




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__       (					UpperCamelCase__      ):







     def __call__(	self  ,		A=None  ,		A=None  ,		**A )  ->    Optional[int]:



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

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

          return tqa_pipeline_inputs







@add_end_docstrings(UpperCamelCase__      )
class 			a__       (					UpperCamelCase__      ):
     a		:			Union[str, Any]							=	"""table,query"""







     def __init__(	self  ,		A=TableQuestionAnsweringArgumentHandler()  ,		*A  ,		**A )  ->    Any:



          '''simple docstring'''



          super().__init__(*A  ,		**A )
          a      =      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      =      bool(getattr(self.model.config  ,		"aggregation_labels"  ,		A ) ) and bool(
              getattr(self.model.config  ,		"num_aggregation_labels"  ,		A ) )
          a      =      "tapas" if hasattr(self.model.config  ,		"aggregation_labels" ) else None







     def 			lowerCAmelCase_				(	self  ,		**A )  ->    List[Any]:



          '''simple docstring'''



          return self.model(**A )







     def 			lowerCAmelCase_				(	self  ,		**A )  ->    Tuple:



          '''simple docstring'''



          if self.framework == "pt":
               a      =      []
               a      =      []
               a      =      None
               a      =      inputs["input_ids"].shape[0]

               a      =      inputs["input_ids"].to(self.device )
               a      =      inputs["attention_mask"].to(self.device )
               a      =      inputs["token_type_ids"].to(self.device )
               a      =      None

               for index in range(A ):
                    # 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      =      token_type_ids_example[:, 3]  # shape (seq_len,)
                         a      =      np.zeros_like(prev_labels_example.cpu().numpy() )  # shape (seq_len,)

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

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

                         a      =      torch.from_numpy(A ).type(torch.long ).to(self.device )

                    a      =      input_ids[index]
                    a      =      attention_mask[index]  # shape (seq_len,)
                    a      =      token_type_ids[index]  # shape (seq_len, 7)
                    a      =      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      =      outputs.logits

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

                    all_logits.append(A )

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

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

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

               a      =      torch.cat(tuple(A )  ,		0 )

               return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(A )  ,		0 ))
          else:
               a      =      []
               a      =      []
               a      =      None
               a      =      inputs["input_ids"].shape[0]

               a      =      inputs["input_ids"]
               a      =      inputs["attention_mask"]
               a      =      inputs["token_type_ids"].numpy()
               a      =      None

               for index in range(A ):
                    # 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      =      token_type_ids_example[:, 3]  # shape (seq_len,)
                         a      =      np.zeros_like(A  ,		dtype=np.intaa )  # shape (seq_len,)

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

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

                         a      =      model_labels

                    a      =      input_ids[index]
                    a      =      attention_mask[index]  # shape (seq_len,)
                    a      =      token_type_ids[index]  # shape (seq_len, 7)
                    a      =      self.model(
                        input_ids=np.expand_dims(A  ,		axis=0 )  ,		attention_mask=np.expand_dims(A  ,		axis=0 )  ,		token_type_ids=np.expand_dims(A  ,		axis=0 )  ,		)
                    a      =      outputs.logits

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

                    all_logits.append(A )

                    a      =      tfp.distributions.Bernoulli(logits=A )
                    a      =      dist_per_token.probs_parameter() * tf.cast(A  ,		tf.floataa )

                    a      =      collections.defaultdict(A )
                    a      =      token_type_ids_example
                    for i, p in enumerate(tf.squeeze(A ).numpy().tolist() ):
                         a      =      token_type_ids_example[:, 0].tolist()[i]
                         a      =      token_type_ids_example[:, 1].tolist()[i] - 1
                         a      =      token_type_ids_example[:, 2].tolist()[i] - 1
                         if col >= 0 and row >= 0 and segment_id == 1:
                              coords_to_probs[(col, row)].append(A )

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

               a      =      tf.concat(tuple(A )  ,		0 )

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







     def __call__(	self  ,		*A  ,		**A )  ->    Tuple:



          '''simple docstring'''



          a      =      self._args_parser(*A  ,		**A )

          a      =      super().__call__(A  ,		**A )
          if len(A ) == 1:
               return results[0]
          return results







     def 			lowerCAmelCase_				(	self  ,		A=None  ,		A=None  ,		A=None  ,		**A )  ->    Any:



          '''simple docstring'''



          a      =      {}
          if padding is not None:
               a      =      padding
          if truncation is not None:
               a      =      truncation

          a      =      {}
          if sequential is not None:
               a      =      sequential
          return preprocess_params, forward_params, {}







     def 			lowerCAmelCase_				(	self  ,		A  ,		A=None  ,		A=True  ,		A=None )  ->    int:



          '''simple docstring'''



          if truncation is None:
               if self.type == "tapas":
                    a      =      "drop_rows_to_fit"
               else:
                    a      =      "do_not_truncate"

          a  ,      a      =      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      =      self.tokenizer(A  ,		A  ,		return_tensors=self.framework  ,		truncation=A  ,		padding=A )
          a      =      table
          return inputs







     def 			lowerCAmelCase_				(	self  ,		A  ,		A=False )  ->    Optional[Any]:



          '''simple docstring'''



          a      =      model_inputs.pop("table" )

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







     def 			lowerCAmelCase_				(	self  ,		A )  ->    Optional[Any]:



          '''simple docstring'''



          a      =      model_outputs["model_inputs"]
          a      =      model_outputs["table"]
          a      =      model_outputs["outputs"]
          if self.type == "tapas":
               if self.aggregate:
                    a  ,      a      =      outputs[:2]
                    a      =      self.tokenizer.convert_logits_to_predictions(A  ,		A  ,		A )
                    a  ,      a      =      predictions
                    a      =      {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(A )}

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

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

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







def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     list:
     a      =      []
     a      =      1

     for i in range(len(__UpperCamelCase)):
          if i + 1 < len(__UpperCamelCase) and text[i] == text[i + 1]:
               count += 1
          else:
               encoded.append((text[i], count))
               a      =      1

     return encoded

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     str:
     return "".join(char * length for char, length in encoded)


if __name__ == "__main__":
   from doctest import testmod

   testmod(name="run_length_encode", verbose=True)
   testmod(name="run_length_decode", verbose=True)

