




"""simple docstring"""




import unittest

from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      YosoForMaskedLM,
      YosoForMultipleChoice,
      YosoForQuestionAnswering,
      YosoForSequenceClassification,
      YosoForTokenClassification,
      YosoModel,
  )
  from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST
class      _UpperCamelCase :
      def __init__(     self				:int	, lowerCamelCase				:int	, lowerCamelCase				:Optional[Any]=13	, lowerCamelCase				:Optional[Any]=7	, lowerCamelCase				:Union[str, Any]=True	, lowerCamelCase				:Tuple=True	, lowerCamelCase				:Optional[Any]=True	, lowerCamelCase				:str=True	, lowerCamelCase				:Any=99	, lowerCamelCase				:Optional[int]=32	, lowerCamelCase				:Any=5	, lowerCamelCase				:List[Any]=4	, lowerCamelCase				:Optional[Any]=37	, lowerCamelCase				:Tuple="gelu"	, lowerCamelCase				:Optional[int]=0.1	, lowerCamelCase				:Any=0.1	, lowerCamelCase				:List[Any]=512	, lowerCamelCase				:Tuple=16	, lowerCamelCase				:Optional[int]=2	, lowerCamelCase				:str=0.02	, lowerCamelCase				:Dict=3	, lowerCamelCase				:Union[str, Any]=4	, lowerCamelCase				:Union[str, Any]=None	, )       ->     List[Any]:
       UpperCAmelCase__								=  parent
       UpperCAmelCase__								=  batch_size
       UpperCAmelCase__								=  seq_length
       UpperCAmelCase__								=  is_training
       UpperCAmelCase__								=  use_input_mask
       UpperCAmelCase__								=  use_token_type_ids
       UpperCAmelCase__								=  use_labels
       UpperCAmelCase__								=  vocab_size
       UpperCAmelCase__								=  hidden_size
       UpperCAmelCase__								=  num_hidden_layers
       UpperCAmelCase__								=  num_attention_heads
       UpperCAmelCase__								=  intermediate_size
       UpperCAmelCase__								=  hidden_act
       UpperCAmelCase__								=  hidden_dropout_prob
       UpperCAmelCase__								=  attention_probs_dropout_prob
       UpperCAmelCase__								=  max_position_embeddings
       UpperCAmelCase__								=  type_vocab_size
       UpperCAmelCase__								=  type_sequence_label_size
       UpperCAmelCase__								=  initializer_range
       UpperCAmelCase__								=  num_labels
       UpperCAmelCase__								=  num_choices
       UpperCAmelCase__								=  scope
      def 		UpperCAmelCase_  (     self				:Union[str, Any] )       ->     Any:
       UpperCAmelCase__								=  ids_tensor([self.batch_size, self.seq_length]	, self.vocab_size )

       UpperCAmelCase__								=  None
       if self.use_input_mask:
        UpperCAmelCase__								=  random_attention_mask([self.batch_size, self.seq_length] )

       UpperCAmelCase__								=  None
       if self.use_token_type_ids:
        UpperCAmelCase__								=  ids_tensor([self.batch_size, self.seq_length]	, self.type_vocab_size )

       UpperCAmelCase__								=  None
       UpperCAmelCase__								=  None
       UpperCAmelCase__								=  None
       if self.use_labels:
        UpperCAmelCase__								=  ids_tensor([self.batch_size]	, self.type_sequence_label_size )
        UpperCAmelCase__								=  ids_tensor([self.batch_size, self.seq_length]	, self.num_labels )
        UpperCAmelCase__								=  ids_tensor([self.batch_size]	, self.num_choices )

       UpperCAmelCase__								=  self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def 		UpperCAmelCase_  (     self				:List[Any] )       ->     Optional[Any]:
       return YosoConfig(
           vocab_size=self.vocab_size	, hidden_size=self.hidden_size	, num_hidden_layers=self.num_hidden_layers	, num_attention_heads=self.num_attention_heads	, intermediate_size=self.intermediate_size	, hidden_act=self.hidden_act	, hidden_dropout_prob=self.hidden_dropout_prob	, attention_probs_dropout_prob=self.attention_probs_dropout_prob	, max_position_embeddings=self.max_position_embeddings	, type_vocab_size=self.type_vocab_size	, is_decoder=lowerCamelCase	, initializer_range=self.initializer_range	, )
      def 		UpperCAmelCase_  (     self				:int )       ->     Any:
       UpperCAmelCase__								=  self.get_config()
       UpperCAmelCase__								=  300
       return config
      def 		UpperCAmelCase_  (     self				:List[str] )       ->     Tuple:
       (
           (
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,		
       )								=  self.prepare_config_and_inputs()

       UpperCAmelCase__								=  True
       UpperCAmelCase__								=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
       UpperCAmelCase__								=  ids_tensor([self.batch_size, self.seq_length]	, vocab_size=2 )

       return (
           config,
           input_ids,
           token_type_ids,
           input_mask,
           sequence_labels,
           token_labels,
           choice_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )
      def 		UpperCAmelCase_  (     self				:Any	, lowerCamelCase				:List[str]	, lowerCamelCase				:List[str]	, lowerCamelCase				:str	, lowerCamelCase				:Union[str, Any]	, lowerCamelCase				:Optional[int]	, lowerCamelCase				:Any	, lowerCamelCase				:Tuple )       ->     List[Any]:
       UpperCAmelCase__								=  YosoModel(config=lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  model(lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase )
       UpperCAmelCase__								=  model(lowerCamelCase	, token_type_ids=lowerCamelCase )
       UpperCAmelCase__								=  model(lowerCamelCase )
       self.parent.assertEqual(result.last_hidden_state.shape	, (self.batch_size, self.seq_length, self.hidden_size) )
      def 		UpperCAmelCase_  (     self				:Any	, lowerCamelCase				:str	, lowerCamelCase				:Dict	, lowerCamelCase				:Any	, lowerCamelCase				:Any	, lowerCamelCase				:int	, lowerCamelCase				:Dict	, lowerCamelCase				:Any	, lowerCamelCase				:Union[str, Any]	, lowerCamelCase				:List[Any]	, )       ->     Optional[int]:
       UpperCAmelCase__								=  True
       UpperCAmelCase__								=  YosoModel(lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  model(
           lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase	, encoder_hidden_states=lowerCamelCase	, encoder_attention_mask=lowerCamelCase	, )
       UpperCAmelCase__								=  model(
           lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase	, encoder_hidden_states=lowerCamelCase	, )
       UpperCAmelCase__								=  model(lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase )
       self.parent.assertEqual(result.last_hidden_state.shape	, (self.batch_size, self.seq_length, self.hidden_size) )
      def 		UpperCAmelCase_  (     self				:Tuple	, lowerCamelCase				:List[Any]	, lowerCamelCase				:Tuple	, lowerCamelCase				:List[str]	, lowerCamelCase				:Optional[Any]	, lowerCamelCase				:Tuple	, lowerCamelCase				:Optional[Any]	, lowerCamelCase				:int )       ->     Optional[int]:
       UpperCAmelCase__								=  YosoForMaskedLM(config=lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  model(lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase	, labels=lowerCamelCase )
       self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.vocab_size) )
      def 		UpperCAmelCase_  (     self				:Dict	, lowerCamelCase				:str	, lowerCamelCase				:Dict	, lowerCamelCase				:List[Any]	, lowerCamelCase				:Optional[int]	, lowerCamelCase				:Union[str, Any]	, lowerCamelCase				:int	, lowerCamelCase				:Optional[Any] )       ->     Any:
       UpperCAmelCase__								=  YosoForQuestionAnswering(config=lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  model(
           lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase	, start_positions=lowerCamelCase	, end_positions=lowerCamelCase	, )
       self.parent.assertEqual(result.start_logits.shape	, (self.batch_size, self.seq_length) )
       self.parent.assertEqual(result.end_logits.shape	, (self.batch_size, self.seq_length) )
      def 		UpperCAmelCase_  (     self				:Optional[Any]	, lowerCamelCase				:Optional[Any]	, lowerCamelCase				:Any	, lowerCamelCase				:List[str]	, lowerCamelCase				:Dict	, lowerCamelCase				:str	, lowerCamelCase				:Tuple	, lowerCamelCase				:int )       ->     Dict:
       UpperCAmelCase__								=  self.num_labels
       UpperCAmelCase__								=  YosoForSequenceClassification(lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  model(lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase	, labels=lowerCamelCase )
       self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.num_labels) )
      def 		UpperCAmelCase_  (     self				:Tuple	, lowerCamelCase				:str	, lowerCamelCase				:int	, lowerCamelCase				:Optional[Any]	, lowerCamelCase				:List[str]	, lowerCamelCase				:Dict	, lowerCamelCase				:Optional[int]	, lowerCamelCase				:str )       ->     Dict:
       UpperCAmelCase__								=  self.num_labels
       UpperCAmelCase__								=  YosoForTokenClassification(config=lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  model(lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase	, labels=lowerCamelCase )
       self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.num_labels) )
      def 		UpperCAmelCase_  (     self				:Optional[int]	, lowerCamelCase				:Tuple	, lowerCamelCase				:Tuple	, lowerCamelCase				:int	, lowerCamelCase				:Tuple	, lowerCamelCase				:List[str]	, lowerCamelCase				:Optional[Any]	, lowerCamelCase				:List[str] )       ->     List[Any]:
       UpperCAmelCase__								=  self.num_choices
       UpperCAmelCase__								=  YosoForMultipleChoice(config=lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  input_ids.unsqueeze(1 ).expand(-1	, self.num_choices	, -1 ).contiguous()
       UpperCAmelCase__								=  token_type_ids.unsqueeze(1 ).expand(-1	, self.num_choices	, -1 ).contiguous()
       UpperCAmelCase__								=  input_mask.unsqueeze(1 ).expand(-1	, self.num_choices	, -1 ).contiguous()
       UpperCAmelCase__								=  model(
           lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase	, labels=lowerCamelCase	, )
       self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.num_choices) )







      def 		UpperCAmelCase_  (     self				:int )       ->     List[Any]:
       UpperCAmelCase__								=  self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,		
       )								=  config_and_inputs
       UpperCAmelCase__								=  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict



@require_torch
class      _UpperCamelCase (			lowerCAmelCase		,    lowerCAmelCase		,    unittest.TestCase						):
      UpperCAmelCase_            =						(
          (
              YosoModel,
              YosoForMaskedLM,
              YosoForMultipleChoice,
              YosoForQuestionAnswering,
              YosoForSequenceClassification,
              YosoForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      UpperCAmelCase_            =						False
      UpperCAmelCase_            =						False
      UpperCAmelCase_            =						False

      UpperCAmelCase_            =						()
      UpperCAmelCase_            =						(
          {
              """feature-extraction""": YosoModel,
              """fill-mask""": YosoForMaskedLM,
              """question-answering""": YosoForQuestionAnswering,
              """text-classification""": YosoForSequenceClassification,
              """token-classification""": YosoForTokenClassification,
              """zero-shot""": YosoForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      def 		UpperCAmelCase_  (     self				:Any )       ->     Optional[Any]:
       UpperCAmelCase__								=  YosoModelTester(self )
       UpperCAmelCase__								=  ConfigTester(self	, config_class=lowerCamelCase	, hidden_size=37 )
      def 		UpperCAmelCase_  (     self				:List[Any] )       ->     List[str]:
       self.config_tester.run_common_tests()
      def 		UpperCAmelCase_  (     self				:Optional[int] )       ->     List[Any]:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Tuple )       ->     Optional[Any]:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
        UpperCAmelCase__								=  type
        self.model_tester.create_and_check_model(*lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Tuple )       ->     Optional[int]:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Union[str, Any] )       ->     Dict:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:str )       ->     Optional[Any]:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Any )       ->     List[str]:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Any )       ->     str:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*lowerCamelCase )
      @slow
      def 		UpperCAmelCase_  (     self				:List[Any] )       ->     Optional[int]:
       for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase__								=  YosoModel.from_pretrained(lowerCamelCase )
        self.assertIsNotNone(lowerCamelCase )







      def 		UpperCAmelCase_  (     self				:int )       ->     List[str]:
       return



@require_torch
class      _UpperCamelCase (			unittest.TestCase						):
      @slow
      def 		UpperCAmelCase_  (     self				:Optional[Any] )       ->     Optional[int]:
       UpperCAmelCase__								=  YosoModel.from_pretrained("uw-madison/yoso-4096" )
       UpperCAmelCase__								=  torch.tensor([[0, 1, 2, 3, 4, 5]] )

       with torch.no_grad():
        UpperCAmelCase__								=  model(lowerCamelCase )[0]

       UpperCAmelCase__								=  torch.Size((1, 6, 768) )
       self.assertEqual(output.shape	, lowerCamelCase )

       UpperCAmelCase__								=  torch.tensor(
           [[[-0.06_11, 0.12_42, 0.08_40], [0.02_80, -0.00_48, 0.11_25], [0.01_06, 0.02_26, 0.07_51]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]	, lowerCamelCase	, atol=1e-4 ) )
      @slow
      def 		UpperCAmelCase_  (     self				:Optional[Any] )       ->     List[Any]:
       UpperCAmelCase__								=  YosoForMaskedLM.from_pretrained("uw-madison/yoso-4096" )
       UpperCAmelCase__								=  torch.tensor([[0, 1, 2, 3, 4, 5]] )

       with torch.no_grad():
        UpperCAmelCase__								=  model(lowerCamelCase )[0]

       UpperCAmelCase__								=  5_0265

       UpperCAmelCase__								=  torch.Size((1, 6, vocab_size) )
       self.assertEqual(output.shape	, lowerCamelCase )

       UpperCAmelCase__								=  torch.tensor(
           [[[-2.13_13, -3.72_85, -2.24_07], [-2.70_47, -3.33_14, -2.64_08], [0.06_29, -2.51_66, -0.33_56]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]	, lowerCamelCase	, atol=1e-4 ) )







      @slow
      def 		UpperCAmelCase_  (     self				:Any )       ->     str:
       UpperCAmelCase__								=  YosoForMaskedLM.from_pretrained("uw-madison/yoso-4096" )
       UpperCAmelCase__								=  torch.arange(4096 ).unsqueeze(0 )

       with torch.no_grad():
        UpperCAmelCase__								=  model(lowerCamelCase )[0]

       UpperCAmelCase__								=  5_0265

       UpperCAmelCase__								=  torch.Size((1, 4096, vocab_size) )
       self.assertEqual(output.shape	, lowerCamelCase )

       UpperCAmelCase__								=  torch.tensor(
           [[[-2.39_14, -4.37_42, -5.09_56], [-4.09_88, -4.23_84, -7.04_06], [-3.14_27, -3.71_92, -6.68_00]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]	, lowerCamelCase	, atol=1e-4 ) )











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      _UpperCamelCase (			lowerCAmelCase						):
      def __call__(     self				:Optional[int]	, lowerCamelCase				:Tuple=None	, lowerCamelCase				:Tuple=None	, **lowerCamelCase				:str )       ->     Optional[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:
         UpperCAmelCase__								=  [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:
          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(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:
        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(lowerCAmelCase						)
class      _UpperCamelCase (			lowerCAmelCase						):
      UpperCAmelCase_            =						"""table,query"""
      def __init__(     self				:int	, lowerCamelCase				:Any=TableQuestionAnsweringArgumentHandler()	, *lowerCamelCase				:Optional[int]	, **lowerCamelCase				:str )       ->     int:
       super().__init__(*lowerCamelCase	, **lowerCamelCase )
       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"	, lowerCamelCase ) ) and bool(
           getattr(self.model.config	, "num_aggregation_labels"	, lowerCamelCase ) )
       UpperCAmelCase__								=  "tapas" if hasattr(self.model.config	, "aggregation_labels" ) else None
      def 		UpperCAmelCase_  (     self				:int	, **lowerCamelCase				:Dict )       ->     Optional[int]:
       return self.model(**lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Dict	, **lowerCamelCase				:Optional[int] )       ->     Dict:
       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(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:
          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(lowerCamelCase ).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(lowerCamelCase )

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

         UpperCAmelCase__								=  collections.defaultdict(lowerCamelCase )
         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(lowerCamelCase )

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

        UpperCAmelCase__								=  torch.cat(tuple(lowerCamelCase )	, 0 )

        return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(lowerCamelCase )	, 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(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:
          UpperCAmelCase__								=  token_type_ids_example[:, 3]  # shape (seq_len,)
          UpperCAmelCase__								=  np.zeros_like(lowerCamelCase	, 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(lowerCamelCase	, axis=0 )	, attention_mask=np.expand_dims(lowerCamelCase	, axis=0 )	, token_type_ids=np.expand_dims(lowerCamelCase	, axis=0 )	, )
         UpperCAmelCase__								=  outputs.logits

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

         all_logits.append(lowerCamelCase )

         UpperCAmelCase__								=  tfp.distributions.Bernoulli(logits=lowerCamelCase )
         UpperCAmelCase__								=  dist_per_token.probs_parameter() * tf.cast(lowerCamelCase	, tf.floataa )

         UpperCAmelCase__								=  collections.defaultdict(lowerCamelCase )
         UpperCAmelCase__								=  token_type_ids_example
         for i, p in enumerate(tf.squeeze(lowerCamelCase ).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(lowerCamelCase )

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

        UpperCAmelCase__								=  tf.concat(tuple(lowerCamelCase )	, 0 )

        return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(lowerCamelCase )	, 0 ))
      def __call__(     self				:str	, *lowerCamelCase				:List[Any]	, **lowerCamelCase				:List[Any] )       ->     Dict:
       UpperCAmelCase__								=  self._args_parser(*lowerCamelCase	, **lowerCamelCase )

       UpperCAmelCase__								=  super().__call__(lowerCamelCase	, **lowerCamelCase )
       if len(lowerCamelCase ) == 1:
        return results[0]
       return results
      def 		UpperCAmelCase_  (     self				:List[str]	, lowerCamelCase				:int=None	, lowerCamelCase				:Optional[int]=None	, lowerCamelCase				:int=None	, **lowerCamelCase				:Tuple )       ->     Tuple:
       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				:Optional[int]	, lowerCamelCase				:List[Any]	, lowerCamelCase				:Tuple=None	, lowerCamelCase				:Union[str, Any]=True	, lowerCamelCase				:str=None )       ->     Union[str, Any]:
       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(lowerCamelCase	, lowerCamelCase	, return_tensors=self.framework	, truncation=lowerCamelCase	, padding=lowerCamelCase )
       UpperCAmelCase__								=  table
       return inputs
      def 		UpperCAmelCase_  (     self				:Union[str, Any]	, lowerCamelCase				:Optional[int]	, lowerCamelCase				:Optional[Any]=False )       ->     int:
       UpperCAmelCase__								=  model_inputs.pop("table" )

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







      def 		UpperCAmelCase_  (     self				:Any	, lowerCamelCase				:Optional[Any] )       ->     Dict:
       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(lowerCamelCase	, lowerCamelCase	, lowerCamelCase )
         UpperCAmelCase__	,	UpperCAmelCase__								=  predictions
         UpperCAmelCase__								=  {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(lowerCamelCase )}

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

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

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





