
'''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__ (							UpperCamelCase							):



  '''simple docstring'''






  def __call__(self	:			Optional[Any]						,			lowerCAmelCase_	:			List[Any]=None						,			lowerCAmelCase_	:			Any=None						,			**lowerCAmelCase_	:			List[Any]     ) ->							Union[str, Any]:
        # 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:
                    snake_case_    							=				[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:
                          snake_case_    							=				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:
              snake_case_    							=				[{"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."     )

                    snake_case_    							=				pd.DataFrame(tqa_pipeline_input["table"]     )

        return tqa_pipeline_inputs



@add_end_docstrings(UpperCamelCase							)
class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			'table,query'

  def __init__(self	:			Union[str, Any]						,			lowerCAmelCase_	:			Any=TableQuestionAnsweringArgumentHandler()						,			*lowerCAmelCase_	:			Tuple						,			**lowerCAmelCase_	:			str     ) ->							Optional[Any]:
        super().__init__(*lowerCAmelCase_						,			**lowerCAmelCase_     )
        snake_case_    							=				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()     )     )

        snake_case_    							=				bool(getattr(self.model.config						,			"aggregation_labels"						,			lowerCAmelCase_     )     ) and bool(
            getattr(self.model.config						,			"num_aggregation_labels"						,			lowerCAmelCase_     )     )
        snake_case_    							=				"tapas" if hasattr(self.model.config						,			"aggregation_labels"     ) else None

  def 		_lowerCamelCase						(self	:			Optional[int]						,			**lowerCAmelCase_	:			Tuple     ) ->							Union[str, Any]:
        return self.model(**lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			List[str]						,			**lowerCAmelCase_	:			str     ) ->							Dict:
        if self.framework == "pt":
              snake_case_    							=				[]
              snake_case_    							=				[]
              snake_case_    							=				None
              snake_case_    							=				inputs["input_ids"].shape[0]

              snake_case_    							=				inputs["input_ids"].to(self.device     )
              snake_case_    							=				inputs["attention_mask"].to(self.device     )
              snake_case_    							=				inputs["token_type_ids"].to(self.device     )
              snake_case_    							=				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:
                          snake_case_    							=				token_type_ids_example[:, 3]  # shape (seq_len,)
                          snake_case_    							=				np.zeros_like(prev_labels_example.cpu().numpy()     )  # shape (seq_len,)

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

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

                          snake_case_    							=				torch.from_numpy(lowerCAmelCase_     ).type(torch.long     ).to(self.device     )

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

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

                    all_logits.append(lowerCAmelCase_     )

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

                    snake_case_    							=				collections.defaultdict(lowerCAmelCase_     )
                    for i, p in enumerate(probabilities.squeeze().tolist()     ):
                          snake_case_    							=				token_type_ids_example[:, 0].tolist()[i]
                          snake_case_    							=				token_type_ids_example[:, 1].tolist()[i] - 1
                          snake_case_    							=				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_     )

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

              snake_case_    							=				torch.cat(tuple(lowerCAmelCase_     )						,			0     )

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

              snake_case_    							=				inputs["input_ids"]
              snake_case_    							=				inputs["attention_mask"]
              snake_case_    							=				inputs["token_type_ids"].numpy()
              snake_case_    							=				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:
                          snake_case_    							=				token_type_ids_example[:, 3]  # shape (seq_len,)
                          snake_case_    							=				np.zeros_like(lowerCAmelCase_						,			dtype=np.intaa     )  # shape (seq_len,)

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

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

                          snake_case_    							=				model_labels

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

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

                    all_logits.append(lowerCAmelCase_     )

                    snake_case_    							=				tfp.distributions.Bernoulli(logits=lowerCAmelCase_     )
                    snake_case_    							=				dist_per_token.probs_parameter() * tf.cast(lowerCAmelCase_						,			tf.floataa     )

                    snake_case_    							=				collections.defaultdict(lowerCAmelCase_     )
                    snake_case_    							=				token_type_ids_example
                    for i, p in enumerate(tf.squeeze(lowerCAmelCase_     ).numpy().tolist()     ):
                          snake_case_    							=				token_type_ids_example[:, 0].tolist()[i]
                          snake_case_    							=				token_type_ids_example[:, 1].tolist()[i] - 1
                          snake_case_    							=				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_     )

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

              snake_case_    							=				tf.concat(tuple(lowerCAmelCase_     )						,			0     )

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

  def __call__(self	:			Dict						,			*lowerCAmelCase_	:			Dict						,			**lowerCAmelCase_	:			Union[str, Any]     ) ->							str:
        snake_case_    							=				self._args_parser(*lowerCAmelCase_						,			**lowerCAmelCase_     )

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

  def 		_lowerCamelCase						(self	:			Optional[Any]						,			lowerCAmelCase_	:			List[str]=None						,			lowerCAmelCase_	:			List[str]=None						,			lowerCAmelCase_	:			Tuple=None						,			**lowerCAmelCase_	:			Dict     ) ->							List[Any]:
        snake_case_    							=				{}
        if padding is not None:
              snake_case_    							=				padding
        if truncation is not None:
              snake_case_    							=				truncation

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

  def 		_lowerCamelCase						(self	:			Tuple						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			List[Any]=None						,			lowerCAmelCase_	:			Any=True						,			lowerCAmelCase_	:			List[Any]=None     ) ->							int:
        if truncation is None:
              if self.type == "tapas":
                    snake_case_    							=				"drop_rows_to_fit"
              else:
                    snake_case_    							=				"do_not_truncate"

        snake_case_							,						snake_case_    							=				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"     )
        snake_case_    							=				self.tokenizer(lowerCAmelCase_						,			lowerCAmelCase_						,			return_tensors=self.framework						,			truncation=lowerCAmelCase_						,			padding=lowerCAmelCase_     )
        snake_case_    							=				table
        return inputs

  def 		_lowerCamelCase						(self	:			Optional[Any]						,			lowerCAmelCase_	:			List[Any]						,			lowerCAmelCase_	:			List[Any]=False     ) ->							Tuple:
        snake_case_    							=				model_inputs.pop("table"     )

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


  def 		_lowerCamelCase						(self	:			Tuple						,			lowerCAmelCase_	:			List[Any]     ) ->							Any:
        snake_case_    							=				model_outputs["model_inputs"]
        snake_case_    							=				model_outputs["table"]
        snake_case_    							=				model_outputs["outputs"]
        if self.type == "tapas":
              if self.aggregate:
                    snake_case_							,						snake_case_    							=				outputs[:2]
                    snake_case_    							=				self.tokenizer.convert_logits_to_predictions(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_     )
                    snake_case_							,						snake_case_    							=				predictions
                    snake_case_    							=				{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(lowerCAmelCase_     )}

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

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

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









'''simple docstring'''







def     _UpperCamelCase				(  snake_case__ = 1_000				):
      __A		 =   []
      __A		 =   []
      __A		 =   3
      __A		 =   5
      __A		 =   1
      while True:
            __A		 =   z * temp
            if result < n:
                  zmulti.append(snake_case__				)
                  temp += 1
            else:
                  __A		 =   1
                  break
      while True:
            __A		 =   x * temp
            if result < n:
                  xmulti.append(snake_case__				)
                  temp += 1
            else:
                  break
      __A		 =   list(set(xmulti + zmulti				)				)
      return sum(snake_case__				)


if __name__ == "__main__":
       print(F"""{solution() = }""")


