



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					(	lowerCamelCase__			):




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



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

																			if table[0].get("""query"""						) is not None and table[0].get("""table"""						) is not None:
																								_lowerCAmelCase         =       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(_snake_case		,							_snake_case						) or isinstance(_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(_snake_case						)})'						)
									else:
														_lowerCAmelCase         =       [{"""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         =       pd.DataFrame(tqa_pipeline_input["""table"""]						)

									return tqa_pipeline_inputs





@add_end_docstrings(lowerCamelCase__			)
class    __lowerCAmelCase					(	lowerCamelCase__			):
				__lowerCamelCase					=							'''table,query'''




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



									"""simple docstring"""





									super().__init__(*_snake_case		,							**_snake_case						)
									_lowerCAmelCase         =       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         =       bool(getattr(self.model.config		,							"""aggregation_labels"""		,							_snake_case						)						) and bool(
									    getattr(self.model.config		,							"""num_aggregation_labels"""		,							_snake_case						)						)
									_lowerCAmelCase         =       """tapas""" if hasattr(self.model.config		,							"""aggregation_labels"""						) else None




				def 				snake_case  (				self		,							**_snake_case						):



									"""simple docstring"""





									return self.model(**_snake_case						)




				def 				snake_case  (				self		,							**_snake_case						):



									"""simple docstring"""





									if self.framework == "pt":
														_lowerCAmelCase         =       []
														_lowerCAmelCase         =       []
														_lowerCAmelCase         =       None
														_lowerCAmelCase         =       inputs["""input_ids"""].shape[0]

														_lowerCAmelCase         =       inputs["""input_ids"""].to(self.device						)
														_lowerCAmelCase         =       inputs["""attention_mask"""].to(self.device						)
														_lowerCAmelCase         =       inputs["""token_type_ids"""].to(self.device						)
														_lowerCAmelCase         =       None

														for index in range(_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:
																								_lowerCAmelCase         =       token_type_ids_example[:, 3]  # shape (seq_len,)
																								_lowerCAmelCase         =       np.zeros_like(prev_labels_example.cpu().numpy()						)  # shape (seq_len,)

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

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

																								_lowerCAmelCase         =       torch.from_numpy(_snake_case						).type(torch.long						).to(self.device						)

																			_lowerCAmelCase         =       input_ids[index]
																			_lowerCAmelCase         =       attention_mask[index]  # shape (seq_len,)
																			_lowerCAmelCase         =       token_type_ids[index]  # shape (seq_len, 7)
																			_lowerCAmelCase         =       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         =       outputs.logits

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

																			all_logits.append(_snake_case						)

																			_lowerCAmelCase         =       torch.distributions.Bernoulli(logits=_snake_case						)
																			_lowerCAmelCase         =       dist_per_token.probs * attention_mask_example.type(torch.floataa						).to(
																			    dist_per_token.probs.device						)

																			_lowerCAmelCase         =       collections.defaultdict(_snake_case						)
																			for i, p in enumerate(probabilities.squeeze().tolist()						):
																								_lowerCAmelCase         =       token_type_ids_example[:, 0].tolist()[i]
																								_lowerCAmelCase         =       token_type_ids_example[:, 1].tolist()[i] - 1
																								_lowerCAmelCase         =       token_type_ids_example[:, 2].tolist()[i] - 1
																								if col >= 0 and row >= 0 and segment_id == 1:
																													coords_to_probs[(col, row)].append(_snake_case						)

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

														_lowerCAmelCase         =       torch.cat(tuple(_snake_case						)		,							0						)

														return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(_snake_case						)		,							0						))
									else:
														_lowerCAmelCase         =       []
														_lowerCAmelCase         =       []
														_lowerCAmelCase         =       None
														_lowerCAmelCase         =       inputs["""input_ids"""].shape[0]

														_lowerCAmelCase         =       inputs["""input_ids"""]
														_lowerCAmelCase         =       inputs["""attention_mask"""]
														_lowerCAmelCase         =       inputs["""token_type_ids"""].numpy()
														_lowerCAmelCase         =       None

														for index in range(_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:
																								_lowerCAmelCase         =       token_type_ids_example[:, 3]  # shape (seq_len,)
																								_lowerCAmelCase         =       np.zeros_like(_snake_case		,							dtype=np.intaa						)  # shape (seq_len,)

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

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

																								_lowerCAmelCase         =       model_labels

																			_lowerCAmelCase         =       input_ids[index]
																			_lowerCAmelCase         =       attention_mask[index]  # shape (seq_len,)
																			_lowerCAmelCase         =       token_type_ids[index]  # shape (seq_len, 7)
																			_lowerCAmelCase         =       self.model(
																			    input_ids=np.expand_dims(_snake_case		,							axis=0						)		,							attention_mask=np.expand_dims(_snake_case		,							axis=0						)		,							token_type_ids=np.expand_dims(_snake_case		,							axis=0						)		,							)
																			_lowerCAmelCase         =       outputs.logits

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

																			all_logits.append(_snake_case						)

																			_lowerCAmelCase         =       tfp.distributions.Bernoulli(logits=_snake_case						)
																			_lowerCAmelCase         =       dist_per_token.probs_parameter() * tf.cast(_snake_case		,							tf.floataa						)

																			_lowerCAmelCase         =       collections.defaultdict(_snake_case						)
																			_lowerCAmelCase         =       token_type_ids_example
																			for i, p in enumerate(tf.squeeze(_snake_case						).numpy().tolist()						):
																								_lowerCAmelCase         =       token_type_ids_example[:, 0].tolist()[i]
																								_lowerCAmelCase         =       token_type_ids_example[:, 1].tolist()[i] - 1
																								_lowerCAmelCase         =       token_type_ids_example[:, 2].tolist()[i] - 1
																								if col >= 0 and row >= 0 and segment_id == 1:
																													coords_to_probs[(col, row)].append(_snake_case						)

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

														_lowerCAmelCase         =       tf.concat(tuple(_snake_case						)		,							0						)

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




				def __call__(				self		,							*_snake_case		,							**_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       self._args_parser(*_snake_case		,							**_snake_case						)

									_lowerCAmelCase         =       super().__call__(_snake_case		,							**_snake_case						)
									if len(_snake_case						) == 1:
														return results[0]
									return results




				def 				snake_case  (				self		,							_snake_case=None		,							_snake_case=None		,							_snake_case=None		,							**_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       {}
									if padding is not None:
														_lowerCAmelCase         =       padding
									if truncation is not None:
														_lowerCAmelCase         =       truncation

									_lowerCAmelCase         =       {}
									if sequential is not None:
														_lowerCAmelCase         =       sequential
									return preprocess_params, forward_params, {}




				def 				snake_case  (				self		,							_snake_case		,							_snake_case=None		,							_snake_case=True		,							_snake_case=None						):



									"""simple docstring"""





									if truncation is None:
														if self.type == "tapas":
																			_lowerCAmelCase         =       """drop_rows_to_fit"""
														else:
																			_lowerCAmelCase         =       """do_not_truncate"""

									_lowerCAmelCase      ,      _lowerCAmelCase         =       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         =       self.tokenizer(_snake_case		,							_snake_case		,							return_tensors=self.framework		,							truncation=_snake_case		,							padding=_snake_case						)
									_lowerCAmelCase         =       table
									return inputs




				def 				snake_case  (				self		,							_snake_case		,							_snake_case=False						):



									"""simple docstring"""





									_lowerCAmelCase         =       model_inputs.pop("""table"""						)

									if self.type == "tapas":
														if sequential:
																			_lowerCAmelCase         =       self.sequential_inference(**_snake_case						)
														else:
																			_lowerCAmelCase         =       self.batch_inference(**_snake_case						)
									else:
														_lowerCAmelCase         =       self.model.generate(**_snake_case						)
									_lowerCAmelCase         =       {"""model_inputs""": model_inputs, """table""": table, """outputs""": outputs}
									return model_outputs




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       model_outputs["""model_inputs"""]
									_lowerCAmelCase         =       model_outputs["""table"""]
									_lowerCAmelCase         =       model_outputs["""outputs"""]
									if self.type == "tapas":
														if self.aggregate:
																			_lowerCAmelCase      ,      _lowerCAmelCase         =       outputs[:2]
																			_lowerCAmelCase         =       self.tokenizer.convert_logits_to_predictions(_snake_case		,							_snake_case		,							_snake_case						)
																			_lowerCAmelCase      ,      _lowerCAmelCase         =       predictions
																			_lowerCAmelCase         =       {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(_snake_case						)}

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

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

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








# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       Queue()
					_lowerCAmelCase         =       [False] * len(snake_case     )
					_lowerCAmelCase         =       [-1] * len(snake_case     )

					def bfs():
										while not queue.empty():
															_lowerCAmelCase         =       queue.get()
															_lowerCAmelCase         =       True

															for neighbour in graph[u]:
																				if neighbour == u:
																									return False

																				if color[neighbour] == -1:
																									_lowerCAmelCase         =       1 - color[u]
																									queue.put(snake_case     )

																				elif color[neighbour] == color[u]:
																									return False

										return True

					for i in range(len(snake_case     )     ):
										if not visited[i]:
															queue.put(snake_case     )
															_lowerCAmelCase         =       0
															if bfs() is False:
																				return False

					return True


if __name__ == "__main__":
	# Adjacency List of graph
	print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))



