




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   (			a__    ):



     def __call__(  self   :							Union[str, Any]      ,		lowercase__   :							Union[str, Any]=None      ,		lowercase__   :							List[Any]=None      ,		**lowercase__   :							str       ):
       # 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(lowercase__      ,		lowercase__       ) and table.get('''query'''       ) is not None and table.get('''table'''       ) is not None:
           a_		    =  [table]
         elif isinstance(lowercase__      ,		lowercase__       ) and len(lowercase__       ) > 0:
           if not all(isinstance(lowercase__      ,		lowercase__       ) for d in table       ):
             raise ValueError(
                 f"Keyword argument `table` should be a list of dict, but is {(type(lowercase__       ) 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(lowercase__      ,		lowercase__       ) or isinstance(lowercase__      ,		types.GeneratorType       ):
           return table
         else:
           raise ValueError(
               '''Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but '''
               f"is {type(lowercase__       )})"       )
       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(a__    )
class 							__lowercase   (			a__    ):
     _lowerCAmelCase		       =						"table,query"



     def __init__(  self   :							Tuple      ,		lowercase__   :							Dict=TableQuestionAnsweringArgumentHandler()      ,		*lowercase__   :							Tuple      ,		**lowercase__   :							List[Any]       ):
       super().__init__(*lowercase__      ,		**lowercase__       )
       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'''      ,		lowercase__       )       ) and bool(
           getattr(self.model.config      ,		'''num_aggregation_labels'''      ,		lowercase__       )       )
       a_		    =  '''tapas''' if hasattr(self.model.config      ,		'''aggregation_labels'''       ) else None



     def 				__magic_name__		(  self   :							int      ,		**lowercase__   :							Any       ):
       return self.model(**lowercase__       )



     def 				__magic_name__		(  self   :							List[Any]      ,		**lowercase__   :							Tuple       ):
       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(lowercase__       ):
           # 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(lowercase__       ).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(lowercase__       )

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

           a_		    =  collections.defaultdict(lowercase__       )
           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(lowercase__       )

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

         a_		    =  torch.cat(tuple(lowercase__       )      ,		0       )

         return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(lowercase__       )      ,		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(lowercase__       ):
           # 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(lowercase__      ,		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(lowercase__      ,		axis=0       )      ,		attention_mask=np.expand_dims(lowercase__      ,		axis=0       )      ,		token_type_ids=np.expand_dims(lowercase__      ,		axis=0       )      ,		)
           a_		    =  outputs.logits

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

           all_logits.append(lowercase__       )

           a_		    =  tfp.distributions.Bernoulli(logits=lowercase__       )
           a_		    =  dist_per_token.probs_parameter() * tf.cast(lowercase__      ,		tf.floataa       )

           a_		    =  collections.defaultdict(lowercase__       )
           a_		    =  token_type_ids_example
           for i, p in enumerate(tf.squeeze(lowercase__       ).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(lowercase__       )

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

         a_		    =  tf.concat(tuple(lowercase__       )      ,		0       )

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



     def __call__(  self   :							Union[str, Any]      ,		*lowercase__   :							str      ,		**lowercase__   :							Optional[Any]       ):
       a_		    =  self._args_parser(*lowercase__      ,		**lowercase__       )

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



     def 				__magic_name__		(  self   :							Tuple      ,		lowercase__   :							int=None      ,		lowercase__   :							Optional[Any]=None      ,		lowercase__   :							int=None      ,		**lowercase__   :							Union[str, Any]       ):
       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 				__magic_name__		(  self   :							List[str]      ,		lowercase__   :							Tuple      ,		lowercase__   :							str=None      ,		lowercase__   :							Any=True      ,		lowercase__   :							List[str]=None       ):
       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(lowercase__      ,		lowercase__      ,		return_tensors=self.framework      ,		truncation=lowercase__      ,		padding=lowercase__       )
       a_		    =  table
       return inputs



     def 				__magic_name__		(  self   :							Optional[int]      ,		lowercase__   :							Tuple      ,		lowercase__   :							Union[str, Any]=False       ):
       a_		    =  model_inputs.pop('''table'''       )

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



     def 				__magic_name__		(  self   :							Optional[int]      ,		lowercase__   :							Dict       ):
       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(lowercase__      ,		lowercase__      ,		lowercase__       )
           a_    , a_		    =  predictions
           a_		    =  {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(lowercase__       )}

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

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

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






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if not root or root not in binary_tree_mirror_dictionary:
    return
  a_    , a_		    =  binary_tree_mirror_dictionary[root][:2]
  a_		    =  [right_child, left_child]
  binary_tree_mirror_dict(_A      ,      _A			)
  binary_tree_mirror_dict(_A      ,      _A			)






def 				UpperCAmelCase__  (							_A      ,      _A = 1			):






  """simple docstring"""


  if not binary_tree:
    raise ValueError('''binary tree cannot be empty'''			)
  if root not in binary_tree:
    a_		    =  f"root {root} is not present in the binary_tree"
    raise ValueError(_A			)
  a_		    =  dict(_A			)
  binary_tree_mirror_dict(_A      ,      _A			)
  return binary_tree_mirror_dictionary


if __name__ == "__main__":
  UpperCamelCase__       				=	{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F"""Binary tree: {binary_tree}""")
  UpperCamelCase__       				=	binary_tree_mirror(binary_tree, 5)
  print(F"""Binary tree mirror: {binary_tree_mirror_dictionary}""")
