




"""simple docstring"""




import os
import re
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase  :				Optional[Any]						=				logging.get_logger(__name__)
_lowerCAmelCase  :				str						=				{"vocab_file": "spiece.model"}

_lowerCAmelCase  :				Dict						=				{
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
}

_lowerCAmelCase  :				Union[str, Any]						=				{
    "google/fnet-base": 5_1_2,
    "google/fnet-large": 5_1_2,
}

_lowerCAmelCase  :				List[Any]						=				"▁"
class      _UpperCamelCase (			lowerCAmelCase						):
      UpperCAmelCase_            =						VOCAB_FILES_NAMES
      UpperCAmelCase_            =						PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_            =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_            =						['input_ids', 'token_type_ids']
      def __init__(     self				:List[Any]	, lowerCamelCase				:Union[str, Any]	, lowerCamelCase				:Optional[int]=False	, lowerCamelCase				:Union[str, Any]=True	, lowerCamelCase				:str=True	, lowerCamelCase				:Optional[Any]="<unk>"	, lowerCamelCase				:int="[SEP]"	, lowerCamelCase				:Any="<pad>"	, lowerCamelCase				:Dict="[CLS]"	, lowerCamelCase				:int="[MASK]"	, lowerCamelCase				:Optional[Dict[str, Any]] = None	, **lowerCamelCase				:Optional[int]	, )       ->     int:
       UpperCAmelCase__								=  (
           AddedToken(lowerCamelCase	, lstrip=lowerCamelCase	, rstrip=lowerCamelCase	, normalized=lowerCamelCase )
           if isinstance(lowerCamelCase	, lowerCamelCase )
           else mask_token
       )

       UpperCAmelCase__								=  {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           do_lower_case=lowerCamelCase	, remove_space=lowerCamelCase	, keep_accents=lowerCamelCase	, unk_token=lowerCamelCase	, sep_token=lowerCamelCase	, pad_token=lowerCamelCase	, cls_token=lowerCamelCase	, mask_token=lowerCamelCase	, sp_model_kwargs=self.sp_model_kwargs	, **lowerCamelCase	, )

       UpperCAmelCase__								=  do_lower_case
       UpperCAmelCase__								=  remove_space
       UpperCAmelCase__								=  keep_accents
       UpperCAmelCase__								=  vocab_file

       UpperCAmelCase__								=  spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(lowerCamelCase )
      @property
      def 		UpperCAmelCase_  (     self				:Optional[int] )       ->     Dict:
       return len(self.sp_model )
      def 		UpperCAmelCase_  (     self				:str )       ->     Optional[Any]:
       UpperCAmelCase__								=  {self.convert_ids_to_tokens(lowerCamelCase ): i for i in range(self.vocab_size )}
       vocab.update(self.added_tokens_encoder )
       return vocab
      def __getstate__(     self				:Tuple )       ->     int:
       UpperCAmelCase__								=  self.__dict__.copy()
       UpperCAmelCase__								=  None
       return state
      def __setstate__(     self				:Tuple	, lowerCamelCase				:str )       ->     str:
       UpperCAmelCase__								=  d

       # for backward compatibility
       if not hasattr(self	, "sp_model_kwargs" ):
        UpperCAmelCase__								=  {}

       UpperCAmelCase__								=  spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(self.vocab_file )
      def 		UpperCAmelCase_  (     self				:Tuple	, lowerCamelCase				:Dict )       ->     Any:






       if self.remove_space:
        UpperCAmelCase__								=  " ".join(inputs.strip().split() )
       else:
        UpperCAmelCase__								=  inputs
       UpperCAmelCase__								=  outputs.replace("``"	, "\"" ).replace("''"	, "\"" )

       if not self.keep_accents:
        UpperCAmelCase__								=  unicodedata.normalize("NFKD"	, lowerCamelCase )
        UpperCAmelCase__								=  "".join([c for c in outputs if not unicodedata.combining(lowerCamelCase )] )
       if self.do_lower_case:
        UpperCAmelCase__								=  outputs.lower()

       return outputs
      def 		UpperCAmelCase_  (     self				:str	, lowerCamelCase				:str )       ->     int:
       UpperCAmelCase__								=  self.preprocess_text(lowerCamelCase )
       UpperCAmelCase__								=  self.sp_model.encode(lowerCamelCase	, out_type=lowerCamelCase )
       UpperCAmelCase__								=  []
       for piece in pieces:
        if len(lowerCamelCase ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
         UpperCAmelCase__								=  self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCamelCase	, "" ) )
         if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
          if len(cur_pieces[0] ) == 1:
           UpperCAmelCase__								=  cur_pieces[1:]
          else:
           UpperCAmelCase__								=  cur_pieces[0][1:]
         cur_pieces.append(piece[-1] )
         new_pieces.extend(lowerCamelCase )
        else:
         new_pieces.append(lowerCamelCase )

       return new_pieces
      def 		UpperCAmelCase_  (     self				:List[Any]	, lowerCamelCase				:str )       ->     Union[str, Any]:
       return self.sp_model.PieceToId(lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Union[str, Any]	, lowerCamelCase				:Any )       ->     Dict:
       return self.sp_model.IdToPiece(lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:List[Any]	, lowerCamelCase				:List[Any] )       ->     Dict:
       UpperCAmelCase__								=  []
       UpperCAmelCase__								=  ""
       UpperCAmelCase__								=  False
       for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
         if not prev_is_special:
          out_string += " "
         out_string += self.sp_model.decode(lowerCamelCase ) + token
         UpperCAmelCase__								=  True
         UpperCAmelCase__								=  []
        else:
         current_sub_tokens.append(lowerCamelCase )
         UpperCAmelCase__								=  False
       out_string += self.sp_model.decode(lowerCamelCase )
       return out_string.strip()
      def 		UpperCAmelCase_  (     self				:int	, lowerCamelCase				:List[int]	, lowerCamelCase				:bool = False	, lowerCamelCase				:bool = None	, lowerCamelCase				:bool = True	, **lowerCamelCase				:int	, )       ->     Dict:
       UpperCAmelCase__								=  kwargs.pop("use_source_tokenizer"	, lowerCamelCase )

       UpperCAmelCase__								=  self.convert_ids_to_tokens(lowerCamelCase	, skip_special_tokens=lowerCamelCase )

       # To avoid mixing byte-level and unicode for byte-level BPT
       # we need to build string separately for added tokens and byte-level tokens
       # cf. https://github.com/huggingface/transformers/issues/1133
       UpperCAmelCase__								=  []
       UpperCAmelCase__								=  []
       for token in filtered_tokens:
        if skip_special_tokens and token in self.all_special_ids:
         continue
        if token in self.added_tokens_encoder:
         if current_sub_text:
          sub_texts.append(self.convert_tokens_to_string(lowerCamelCase ) )
          UpperCAmelCase__								=  []
         sub_texts.append(lowerCamelCase )
        else:
         current_sub_text.append(lowerCamelCase )
       if current_sub_text:
        sub_texts.append(self.convert_tokens_to_string(lowerCamelCase ) )

       # Mimic the behavior of the Rust tokenizer:
       # No space after <unk>
       if spaces_between_special_tokens:
        UpperCAmelCase__								=  re.sub(r"(<unk>) "	, r"\1"	, " ".join(lowerCamelCase ) )
       else:
        UpperCAmelCase__								=  "".join(lowerCamelCase )

       UpperCAmelCase__								=  (
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
        UpperCAmelCase__								=  self.clean_up_tokenization(lowerCamelCase )
        return clean_text
       else:
        return text
      def 		UpperCAmelCase_  (     self				:Tuple	, lowerCamelCase				:List[int]	, lowerCamelCase				:Optional[List[int]] = None )       ->     str:
       UpperCAmelCase__								=  [self.sep_token_id]
       UpperCAmelCase__								=  [self.cls_token_id]
       if token_ids_a is None:
        return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep
      def 		UpperCAmelCase_  (     self				:Dict	, lowerCamelCase				:List[int]	, lowerCamelCase				:Optional[List[int]] = None	, lowerCamelCase				:bool = False )       ->     Dict:






       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=lowerCamelCase	, token_ids_a=lowerCamelCase	, already_has_special_tokens=lowerCamelCase )

       if token_ids_a is not None:
        return [1] + ([0] * len(lowerCamelCase )) + [1] + ([0] * len(lowerCamelCase )) + [1]
       return [1] + ([0] * len(lowerCamelCase )) + [1]
      def 		UpperCAmelCase_  (     self				:Union[str, Any]	, lowerCamelCase				:List[int]	, lowerCamelCase				:Optional[List[int]] = None )       ->     Dict:
       UpperCAmelCase__								=  [self.sep_token_id]
       UpperCAmelCase__								=  [self.cls_token_id]

       if token_ids_a is None:
        return len(cls + token_ids_a + sep ) * [0]
       return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







      def 		UpperCAmelCase_  (     self				:Dict	, lowerCamelCase				:str	, lowerCamelCase				:Optional[str] = None )       ->     str:






       if not os.path.isdir(lowerCamelCase ):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
        return
       UpperCAmelCase__								=  os.path.join(
           lowerCamelCase	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

       if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase ) and os.path.isfile(self.vocab_file ):
        copyfile(self.vocab_file	, lowerCamelCase )
       elif not os.path.isfile(self.vocab_file ):
        with open(lowerCamelCase	, "wb" ) as fi:
         UpperCAmelCase__								=  self.sp_model.serialized_model_proto()
         fi.write(lowerCamelCase )

       return (out_vocab_file,)











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]





