


"""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 	A_       (     lowerCAmelCase__   ):





  """simple docstring"""

  def __call__(						self  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					**__UpperCAmelCase						)				->					Optional[Any]:
     requires_backends(self  ,					'pandas'						)
     import pandas as pd

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

           if table[0].get('query'						) is not None and table[0].get('table'						) is not None:
              a   :	Tuple						=    table
           else:
              raise ValueError(
                  'If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`'
                  f' and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys.'						)
        elif Dataset is not None and isinstance(snake_case__  ,					snake_case__						) or isinstance(snake_case__  ,					types.GeneratorType						):
           return table
        else:
           raise ValueError(
               'Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but '
               f'is {type(snake_case__						)})'						)
     else:
        a   :	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.'						)

           a   :	List[str]						=    pd.DataFrame(tqa_pipeline_input['table']						)

     return tqa_pipeline_inputs



@add_end_docstrings(lowerCAmelCase__   )
class 	A_       (     lowerCAmelCase__   ):





  """simple docstring"""

  lowercase		:	Tuple   =						"table,query"
  def __init__(						self  ,					__UpperCAmelCase=TableQuestionAnsweringArgumentHandler()  ,					*__UpperCAmelCase  ,					**__UpperCAmelCase						)				->					List[str]:
     super().__init__(*snake_case__  ,					**snake_case__						)
     a   :	Optional[int]						=    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()						)						)

     a   :	List[Any]						=    bool(getattr(self.model.config  ,					'aggregation_labels'  ,					snake_case__						)						) and bool(
         getattr(self.model.config  ,					'num_aggregation_labels'  ,					snake_case__						)						)
     a   :	str						=    'tapas' if hasattr(self.model.config  ,					'aggregation_labels'						) else None
  def 							lowercase_     (						self  ,					**__UpperCAmelCase						)				->					List[Any]:
     return self.model(**snake_case__						)
  def 							lowercase_     (						self  ,					**__UpperCAmelCase						)				->					Union[str, Any]:





     if self.framework == "pt":
        a   :	Optional[Any]						=    []
        a   :	Any						=    []
        a   :	List[str]						=    None
        a   :	List[str]						=    inputs['input_ids'].shape[0]

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

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

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

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

              a   :	str						=    torch.from_numpy(snake_case__						).type(torch.long						).to(self.device						)

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

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

           all_logits.append(snake_case__						)

           a   :	int						=    torch.distributions.Bernoulli(logits=snake_case__						)
           a   :	str						=    dist_per_token.probs * attention_mask_example.type(torch.floataa						).to(
               dist_per_token.probs.device						)

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

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

        a   :	Optional[int]						=    torch.cat(tuple(snake_case__						)  ,					0						)

        return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(snake_case__						)  ,					0						))
     else:
        a   :	Optional[int]						=    []
        a   :	Any						=    []
        a   :	Optional[Any]						=    None
        a   :	Optional[Any]						=    inputs['input_ids'].shape[0]

        a   :	Optional[Any]						=    inputs['input_ids']
        a   :	int						=    inputs['attention_mask']
        a   :	Tuple						=    inputs['token_type_ids'].numpy()
        a   :	Dict						=    None

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

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

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

              a   :	Any						=    model_labels

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

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

           all_logits.append(snake_case__						)

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

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

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

        a   :	List[Any]						=    tf.concat(tuple(snake_case__						)  ,					0						)

        return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(snake_case__						)  ,					0						))
  def __call__(						self  ,					*__UpperCAmelCase  ,					**__UpperCAmelCase						)				->					int:
     a   :	Optional[Any]						=    self._args_parser(*snake_case__  ,					**snake_case__						)

     a   :	int						=    super().__call__(snake_case__  ,					**snake_case__						)
     if len(snake_case__						) == 1:
        return results[0]
     return results
  def 							lowercase_     (						self  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					**__UpperCAmelCase						)				->					Any:
     a   :	str						=    {}
     if padding is not None:
        a   :	Optional[int]						=    padding
     if truncation is not None:
        a   :	Optional[Any]						=    truncation

     a   :	Dict						=    {}
     if sequential is not None:
        a   :	Any						=    sequential
     return preprocess_params, forward_params, {}
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=True  ,					__UpperCAmelCase=None						)				->					str:





     if truncation is None:
        if self.type == "tapas":
           a   :	List[Any]						=    'drop_rows_to_fit'
        else:
           a   :	List[Any]						=    'do_not_truncate'

     a	, a   :	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'						)
     a   :	List[Any]						=    self.tokenizer(snake_case__  ,					snake_case__  ,					return_tensors=self.framework  ,					truncation=snake_case__  ,					padding=snake_case__						)
     a   :	List[Any]						=    table
     return inputs
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase=False						)				->					Union[str, Any]:
     a   :	int						=    model_inputs.pop('table'						)

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






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

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

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

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







from __future__ import annotations

from typing import Any

class        UpperCamelCase__:

	"""simple docstring"""

	def __init__(      self				:			Dict							,			snake_case__				:			Any							,			snake_case__				:			Any ):  # noqa: A002
			"""simple docstring"""





			A				=item
			A				=next


class        UpperCamelCase__:

	"""simple docstring"""

	def __init__(      self				:			Optional[Any] ):
			"""simple docstring"""





			A				=None
			A				=0







	def        _a					(      self				:			str							,			snake_case__				:			Any ):
			"""simple docstring"""





			A				=Node(snake_case__							,			self.head )
			self.size += 1







	def        _a					(      self				:			Dict ):
			"""simple docstring"""





			if self.head is None:
					return None
			else:
					A				=self.head.item
					A				=self.head.next
					self.size -= 1
					return item







	def        _a					(      self				:			Any ):
			"""simple docstring"""





			return self.head is None







	def __str__(      self				:			Dict ):
			"""simple docstring"""





			if self.is_empty():
					return ""
			else:
					A				=self.head
					A				=""
					A				=[]
					while iterate:
							item_list.append(str(iterate.item ) )
							A				=iterate.next

					A				=" --> ".join(snake_case__ )

					return item_str







	def __len__(      self				:			Union[str, Any] ):
			"""simple docstring"""





			return self.size




