




import collections
import types

import numpy as np

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


if is_torch_available():
				import torch

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

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

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






class 					__lowerCAmelCase				( UpperCAmelCase__				):







			def __call__( self					:    List[Any]					,					snake_case__					:    Any=None					,					snake_case__					:    Optional[int]=None					,					**snake_case__					:    Optional[Any]   ):




									"""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:
																					_UpperCAmelCase									=  [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:
																											_UpperCAmelCase									=  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:
															_UpperCAmelCase									=  [{"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."   )

																					_UpperCAmelCase									=  pd.DataFrame(tqa_pipeline_input["table"]   )

									return tqa_pipeline_inputs






@add_end_docstrings(UpperCAmelCase__				)
class 					__lowerCAmelCase				( UpperCAmelCase__				):
			snake_case_   :				Any				  =  "table,query"







			def __init__( self					:    int					,					snake_case__					:    List[Any]=TableQuestionAnsweringArgumentHandler()					,					*snake_case__					:    Any					,					**snake_case__					:    List[Any]   ):




									"""simple docstring"""


									super().__init__(*snake_case__					,					**snake_case__   )
									_UpperCAmelCase									=  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()   )   )

									_UpperCAmelCase									=  bool(getattr(self.model.config					,					"aggregation_labels"					,					snake_case__   )   ) and bool(
									    getattr(self.model.config					,					"num_aggregation_labels"					,					snake_case__   )   )
									_UpperCAmelCase									=  "tapas" if hasattr(self.model.config					,					"aggregation_labels"   ) else None







			def UpperCamelCase    ( self					:    Tuple					,					**snake_case__					:    str   ):




									"""simple docstring"""


									return self.model(**snake_case__   )







			def UpperCamelCase    ( self					:    Tuple					,					**snake_case__					:    Optional[int]   ):




									"""simple docstring"""


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

															_UpperCAmelCase									=  inputs["input_ids"].to(self.device   )
															_UpperCAmelCase									=  inputs["attention_mask"].to(self.device   )
															_UpperCAmelCase									=  inputs["token_type_ids"].to(self.device   )
															_UpperCAmelCase									=  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:
																											_UpperCAmelCase									=  token_type_ids_example[:, 3]  # shape (seq_len,)
																											_UpperCAmelCase									=  np.zeros_like(prev_labels_example.cpu().numpy()   )  # shape (seq_len,)

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

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

																											_UpperCAmelCase									=  torch.from_numpy(snake_case__   ).type(torch.long   ).to(self.device   )

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

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

																					all_logits.append(snake_case__   )

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

																					_UpperCAmelCase									=  collections.defaultdict(snake_case__   )
																					for i, p in enumerate(probabilities.squeeze().tolist()   ):
																											_UpperCAmelCase									=  token_type_ids_example[:, 0].tolist()[i]
																											_UpperCAmelCase									=  token_type_ids_example[:, 1].tolist()[i] - 1
																											_UpperCAmelCase									=  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__   )

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

															_UpperCAmelCase									=  torch.cat(tuple(snake_case__   )					,					0   )

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

															_UpperCAmelCase									=  inputs["input_ids"]
															_UpperCAmelCase									=  inputs["attention_mask"]
															_UpperCAmelCase									=  inputs["token_type_ids"].numpy()
															_UpperCAmelCase									=  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:
																											_UpperCAmelCase									=  token_type_ids_example[:, 3]  # shape (seq_len,)
																											_UpperCAmelCase									=  np.zeros_like(snake_case__					,					dtype=np.intaa   )  # shape (seq_len,)

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

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

																											_UpperCAmelCase									=  model_labels

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

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

																					all_logits.append(snake_case__   )

																					_UpperCAmelCase									=  tfp.distributions.Bernoulli(logits=snake_case__   )
																					_UpperCAmelCase									=  dist_per_token.probs_parameter() * tf.cast(snake_case__					,					tf.floataa   )

																					_UpperCAmelCase									=  collections.defaultdict(snake_case__   )
																					_UpperCAmelCase									=  token_type_ids_example
																					for i, p in enumerate(tf.squeeze(snake_case__   ).numpy().tolist()   ):
																											_UpperCAmelCase									=  token_type_ids_example[:, 0].tolist()[i]
																											_UpperCAmelCase									=  token_type_ids_example[:, 1].tolist()[i] - 1
																											_UpperCAmelCase									=  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__   )

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

															_UpperCAmelCase									=  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					:    Union[str, Any]					,					*snake_case__					:    Tuple					,					**snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self._args_parser(*snake_case__					,					**snake_case__   )

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







			def UpperCamelCase    ( self					:    Dict					,					snake_case__					:    Optional[int]=None					,					snake_case__					:    List[Any]=None					,					snake_case__					:    int=None					,					**snake_case__					:    Dict   ):




									"""simple docstring"""


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

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







			def UpperCamelCase    ( self					:    str					,					snake_case__					:    int					,					snake_case__					:    Union[str, Any]=None					,					snake_case__					:    int=True					,					snake_case__					:    Any=None   ):




									"""simple docstring"""


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

									_UpperCAmelCase							,				_UpperCAmelCase									=  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"   )
									_UpperCAmelCase									=  self.tokenizer(snake_case__					,					snake_case__					,					return_tensors=self.framework					,					truncation=snake_case__					,					padding=snake_case__   )
									_UpperCAmelCase									=  table
									return inputs







			def UpperCamelCase    ( self					:    Union[str, Any]					,					snake_case__					:    Any					,					snake_case__					:    Any=False   ):




									"""simple docstring"""


									_UpperCAmelCase									=  model_inputs.pop("table"   )

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







			def UpperCamelCase    ( self					:    Any					,					snake_case__					:    Dict   ):




									"""simple docstring"""


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

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

																					answers.append(snake_case__   )
															if len(snake_case__   ) == 0:
																					raise PipelineException("Empty answer"   )
									else:
															_UpperCAmelCase									=  [{"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]










from math import exp


def 							__SCREAMING_SNAKE_CASE						(    snake_case_		):

						'''simple docstring'''


						return 8 * x - 2 * exp(-x		)


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase									=  lower_bound
						_UpperCAmelCase									=  upper_bound
						for _ in range(0			,					snake_case_		):
												_UpperCAmelCase							,				_UpperCAmelCase									=  xa, xa - (f(snake_case_		) * (xa - xa)) / (f(snake_case_		) - f(snake_case_		))
						return xa


if __name__ == "__main__":
				print(f"""Example: {secant_method(1, 3, 2)}""")




