
"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


_a									=				logging.get_logger(__name__)


class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		int    	=						["""pixel_values"""]







  def __init__(						self							,  lowercase_ = True							,  lowercase_ = None							,  lowercase_ = PIL.Image.NEAREST							,  lowercase_ = False							,  lowercase_ = None							,  lowercase_ = 1 / 255							,  lowercase_ = False							,  lowercase_ = True							,  lowercase_ = True							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = True							,  **lowercase_							,  ):

     """simple docstring"""
     super().__init__(**lowercase_    )
     UpperCAmelCase_   :   List[str]     	=       size if size is not None else {"height": 346, "width": 346}
     UpperCAmelCase_   :   Optional[int]     	=       get_size_dict(lowercase_    )
     UpperCAmelCase_   :   str     	=       crop_size if crop_size is not None else {"height": 289, "width": 289}
     UpperCAmelCase_   :   str     	=       get_size_dict(lowercase_							,  param_name="crop_size"    )

     UpperCAmelCase_   :   Any     	=       do_resize
     UpperCAmelCase_   :   Tuple     	=       size
     UpperCAmelCase_   :   Union[str, Any]     	=       resample
     UpperCAmelCase_   :   Dict     	=       do_center_crop
     UpperCAmelCase_   :   Optional[Any]     	=       crop_size
     UpperCAmelCase_   :   Dict     	=       do_rescale
     UpperCAmelCase_   :   Dict     	=       rescale_factor
     UpperCAmelCase_   :   List[str]     	=       rescale_offset
     UpperCAmelCase_   :   Optional[int]     	=       do_normalize
     UpperCAmelCase_   :   Optional[Any]     	=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     UpperCAmelCase_   :   Union[str, Any]     	=       image_std if image_std is not None else IMAGENET_STANDARD_STD
     UpperCAmelCase_   :   str     	=       include_top







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_ = PIL.Image.NEAREST							,  lowercase_ = None							,  **lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       get_size_dict(lowercase_    )
     if "height" not in size or "width" not in size:
        raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""    )
     return resize(
         lowercase_							,  size=(size["height"], size["width"])							,  resample=lowercase_							,  data_format=lowercase_							,  **lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_ = None							,  **lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       get_size_dict(lowercase_    )
     if "height" not in size or "width" not in size:
        raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""    )
     return center_crop(lowercase_							,  size=(size["height"], size["width"])							,  data_format=lowercase_							,  **lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_ = True							,  lowercase_ = None							,  **lowercase_							,  ):

     """simple docstring"""
     if offset:
        UpperCAmelCase_   :   Optional[int]     	=       (image - 1_27.5) * scale
        if data_format is not None:
           UpperCAmelCase_   :   int     	=       to_channel_dimension_format(lowercase_							,  lowercase_    )
        UpperCAmelCase_   :   Optional[int]     	=       rescaled_image.astype(np.floataa    )
     else:
        UpperCAmelCase_   :   List[str]     	=       rescale(lowercase_							,  scale=lowercase_							,  data_format=lowercase_							,  **lowercase_    )
     return rescaled_image







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_ = None							,  **lowercase_							,  ):

     """simple docstring"""
     return normalize(lowercase_							,  mean=lowercase_							,  std=lowercase_							,  data_format=lowercase_							,  **lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_=None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = ChannelDimension.FIRST							,  **lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       do_resize if do_resize is not None else self.do_resize
     UpperCAmelCase_   :   Optional[int]     	=       resample if resample is not None else self.resample
     UpperCAmelCase_   :   int     	=       do_center_crop if do_center_crop is not None else self.do_center_crop
     UpperCAmelCase_   :   Tuple     	=       do_rescale if do_rescale is not None else self.do_rescale
     UpperCAmelCase_   :   List[str]     	=       rescale_factor if rescale_factor is not None else self.rescale_factor
     UpperCAmelCase_   :   str     	=       rescale_offset if rescale_offset is not None else self.rescale_offset
     UpperCAmelCase_   :   str     	=       do_normalize if do_normalize is not None else self.do_normalize
     UpperCAmelCase_   :   List[str]     	=       image_mean if image_mean is not None else self.image_mean
     UpperCAmelCase_   :   Any     	=       image_std if image_std is not None else self.image_std
     UpperCAmelCase_   :   List[Any]     	=       include_top if include_top is not None else self.include_top

     UpperCAmelCase_   :   List[str]     	=       size if size is not None else self.size
     UpperCAmelCase_   :   Optional[int]     	=       get_size_dict(lowercase_    )
     UpperCAmelCase_   :   int     	=       crop_size if crop_size is not None else self.crop_size
     UpperCAmelCase_   :   Tuple     	=       get_size_dict(lowercase_							,  param_name="crop_size"    )

     UpperCAmelCase_   :   List[Any]     	=       make_list_of_images(lowercase_    )

     if not valid_images(lowercase_    ):
        raise ValueError(
            "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
            "torch.Tensor, tf.Tensor or jax.ndarray."    )

     if do_resize and size is None or resample is None:
        raise ValueError("Size and resample must be specified if do_resize is True."    )

     if do_center_crop and crop_size is None:
        raise ValueError("Crop size must be specified if do_center_crop is True."    )

     if do_rescale and rescale_factor is None:
        raise ValueError("Rescale factor must be specified if do_rescale is True."    )

     if do_normalize and (image_mean is None or image_std is None):
        raise ValueError("Image mean and std must be specified if do_normalize is True."    )

     # All transformations expect numpy arrays.
     UpperCAmelCase_   :   Any     	=       [to_numpy_array(lowercase_    ) for image in images]

     if do_resize:
        UpperCAmelCase_   :   str     	=       [self.resize(image=lowercase_							,  size=lowercase_							,  resample=lowercase_    ) for image in images]

     if do_center_crop:
        UpperCAmelCase_   :   Dict     	=       [self.center_crop(image=lowercase_							,  size=lowercase_    ) for image in images]

     if do_rescale:
        UpperCAmelCase_   :   Union[str, Any]     	=       [self.rescale(image=lowercase_							,  scale=lowercase_							,  offset=lowercase_    ) for image in images]

     if do_normalize:
        UpperCAmelCase_   :   Optional[Any]     	=       [self.normalize(image=lowercase_							,  mean=lowercase_							,  std=lowercase_    ) for image in images]

     if include_top:
        UpperCAmelCase_   :   Optional[int]     	=       [self.normalize(image=lowercase_							,  mean=[0, 0, 0]							,  std=lowercase_    ) for image in images]

     UpperCAmelCase_   :   str     	=       [to_channel_dimension_format(lowercase_							,  lowercase_    ) for image in images]

     UpperCAmelCase_   :   List[Any]     	=       {"pixel_values": images}
     return BatchFeature(data=lowercase_							,  tensor_type=lowercase_    )




"""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 			_lowercase  (							__UpperCAmelCase						):




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

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

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

			return tqa_pipeline_inputs






@add_end_docstrings(__UpperCAmelCase						)
class 			_lowercase  (							__UpperCAmelCase						):
	lowercase_								=      'table,query'




	def __init__(					self						,					UpperCAmelCase_=TableQuestionAnsweringArgumentHandler()						,					*UpperCAmelCase_						,					**UpperCAmelCase_   )						->				int:
			super().__init__(*UpperCAmelCase_						,					**UpperCAmelCase_   )
			lowerCamelCase	:     List[str]            =       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()   )   )

			lowerCamelCase	:     Dict            =       bool(getattr(self.model.config						,					'aggregation_labels'						,					UpperCAmelCase_   )   ) and bool(
			    getattr(self.model.config						,					'num_aggregation_labels'						,					UpperCAmelCase_   )   )
			lowerCamelCase	:     str            =       'tapas' if hasattr(self.model.config						,					'aggregation_labels'   ) else None




	def      _UpperCamelCase       (					self						,					**UpperCAmelCase_   )						->				Any:
			return self.model(**UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					**UpperCAmelCase_   )						->				Dict:
			if self.framework == "pt":
					lowerCamelCase	:     List[Any]            =       []
					lowerCamelCase	:     Optional[Any]            =       []
					lowerCamelCase	:     List[str]            =       None
					lowerCamelCase	:     List[str]            =       inputs['input_ids'].shape[0]

					lowerCamelCase	:     Any            =       inputs['input_ids'].to(self.device   )
					lowerCamelCase	:     Optional[Any]            =       inputs['attention_mask'].to(self.device   )
					lowerCamelCase	:     Optional[int]            =       inputs['token_type_ids'].to(self.device   )
					lowerCamelCase	:     str            =       None

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

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

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

									lowerCamelCase	:     Optional[int]            =       torch.from_numpy(UpperCAmelCase_   ).type(torch.long   ).to(self.device   )

							lowerCamelCase	:     List[Any]            =       input_ids[index]
							lowerCamelCase	:     Optional[int]            =       attention_mask[index]  # shape (seq_len,)
							lowerCamelCase	:     List[str]            =       token_type_ids[index]  # shape (seq_len, 7)
							lowerCamelCase	:     Tuple            =       self.model(
							    input_ids=input_ids_example.unsqueeze(0   )						,					attention_mask=attention_mask_example.unsqueeze(0   )						,					token_type_ids=token_type_ids_example.unsqueeze(0   )						,					)
							lowerCamelCase	:     str            =       outputs.logits

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

							all_logits.append(UpperCAmelCase_   )

							lowerCamelCase	:     Dict            =       torch.distributions.Bernoulli(logits=UpperCAmelCase_   )
							lowerCamelCase	:     Tuple            =       dist_per_token.probs * attention_mask_example.type(torch.floataa   ).to(
							    dist_per_token.probs.device   )

							lowerCamelCase	:     str            =       collections.defaultdict(UpperCAmelCase_   )
							for i, p in enumerate(probabilities.squeeze().tolist()   ):
									lowerCamelCase	:     Tuple            =       token_type_ids_example[:, 0].tolist()[i]
									lowerCamelCase	:     Dict            =       token_type_ids_example[:, 1].tolist()[i] - 1
									lowerCamelCase	:     List[Any]            =       token_type_ids_example[:, 2].tolist()[i] - 1
									if col >= 0 and row >= 0 and segment_id == 1:
											coords_to_probs[(col, row)].append(UpperCAmelCase_   )

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

					lowerCamelCase	:     int            =       torch.cat(tuple(UpperCAmelCase_   )						,					0   )

					return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(UpperCAmelCase_   )						,					0   ))
			else:
					lowerCamelCase	:     Union[str, Any]            =       []
					lowerCamelCase	:     Union[str, Any]            =       []
					lowerCamelCase	:     Union[str, Any]            =       None
					lowerCamelCase	:     str            =       inputs['input_ids'].shape[0]

					lowerCamelCase	:     List[Any]            =       inputs['input_ids']
					lowerCamelCase	:     str            =       inputs['attention_mask']
					lowerCamelCase	:     Optional[Any]            =       inputs['token_type_ids'].numpy()
					lowerCamelCase	:     Optional[Any]            =       None

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

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

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

									lowerCamelCase	:     Optional[int]            =       model_labels

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

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

							all_logits.append(UpperCAmelCase_   )

							lowerCamelCase	:     int            =       tfp.distributions.Bernoulli(logits=UpperCAmelCase_   )
							lowerCamelCase	:     Dict            =       dist_per_token.probs_parameter() * tf.cast(UpperCAmelCase_						,					tf.floataa   )

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

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

					lowerCamelCase	:     Union[str, Any]            =       tf.concat(tuple(UpperCAmelCase_   )						,					0   )

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




	def __call__(					self						,					*UpperCAmelCase_						,					**UpperCAmelCase_   )						->				Optional[int]:
			lowerCamelCase	:     Optional[Any]            =       self._args_parser(*UpperCAmelCase_						,					**UpperCAmelCase_   )

			lowerCamelCase	:     Union[str, Any]            =       super().__call__(UpperCAmelCase_						,					**UpperCAmelCase_   )
			if len(UpperCAmelCase_   ) == 1:
					return results[0]
			return results




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_=None						,					UpperCAmelCase_=None						,					UpperCAmelCase_=None						,					**UpperCAmelCase_   )						->				Union[str, Any]:
			lowerCamelCase	:     List[Any]            =       {}
			if padding is not None:
					lowerCamelCase	:     Union[str, Any]            =       padding
			if truncation is not None:
					lowerCamelCase	:     Dict            =       truncation

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




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_=None						,					UpperCAmelCase_=True						,					UpperCAmelCase_=None   )						->				str:
			if truncation is None:
					if self.type == "tapas":
							lowerCamelCase	:     Any            =       'drop_rows_to_fit'
					else:
							lowerCamelCase	:     str            =       'do_not_truncate'

			lowerCamelCase      ,       lowerCamelCase	:     List[str]            =       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'   )
			lowerCamelCase	:     Union[str, Any]            =       self.tokenizer(UpperCAmelCase_						,					UpperCAmelCase_						,					return_tensors=self.framework						,					truncation=UpperCAmelCase_						,					padding=UpperCAmelCase_   )
			lowerCamelCase	:     int            =       table
			return inputs




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_=False   )						->				Optional[Any]:
			lowerCamelCase	:     Optional[Any]            =       model_inputs.pop('table'   )

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




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

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

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

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






