'''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    lowerCamelCase__  (   __snake_case     ):



   def __call__(    self					,      lowerCAmelCase__=None					,      lowerCAmelCase__=None					,      **lowerCAmelCase__     )				->     List[Any]:
        """simple docstring"""





        requires_backends(self					,      """pandas"""     )
        import pandas as pd

        if table is None:
             raise ValueError("""Keyword argument `table` cannot be None."""     )
        elif query is None:
             if isinstance(lowerCamelCase__					,      lowerCamelCase__     ) and table.get("""query"""     ) is not None and table.get("""table"""     ) is not None:
                  _UpperCamelCase  :Dict       =[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  :Union[str, 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(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  :Dict       =[{'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  :int       =pd.DataFrame(tqa_pipeline_input["""table"""]     )

        return tqa_pipeline_inputs




@add_end_docstrings(__snake_case     )
class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						"""table,query"""



   def __init__(    self					,      lowerCAmelCase__=TableQuestionAnsweringArgumentHandler()					,      *lowerCAmelCase__					,      **lowerCAmelCase__     )				->     List[str]:
        """simple docstring"""





        super().__init__(*lowerCamelCase__					,      **lowerCamelCase__     )
        _UpperCamelCase  :List[Any]       =args_parser

        self.check_model_type(
            dict(
                TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
                + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()     )
            if self.framework == """tf"""
            else dict(
                MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()     )     )

        _UpperCamelCase  :List[str]       =bool(getattr(self.model.config					,      """aggregation_labels"""					,      lowerCamelCase__     )     ) and bool(
            getattr(self.model.config					,      """num_aggregation_labels"""					,      lowerCamelCase__     )     )
        _UpperCamelCase  :Optional[int]       ='tapas' if hasattr(self.model.config					,      """aggregation_labels"""     ) else None



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     str:
        """simple docstring"""





        return self.model(**lowerCamelCase__     )



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     List[Any]:
        """simple docstring"""





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

             _UpperCamelCase  :Optional[int]       =inputs['input_ids'].to(self.device     )
             _UpperCamelCase  :Any       =inputs['attention_mask'].to(self.device     )
             _UpperCamelCase  :List[str]       =inputs['token_type_ids'].to(self.device     )
             _UpperCamelCase  :Tuple       =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  :Optional[int]       =token_type_ids_example[:, 3]  # shape (seq_len,)
                       _UpperCamelCase  :Tuple       =np.zeros_like(prev_labels_example.cpu().numpy()     )  # shape (seq_len,)

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

                            if row_id >= 0 and col_id >= 0 and segment_id == 1:
                                 _UpperCamelCase  :Union[str, Any]       =int(prev_answers[(col_id, row_id)]     )

                       _UpperCamelCase  :str       =torch.from_numpy(lowerCamelCase__     ).type(torch.long     ).to(self.device     )

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

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

                  all_logits.append(lowerCamelCase__     )

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

                  _UpperCamelCase  :Union[str, Any]       =collections.defaultdict(lowerCamelCase__     )
                  for i, p in enumerate(probabilities.squeeze().tolist()     ):
                       _UpperCamelCase  :List[Any]       =token_type_ids_example[:, 0].tolist()[i]
                       _UpperCamelCase  :int       =token_type_ids_example[:, 1].tolist()[i] - 1
                       _UpperCamelCase  :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(lowerCamelCase__     )

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

             _UpperCamelCase  :str       =torch.cat(tuple(lowerCamelCase__     )					,      0     )

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

             _UpperCamelCase  :List[str]       =inputs['input_ids']
             _UpperCamelCase  :Union[str, Any]       =inputs['attention_mask']
             _UpperCamelCase  :Any       =inputs['token_type_ids'].numpy()
             _UpperCamelCase  :str       =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  :List[str]       =token_type_ids_example[:, 3]  # shape (seq_len,)
                       _UpperCamelCase  :str       =np.zeros_like(lowerCamelCase__					,      dtype=np.intaa     )  # shape (seq_len,)

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

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

                       _UpperCamelCase  :str       =model_labels

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

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

                  all_logits.append(lowerCamelCase__     )

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

                  _UpperCamelCase  :str       =collections.defaultdict(lowerCamelCase__     )
                  _UpperCamelCase  :Tuple       =token_type_ids_example
                  for i, p in enumerate(tf.squeeze(lowerCamelCase__     ).numpy().tolist()     ):
                       _UpperCamelCase  :Optional[Any]       =token_type_ids_example[:, 0].tolist()[i]
                       _UpperCamelCase  :str       =token_type_ids_example[:, 1].tolist()[i] - 1
                       _UpperCamelCase  :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(lowerCamelCase__     )

                  _UpperCamelCase  :Union[str, Any]       ={key: np.array(coords_to_probs[key]     ).mean() > 0.5 for key in coords_to_probs}

             _UpperCamelCase  :Optional[int]       =tf.concat(tuple(lowerCamelCase__     )					,      0     )

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



   def __call__(    self					,      *lowerCAmelCase__					,      **lowerCAmelCase__     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :Any       =self._args_parser(*lowerCamelCase__					,      **lowerCamelCase__     )

        _UpperCamelCase  :Any       =super().__call__(lowerCamelCase__					,      **lowerCamelCase__     )
        if len(lowerCamelCase__     ) == 1:
             return results[0]
        return results



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__=None					,      lowerCAmelCase__=None					,      lowerCAmelCase__=None					,      **lowerCAmelCase__     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       ={}
        if padding is not None:
             _UpperCamelCase  :int       =padding
        if truncation is not None:
             _UpperCamelCase  :List[str]       =truncation

        _UpperCamelCase  :Tuple       ={}
        if sequential is not None:
             _UpperCamelCase  :List[Any]       =sequential
        return preprocess_params, forward_params, {}



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=None					,      lowerCAmelCase__=True					,      lowerCAmelCase__=None     )				->     Any:
        """simple docstring"""





        if truncation is None:
             if self.type == "tapas":
                  _UpperCamelCase  :Dict       ='drop_rows_to_fit'
             else:
                  _UpperCamelCase  :Any       ='do_not_truncate'

        _UpperCamelCase  :Optional[int]       =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  :Tuple       =self.tokenizer(lowerCamelCase__					,      lowerCamelCase__					,      return_tensors=self.framework					,      truncation=lowerCamelCase__					,      padding=lowerCamelCase__     )
        _UpperCamelCase  :List[Any]       =table
        return inputs



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=False     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Optional[Any]       =model_inputs.pop("""table"""     )

        if self.type == "tapas":
             if sequential:
                  _UpperCamelCase  :Tuple       =self.sequential_inference(**lowerCamelCase__     )
             else:
                  _UpperCamelCase  :Optional[Any]       =self.batch_inference(**lowerCamelCase__     )
        else:
             _UpperCamelCase  :str       =self.model.generate(**lowerCamelCase__     )
        _UpperCamelCase  :Tuple       ={'model_inputs': model_inputs, 'table': table, 'outputs': outputs}
        return model_outputs




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





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

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

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

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







from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
A_			:Union[str, Any]	    =		load_iris()
iris.keys()


print(f"Target names: \n {iris.target_names} ")
print(f"\n Features: \n {iris.feature_names}")

# Train set e Test set
A_    ,A_    ,A_    ,A_			:Tuple	    =		train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

A_			:Any	    =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_			:Optional[Any]	    =		[[1, 2, 1, 4], [2, 3, 4, 5]]

A_			:Union[str, Any]	    =		knn.predict(X_new)

print(
    f"\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"
    f" {iris['target_names'][prediction]}"
)

