_ids_example
												for i, p in enumerate(tf.squeeze(_UpperCamelCase						).numpy().tolist()						):
															_lowercase    :   Optional[Any]      =     token_type_ids_example[:, 0].tolist()[i]
															_lowercase    :   List[Any]      =     token_type_ids_example[:, 1].tolist()[i] - 1
															_lowercase    :   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(_UpperCamelCase						)

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

									_lowercase    :   Union[str, Any]      =     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						):





						"""simple docstring"""





						_lowercase    :   Dict      =     self._args_parser(*_UpperCamelCase    ,       **_UpperCamelCase						)

						_lowercase    :   Tuple      =     super().__call__(_UpperCamelCase    ,       **_UpperCamelCase						)
						if len(_UpperCamelCase						) == 1:
									return results[0]
						return results


			def 		_lowerCamelCase     (				self    ,       _UpperCamelCase=None    ,       _UpperCamelCase=None    ,       _UpperCamelCase=None    ,       **_UpperCamelCase						):





						"""simple docstring"""





						_lowercase    :   str      =     {}
						if padding is not None:
									_lowercase    :   int      =     padding
						if truncation is not None:
									_lowercase    :   str      =     truncation

						_lowercase    :   Any      =     {}
						if sequential is not None:
									_lowercase    :   Dict      =     sequential
						return preprocess_params, forward_params, {}


			def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase=None    ,       _UpperCamelCase=True    ,       _UpperCamelCase=None						):





						"""simple docstring"""





						if truncation is None:
									if self.type == "tapas":
												_lowercase    :   Dict      =     "drop_rows_to_fit"
									else:
												_lowercase    :   Optional[int]      =     "do_not_truncate"

						_lowercase    :   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"						)
						_lowercase    :   Any      =     self.tokenizer(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors=self.framework    ,       truncation=_UpperCamelCase    ,       padding=_UpperCamelCase						)
						_lowercase    :   List[Any]      =     table
						return inputs


			def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase=False						):





						"""simple docstring"""





						_lowercase    :   Dict      =     model_inputs.pop("table"						)

						if self.type == "tapas":
									if sequential:
												_lowercase    :   Tuple      =     self.sequential_inference(**_UpperCamelCase						)
									else:
												_lowercase    :   Any      =     self.batch_inference(**_UpperCamelCase						)
						else:
									_lowercase    :   Tuple      =     self.model.generate(**_UpperCamelCase						)
						_lowercase    :   Union[str, Any]      =     {"model_inputs": model_inputs, "table": table, "outputs": outputs}
						return model_outputs






			def 		_lowerCamelCase     (				self    ,       _UpperCamelCase						):





						"""simple docstring"""





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

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

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

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









'''simple docstring'''
class      a__						:


   def __init__(				self    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   Tuple      =     data
      _lowercase    :   int
      _lowercase    :   Node







def       _A	(			snake_case  )    ->			None:
   _lowercase    :   Any      =     0
   _lowercase    :   str      =     x







def       _A	(			snake_case	,							snake_case  )    ->			None:
   _lowercase  ,   _lowercase    :   Any      =     find_set(snake_case  ), find_set(snake_case  )
   if x == y:
      return

   elif x.rank > y.rank:
      _lowercase    :   Dict      =     x
   else:
      _lowercase    :   Optional[int]      =     y
      if x.rank == y.rank:
         y.rank += 1







def       _A	(			snake_case  )    ->			Node:
   if x != x.parent:
      _lowercase    :   Optional[int]      =     find_set(x.parent  )
   return x.parent







def       _A	(			snake_case  )    ->			set:
   _lowercase    :   Any      =     ({0, 1, 2}, {3, 4, 5})
   for s in sets:
      if node.data in s:
         return s
   _lowercase    :   List[Any]      =     F'''{node.data} is not in {sets}'''
   raise ValueError(snake_case  )







def       _A	(			)    ->			None:
   _lowercase    :   Union[str, Any]      =     [Node(snake_case  ) for i in range(6  )]
   for v in vertex:
      make_set(snake_case  )

   union_set(vertex[0]	,							vertex[1]  )
   union_set(vertex[1]	,							vertex[2]  )
   union_set(vertex[3]	,							vertex[4]  )
   union_set(vertex[3]	,							vertex[5]  )

   for nodea in vertex:
      for nodea in vertex:
         if find_python_set(snake_case  ).isdisjoint(find_python_set(snake_case  )  ):
            assert find_set(snake_case  ) != find_set(snake_case  )
         else:
            assert find_set(snake_case  ) == find_set(snake_case  )


if __name__ == "__main__":
    test_disjoint_set()






