




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    ):






			'''simple docstring'''





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

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

																		__magic_name__				:						int     				=				pd.DataFrame(tqa_pipeline_input['''table''']	)

								return tqa_pipeline_inputs






@add_end_docstrings(_lowerCamelCase    )
class  lowerCamelCase     (		_lowerCamelCase    ):






			'''simple docstring'''





			UpperCamelCase__										='''table,query'''

			def __init__(			self      : Optional[Any]							,    lowerCamelCase_      : str=TableQuestionAnsweringArgumentHandler()							,    *lowerCamelCase_      : Any							,    **lowerCamelCase_      : Dict	)    -> Optional[Any]:
								super().__init__(*lowerCamelCase_							,    **lowerCamelCase_	)
								__magic_name__				:						int     				=				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()	)	)

								__magic_name__				:						Union[str, Any]     				=				bool(getattr(self.model.config							,    '''aggregation_labels'''							,    lowerCamelCase_	)	) and bool(
								    getattr(self.model.config							,    '''num_aggregation_labels'''							,    lowerCamelCase_	)	)
								__magic_name__				:						List[str]     				=				'''tapas''' if hasattr(self.model.config							,    '''aggregation_labels'''	) else None

			def 				UpperCAmelCase__							(			self      : List[Any]							,    **lowerCamelCase_      : Tuple	)    -> int:
								return self.model(**lowerCamelCase_	)

			def 				UpperCAmelCase__							(			self      : Optional[Any]							,    **lowerCamelCase_      : List[str]	)    -> List[str]:
								if self.framework == "pt":
													__magic_name__				:						int     				=				[]
													__magic_name__				:						List[Any]     				=				[]
													__magic_name__				:						Optional[int]     				=				None
													__magic_name__				:						Optional[int]     				=				inputs['''input_ids'''].shape[0]

													__magic_name__				:						str     				=				inputs['''input_ids'''].to(self.device	)
													__magic_name__				:						Union[str, Any]     				=				inputs['''attention_mask'''].to(self.device	)
													__magic_name__				:						Union[str, Any]     				=				inputs['''token_type_ids'''].to(self.device	)
													__magic_name__				:						Tuple     				=				None

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

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

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

																							__magic_name__				:						Union[str, Any]     				=				torch.from_numpy(lowerCamelCase_	).type(torch.long	).to(self.device	)

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

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

																		all_logits.append(lowerCamelCase_	)

																		__magic_name__				:						List[Any]     				=				torch.distributions.Bernoulli(logits=lowerCamelCase_	)
																		__magic_name__				:						Tuple     				=				dist_per_token.probs * attention_mask_example.type(torch.floataa	).to(
																		    dist_per_token.probs.device	)

																		__magic_name__				:						Union[str, Any]     				=				collections.defaultdict(lowerCamelCase_	)
																		for i, p in enumerate(probabilities.squeeze().tolist()	):
																							__magic_name__				:						Union[str, Any]     				=				token_type_ids_example[:, 0].tolist()[i]
																							__magic_name__				:						List[str]     				=				token_type_ids_example[:, 1].tolist()[i] - 1
																							__magic_name__				:						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(lowerCamelCase_	)

																		__magic_name__				:						Union[str, Any]     				=				{key: np.array(coords_to_probs[key]	).mean() > 0.5 for key in coords_to_probs}

													__magic_name__				:						Dict     				=				torch.cat(tuple(lowerCamelCase_	)							,    0	)

													return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(lowerCamelCase_	)							,    0	))
								else:
													__magic_name__				:						Optional[Any]     				=				[]
													__magic_name__				:						Union[str, Any]     				=				[]
													__magic_name__				:						Dict     				=				None
													__magic_name__				:						List[Any]     				=				inputs['''input_ids'''].shape[0]

													__magic_name__				:						Dict     				=				inputs['''input_ids''']
													__magic_name__				:						int     				=				inputs['''attention_mask''']
													__magic_name__				:						Dict     				=				inputs['''token_type_ids'''].numpy()
													__magic_name__				:						Tuple     				=				None

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

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

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

																							__magic_name__				:						str     				=				model_labels

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

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

																		all_logits.append(lowerCamelCase_	)

																		__magic_name__				:						str     				=				tfp.distributions.Bernoulli(logits=lowerCamelCase_	)
																		__magic_name__				:						Dict     				=				dist_per_token.probs_parameter() * tf.cast(lowerCamelCase_							,    tf.floataa	)

																		__magic_name__				:						List[str]     				=				collections.defaultdict(lowerCamelCase_	)
																		__magic_name__				:						Dict     				=				token_type_ids_example
																		for i, p in enumerate(tf.squeeze(lowerCamelCase_	).numpy().tolist()	):
																							__magic_name__				:						Any     				=				token_type_ids_example[:, 0].tolist()[i]
																							__magic_name__				:						List[str]     				=				token_type_ids_example[:, 1].tolist()[i] - 1
																							__magic_name__				:						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(lowerCamelCase_	)

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

													__magic_name__				:						List[str]     				=				tf.concat(tuple(lowerCamelCase_	)							,    0	)

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

			def __call__(			self      : int							,    *lowerCamelCase_      : Union[str, Any]							,    **lowerCamelCase_      : Dict	)    -> List[str]:
								__magic_name__				:						Optional[Any]     				=				self._args_parser(*lowerCamelCase_							,    **lowerCamelCase_	)

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

			def 				UpperCAmelCase__							(			self      : Union[str, Any]							,    lowerCamelCase_      : List[Any]=None							,    lowerCamelCase_      : Optional[int]=None							,    lowerCamelCase_      : Tuple=None							,    **lowerCamelCase_      : str	)    -> Optional[Any]:
								__magic_name__				:						Optional[Any]     				=				{}
								if padding is not None:
													__magic_name__				:						Any     				=				padding
								if truncation is not None:
													__magic_name__				:						Union[str, Any]     				=				truncation

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

			def 				UpperCAmelCase__							(			self      : Dict							,    lowerCamelCase_      : Tuple							,    lowerCamelCase_      : int=None							,    lowerCamelCase_      : Dict=True							,    lowerCamelCase_      : Dict=None	)    -> str:
								if truncation is None:
													if self.type == "tapas":
																		__magic_name__				:						int     				=				'''drop_rows_to_fit'''
													else:
																		__magic_name__				:						Dict     				=				'''do_not_truncate'''

								__magic_name__				,     __magic_name__				:						Optional[int]     				=				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'''	)
								__magic_name__				:						Tuple     				=				self.tokenizer(lowerCamelCase_							,    lowerCamelCase_							,    return_tensors=self.framework							,    truncation=lowerCamelCase_							,    padding=lowerCamelCase_	)
								__magic_name__				:						Any     				=				table
								return inputs

			def 				UpperCAmelCase__							(			self      : str							,    lowerCamelCase_      : List[str]							,    lowerCamelCase_      : List[Any]=False	)    -> int:
								__magic_name__				:						List[str]     				=				model_inputs.pop('''table'''	)

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


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

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

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

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









def 		lowercase__ (		__A:						list[list[int]]					):





					'''simple docstring'''






					if len(__A					) == 0 or not all(len(__A					) == 2 for pair in coordinates					):
										raise ValueError('''The program cannot work out a fitting polynomial.'''					)

					if len({tuple(__A					) for pair in coordinates}					) != len(__A					):
										raise ValueError('''The program cannot work out a fitting polynomial.'''					)

					__magic_name__				:						Union[str, Any]     				=				{x for x, _ in coordinates}
					if len(__A					) == 1:
										return F'''x={coordinates[0][0]}'''

					if len(__A					) != len(__A					):
										raise ValueError('''The program cannot work out a fitting polynomial.'''					)

					__magic_name__				:						List[Any]     				=				len(__A					)

					__magic_name__				:						int     				=				0
					__magic_name__				:						list[list[float]]     				=				[]
					# put the x and x to the power values in a matrix
					while count_of_line < x:
										__magic_name__				:						List[Any]     				=				0
										__magic_name__				:						List[str]     				=				coordinates[count_of_line][0]
										__magic_name__				:						list[float]     				=				[]
										while count_in_line < x:
															count_line.append(a ** (x - (count_in_line + 1))					)
															count_in_line += 1
										matrix.append(__A					)
										count_of_line += 1

					__magic_name__				:						Union[str, Any]     				=				0
					# put the y values into a vector
					__magic_name__				:						list[float]     				=				[]
					while count_of_line < x:
										vector.append(coordinates[count_of_line][1]					)
										count_of_line += 1

					__magic_name__				:						int     				=				0

					while count < x:
										__magic_name__				:						int     				=				0
										while zahlen < x:
															if count == zahlen:
																				zahlen += 1
															if zahlen == x:
																				break
															__magic_name__				:						Tuple     				=				matrix[zahlen][count] / matrix[count][count]
															for counting_columns, item in enumerate(matrix[count]					):
																				# manipulating all the values in the matrix
																				matrix[zahlen][counting_columns] -= item * bruch
															# manipulating the values in the vector
															vector[zahlen] -= vector[count] * bruch
															zahlen += 1
										count += 1

					__magic_name__				:						str     				=				0
					# make solutions
					__magic_name__				:						list[str]     				=				[]
					while count < x:
										solution.append(str(vector[count] / matrix[count][count]					)					)
										count += 1

					__magic_name__				:						List[Any]     				=				0
					__magic_name__				:						Union[str, Any]     				=				'''f(x)='''

					while count < x:
										__magic_name__				:						list[str]     				=				solution[count].split('''E'''					)
										if len(__A					) > 1:
															__magic_name__				:						Union[str, Any]     				=				F'''{remove_e[0]}*10^{remove_e[1]}'''
										solved += F'''x^{x - (count + 1)}*{solution[count]}'''
										if count + 1 != x:
															solved += "+"
										count += 1

					return solved


if __name__ == "__main__":
	print(points_to_polynomial([]))
	print(points_to_polynomial([[]]))
	print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
	print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
	print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
	print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
	print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
	print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
	print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
	print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))



