
'''simple docstring'''


import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


__SCREAMING_SNAKE_CASE						=      logging.getLogger(__name__)




class     __snake_case			(    _a	):




		def 	_snake_case	(  self					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:int			,    _SCREAMING_SNAKE_CASE					:Dict			,    _SCREAMING_SNAKE_CASE					:Dict=None			,    _SCREAMING_SNAKE_CASE					:Tuple=None)					->			List[str]:






								'''simple docstring'''






								if current_layer == 0:
														UpperCamelCase_     :	List[str]					     =     self.embedding_hidden_mapping_in(_SCREAMING_SNAKE_CASE)
								else:
														UpperCamelCase_     :	Dict					     =     hidden_states[0]

								UpperCamelCase_     :	List[str]					     =     int(self.config.num_hidden_layers / self.config.num_hidden_groups)

								# Index of the hidden group
								UpperCamelCase_     :	int					     =     int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups))

								UpperCamelCase_     :	int					     =     self.albert_layer_groups[group_idx](
								    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]			,    )
								UpperCamelCase_     :	Dict					     =     layer_group_output[0]

								return (hidden_states,)







@add_start_docstrings(
    "The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top."					,			_a					,			)
class     __snake_case			(    _a	):




		def __init__(  self					:List[Any]			,    _SCREAMING_SNAKE_CASE					:Union[str, Any])					->			Any:






								'''simple docstring'''






								super().__init__(_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	Union[str, Any]					     =     AlbertTransformerWithPabee(_SCREAMING_SNAKE_CASE)

								self.init_weights()
								UpperCamelCase_     :	Tuple					     =     0
								UpperCamelCase_     :	Dict					     =     0
								UpperCamelCase_     :	Optional[int]					     =     0

								UpperCamelCase_     :	Tuple					     =     0




		def 	_snake_case	(  self					:int			,    _SCREAMING_SNAKE_CASE					:Dict)					->			Optional[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	Any					     =     threshold




		def 	_snake_case	(  self					:Optional[int]			,    _SCREAMING_SNAKE_CASE					:Optional[Any])					->			Optional[int]:






								'''simple docstring'''






								UpperCamelCase_     :	Tuple					     =     patience




		def 	_snake_case	(  self					:str)					->			Tuple:






								'''simple docstring'''






								UpperCamelCase_     :	str					     =     0
								UpperCamelCase_     :	Dict					     =     0




		def 	_snake_case	(  self					:int)					->			List[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     self.inference_layers_num / self.inference_instances_num
								UpperCamelCase_     :	List[str]					     =     (
								    F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
								    F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
								)
								print(_SCREAMING_SNAKE_CASE)





		@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE)
		def 	_snake_case	(  self					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:Dict=None			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]=None			,    _SCREAMING_SNAKE_CASE					:Optional[int]=None			,    _SCREAMING_SNAKE_CASE					:int=None			,    _SCREAMING_SNAKE_CASE					:int=None			,    _SCREAMING_SNAKE_CASE					:Optional[Any]=None			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]=None			,    _SCREAMING_SNAKE_CASE					:Any=None			,    _SCREAMING_SNAKE_CASE					:Any=False			,    )					->			Dict:






								'''simple docstring'''






								if input_ids is not None and inputs_embeds is not None:
														raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time""")
								elif input_ids is not None:
														UpperCamelCase_     :	str					     =     input_ids.size()
								elif inputs_embeds is not None:
														UpperCamelCase_     :	Dict					     =     inputs_embeds.size()[:-1]
								else:
														raise ValueError("""You have to specify either input_ids or inputs_embeds""")

								UpperCamelCase_     :	int					     =     input_ids.device if input_ids is not None else inputs_embeds.device

								if attention_mask is None:
														UpperCamelCase_     :	Optional[int]					     =     torch.ones(_SCREAMING_SNAKE_CASE			,    device=_SCREAMING_SNAKE_CASE)
								if token_type_ids is None:
														UpperCamelCase_     :	Optional[int]					     =     torch.zeros(_SCREAMING_SNAKE_CASE			,    dtype=torch.long			,    device=_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	Tuple					     =     attention_mask.unsqueeze(1).unsqueeze(2)
								UpperCamelCase_     :	List[str]					     =     extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
								UpperCamelCase_     :	int					     =     (1.0 - extended_attention_mask) * -10000.0
								UpperCamelCase_     :	int					     =     self.get_head_mask(_SCREAMING_SNAKE_CASE			,    self.config.num_hidden_layers)

								UpperCamelCase_     :	Tuple					     =     self.embeddings(
								    _SCREAMING_SNAKE_CASE			,    position_ids=_SCREAMING_SNAKE_CASE			,    token_type_ids=_SCREAMING_SNAKE_CASE			,    inputs_embeds=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	int					     =     embedding_output

								if self.training:
														UpperCamelCase_     :	Union[str, Any]					     =     []
														for i in range(self.config.num_hidden_layers):
																				UpperCamelCase_     :	Optional[Any]					     =     self.encoder.adaptive_forward(
																				    _SCREAMING_SNAKE_CASE			,    current_layer=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE			,    head_mask=_SCREAMING_SNAKE_CASE			,    )

																				UpperCamelCase_     :	Optional[int]					     =     self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
																				UpperCamelCase_     :	Union[str, Any]					     =     output_layers[i](output_dropout(_SCREAMING_SNAKE_CASE))
																				res.append(_SCREAMING_SNAKE_CASE)
								elif self.patience == 0:  # Use all layers for inference
														UpperCamelCase_     :	Optional[int]					     =     self.encoder(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    head_mask=_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	Optional[Any]					     =     self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
														UpperCamelCase_     :	List[Any]					     =     [output_layers[self.config.num_hidden_layers - 1](_SCREAMING_SNAKE_CASE)]
								else:
														UpperCamelCase_     :	List[str]					     =     0
														UpperCamelCase_     :	Optional[Any]					     =     None
														UpperCamelCase_     :	Tuple					     =     0
														for i in range(self.config.num_hidden_layers):
																				calculated_layer_num += 1
																				UpperCamelCase_     :	Optional[int]					     =     self.encoder.adaptive_forward(
																				    _SCREAMING_SNAKE_CASE			,    current_layer=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE			,    head_mask=_SCREAMING_SNAKE_CASE			,    )

																				UpperCamelCase_     :	int					     =     self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
																				UpperCamelCase_     :	List[Any]					     =     output_layers[i](_SCREAMING_SNAKE_CASE)
																				if regression:
																										UpperCamelCase_     :	Optional[Any]					     =     logits.detach()
																										if patient_result is not None:
																																UpperCamelCase_     :	List[Any]					     =     patient_result.detach()
																										if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
																																patient_counter += 1
																										else:
																																UpperCamelCase_     :	Optional[int]					     =     0
																				else:
																										UpperCamelCase_     :	List[Any]					     =     logits.detach().argmax(dim=1)
																										if patient_result is not None:
																																UpperCamelCase_     :	Dict					     =     patient_result.detach().argmax(dim=1)
																										if (patient_result is not None) and torch.all(labels.eq(_SCREAMING_SNAKE_CASE)):
																																patient_counter += 1
																										else:
																																UpperCamelCase_     :	Optional[Any]					     =     0

																				UpperCamelCase_     :	Tuple					     =     logits
																				if patient_counter == self.patience:
																										break
														UpperCamelCase_     :	Optional[Any]					     =     [patient_result]
														self.inference_layers_num += calculated_layer_num
														self.inference_instances_num += 1

								return res







@add_start_docstrings(
    "Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. "					,			_a					,			)
class     __snake_case			(    _a	):




		def __init__(  self					:List[str]			,    _SCREAMING_SNAKE_CASE					:Optional[int])					->			Optional[int]:






								'''simple docstring'''






								super().__init__(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	str					     =     config.num_labels

								UpperCamelCase_     :	Dict					     =     AlbertModelWithPabee(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	str					     =     nn.Dropout(config.classifier_dropout_prob)
								UpperCamelCase_     :	Tuple					     =     nn.ModuleList(
								    [nn.Linear(config.hidden_size			,    self.config.num_labels) for _ in range(config.num_hidden_layers)])

								self.init_weights()





		@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE)
		def 	_snake_case	(  self					:List[str]			,    _SCREAMING_SNAKE_CASE					:Optional[Any]=None			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]=None			,    _SCREAMING_SNAKE_CASE					:List[Any]=None			,    _SCREAMING_SNAKE_CASE					:Dict=None			,    _SCREAMING_SNAKE_CASE					:List[Any]=None			,    _SCREAMING_SNAKE_CASE					:str=None			,    _SCREAMING_SNAKE_CASE					:Optional[int]=None			,    )					->			Dict:






								'''simple docstring'''






								UpperCamelCase_     :	Union[str, Any]					     =     self.albert(
								    input_ids=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE			,    token_type_ids=_SCREAMING_SNAKE_CASE			,    position_ids=_SCREAMING_SNAKE_CASE			,    head_mask=_SCREAMING_SNAKE_CASE			,    inputs_embeds=_SCREAMING_SNAKE_CASE			,    output_dropout=self.dropout			,    output_layers=self.classifiers			,    regression=self.num_labels == 1			,    )

								UpperCamelCase_     :	Any					     =     (logits[-1],)

								if labels is not None:
														UpperCamelCase_     :	int					     =     None
														UpperCamelCase_     :	Tuple					     =     0
														for ix, logits_item in enumerate(_SCREAMING_SNAKE_CASE):
																				if self.num_labels == 1:
																										#  We are doing regression
																										UpperCamelCase_     :	Optional[Any]					     =     MSELoss()
																										UpperCamelCase_     :	Tuple					     =     loss_fct(logits_item.view(-1)			,    labels.view(-1))
																				else:
																										UpperCamelCase_     :	int					     =     CrossEntropyLoss()
																										UpperCamelCase_     :	Union[str, Any]					     =     loss_fct(logits_item.view(-1			,    self.num_labels)			,    labels.view(-1))
																				if total_loss is None:
																										UpperCamelCase_     :	Dict					     =     loss
																				else:
																										total_loss += loss * (ix + 1)
																				total_weights += ix + 1
														UpperCamelCase_     :	Optional[int]					     =     (total_loss / total_weights,) + outputs

								return outputs






'''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     __snake_case			(    _a	):




		def __call__(  self					:Dict			,    _SCREAMING_SNAKE_CASE					:List[Any]=None			,    _SCREAMING_SNAKE_CASE					:Tuple=None			,    **_SCREAMING_SNAKE_CASE					:int)					->			str:






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

																				if table[0].get("""query""") is not None and table[0].get("""table""") is not None:
																										UpperCamelCase_     :	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(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE) or isinstance(_SCREAMING_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(_SCREAMING_SNAKE_CASE)})""")
								else:
														UpperCamelCase_     :	int					     =     [{"""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_     :	Any					     =     pd.DataFrame(tqa_pipeline_input["""table"""])

								return tqa_pipeline_inputs







@add_end_docstrings(_a	)
class     __snake_case			(    _a	):
		a_          =					"table,query"




		def __init__(  self					:List[Any]			,    _SCREAMING_SNAKE_CASE					:List[str]=TableQuestionAnsweringArgumentHandler()			,    *_SCREAMING_SNAKE_CASE					:Any			,    **_SCREAMING_SNAKE_CASE					:Any)					->			List[Any]:






								'''simple docstring'''






								super().__init__(*_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Optional[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()))

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




		def 	_snake_case	(  self					:List[Any]			,    **_SCREAMING_SNAKE_CASE					:Tuple)					->			Tuple:






								'''simple docstring'''






								return self.model(**_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:Optional[int]			,    **_SCREAMING_SNAKE_CASE					:Tuple)					->			Tuple:






								'''simple docstring'''






								if self.framework == "pt":
														UpperCamelCase_     :	Optional[int]					     =     []
														UpperCamelCase_     :	Union[str, Any]					     =     []
														UpperCamelCase_     :	int					     =     None
														UpperCamelCase_     :	Dict					     =     inputs["""input_ids"""].shape[0]

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

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

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

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

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

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

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

																				all_logits.append(_SCREAMING_SNAKE_CASE)

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

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

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

														UpperCamelCase_     :	str					     =     torch.cat(tuple(_SCREAMING_SNAKE_CASE)			,    0)

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

														UpperCamelCase_     :	List[str]					     =     inputs["""input_ids"""]
														UpperCamelCase_     :	int					     =     inputs["""attention_mask"""]
														UpperCamelCase_     :	Optional[Any]					     =     inputs["""token_type_ids"""].numpy()
														UpperCamelCase_     :	Dict					     =     None

														for index in range(_SCREAMING_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_     :	Optional[int]					     =     token_type_ids_example[:, 3]  # shape (seq_len,)
																										UpperCamelCase_     :	Optional[Any]					     =     np.zeros_like(_SCREAMING_SNAKE_CASE			,    dtype=np.intaa)  # shape (seq_len,)

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

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

																										UpperCamelCase_     :	Optional[int]					     =     model_labels

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

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

																				all_logits.append(_SCREAMING_SNAKE_CASE)

																				UpperCamelCase_     :	List[Any]					     =     tfp.distributions.Bernoulli(logits=_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     :	str					     =     dist_per_token.probs_parameter() * tf.cast(_SCREAMING_SNAKE_CASE			,    tf.floataa)

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

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

														UpperCamelCase_     :	Optional[Any]					     =     tf.concat(tuple(_SCREAMING_SNAKE_CASE)			,    0)

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




		def __call__(  self					:Union[str, Any]			,    *_SCREAMING_SNAKE_CASE					:str			,    **_SCREAMING_SNAKE_CASE					:Any)					->			List[str]:






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     self._args_parser(*_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	List[str]					     =     super().__call__(_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)
								if len(_SCREAMING_SNAKE_CASE) == 1:
														return results[0]
								return results




		def 	_snake_case	(  self					:Any			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]=None			,    _SCREAMING_SNAKE_CASE					:Dict=None			,    _SCREAMING_SNAKE_CASE					:Optional[int]=None			,    **_SCREAMING_SNAKE_CASE					:List[str])					->			Tuple:






								'''simple docstring'''






								UpperCamelCase_     :	Any					     =     {}
								if padding is not None:
														UpperCamelCase_     :	Tuple					     =     padding
								if truncation is not None:
														UpperCamelCase_     :	Tuple					     =     truncation

								UpperCamelCase_     :	Optional[int]					     =     {}
								if sequential is not None:
														UpperCamelCase_     :	Tuple					     =     sequential
								return preprocess_params, forward_params, {}




		def 	_snake_case	(  self					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:str=None			,    _SCREAMING_SNAKE_CASE					:Tuple=True			,    _SCREAMING_SNAKE_CASE					:str=None)					->			int:






								'''simple docstring'''






								if truncation is None:
														if self.type == "tapas":
																				UpperCamelCase_     :	List[Any]					     =     """drop_rows_to_fit"""
														else:
																				UpperCamelCase_     :	Union[str, Any]					     =     """do_not_truncate"""

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




		def 	_snake_case	(  self					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:List[str]=False)					->			List[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	List[Any]					     =     model_inputs.pop("""table""")

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





		def 	_snake_case	(  self					:Tuple			,    _SCREAMING_SNAKE_CASE					:Optional[int])					->			Any:






								'''simple docstring'''






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

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

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

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




