code: '''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  UpperCAmelCase (       __SCREAMING_SNAKE_CASE):
  
   def __call__( self :	List[str],					a_ :	List[str]=None,					a_ :	Union[str, Any]=None,					**a_ :	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(UpperCamelCase__,					UpperCamelCase__					) and table.get("query"					) is not None and table.get("table"					) is not None:
            UpperCamelCase__ 						=						[table]
         elif isinstance(UpperCamelCase__,					UpperCamelCase__					) and len(UpperCamelCase__					) > 0:
            if not all(isinstance(UpperCamelCase__,					UpperCamelCase__					) for d in table					):
               raise ValueError(
                   f'Keyword argument `table` should be a list of dict, but is {(type(UpperCamelCase__					) for d in table)}'					)
  
            if table[0].get("query"					) is not None and table[0].get("table"					) is not None:
               UpperCamelCase__ 						=						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(UpperCamelCase__,					UpperCamelCase__					) or isinstance(UpperCamelCase__,					types.GeneratorType					):
            return table
         else:
            raise ValueError(
                "Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but "
                f'is {type(UpperCamelCase__					)})'					)
      else:
         UpperCamelCase__ 						=						[{'''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__ 						=						pd.DataFrame(tqa_pipeline_input["table"]					)
  
      return tqa_pipeline_inputs
  
  
  
  
  
  
  
  @add_end_docstrings(__SCREAMING_SNAKE_CASE)
  class  UpperCAmelCase (       __SCREAMING_SNAKE_CASE):
   _lowerCamelCase :					List[Any]    					=					'table,query'
  
   def __init__( self :	List[Any],					a_ :	Union[str, Any]=TableQuestionAnsweringArgumentHandler(),					*a_ :	int,					**a_ :	Dict					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      super().__init__(*UpperCamelCase__,					**UpperCamelCase__					)
      UpperCamelCase__ 						=						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__ 						=						bool(getattr(self.model.config,					"aggregation_labels",					UpperCamelCase__					)					) and bool(
          getattr(self.model.config,					"num_aggregation_labels",					UpperCamelCase__					)					)
      UpperCamelCase__ 						=						'''tapas''' if hasattr(self.model.config,					"aggregation_labels"					) else None
  
   def 		lowercase_						( self :	int,					**a_ :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.model(**UpperCamelCase__					)
  
   def 		lowercase_						( self :	str,					**a_ :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if self.framework == "pt":
         UpperCamelCase__ 						=						[]
         UpperCamelCase__ 						=						[]
         UpperCamelCase__ 						=						None
         UpperCamelCase__ 						=						inputs['''input_ids'''].shape[0]
  
         UpperCamelCase__ 						=						inputs['''input_ids'''].to(self.device					)
         UpperCamelCase__ 						=						inputs['''attention_mask'''].to(self.device					)
         UpperCamelCase__ 						=						inputs['''token_type_ids'''].to(self.device					)
         UpperCamelCase__ 						=						None
  
         for index in range(UpperCamelCase__					):
            # 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__ 						=						token_type_ids_example[:, 3]  # shape (seq_len,)
               UpperCamelCase__ 						=						np.zeros_like(prev_labels_example.cpu().numpy()					)  # shape (seq_len,)
  
               UpperCamelCase__ 						=						token_type_ids[index]  # shape (seq_len, 7)
               for i in range(model_labels.shape[0]					):
                  UpperCamelCase__ 						=						token_type_ids_example[:, 0].tolist()[i]
                  UpperCamelCase__ 						=						token_type_ids_example[:, 1].tolist()[i] - 1
                  UpperCamelCase__ 						=						token_type_ids_example[:, 2].tolist()[i] - 1
  
                  if row_id >= 0 and col_id >= 0 and segment_id == 1:
                     UpperCamelCase__ 						=						int(prev_answers[(col_id, row_id)]					)
  
               UpperCamelCase__ 						=						torch.from_numpy(UpperCamelCase__					).type(torch.long					).to(self.device					)
  
            UpperCamelCase__ 						=						input_ids[index]
            UpperCamelCase__ 						=						attention_mask[index]  # shape (seq_len,)
            UpperCamelCase__ 						=						token_type_ids[index]  # shape (seq_len, 7)
            UpperCamelCase__ 						=						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__ 						=						outputs.logits
  
            if self.aggregate:
               all_aggregations.append(outputs.logits_aggregation					)
  
            all_logits.append(UpperCamelCase__					)
  
            UpperCamelCase__ 						=						torch.distributions.Bernoulli(logits=UpperCamelCase__					)
            UpperCamelCase__ 						=						dist_per_token.probs * attention_mask_example.type(torch.floataa					).to(
                dist_per_token.probs.device					)
  
            UpperCamelCase__ 						=						collections.defaultdict(UpperCamelCase__					)
            for i, p in enumerate(probabilities.squeeze().tolist()					):
               UpperCamelCase__ 						=						token_type_ids_example[:, 0].tolist()[i]
               UpperCamelCase__ 						=						token_type_ids_example[:, 1].tolist()[i] - 1
               UpperCamelCase__ 						=						token_type_ids_example[:, 2].tolist()[i] - 1
               if col >= 0 and row >= 0 and segment_id == 1:
                  coords_to_probs[(col, row)].append(UpperCamelCase__					)
  
            UpperCamelCase__ 						=						{key: np.array(coords_to_probs[key]					).mean() > 0.5 for key in coords_to_probs}
  
         UpperCamelCase__ 						=						torch.cat(tuple(UpperCamelCase__					),					0					)
  
         return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(UpperCamelCase__					),					0					))
      else:
         UpperCamelCase__ 						=						[]
         UpperCamelCase__ 						=						[]
         UpperCamelCase__ 						=						None
         UpperCamelCase__ 						=						inputs['''input_ids'''].shape[0]
  
         UpperCamelCase__ 						=						inputs['''input_ids''']
         UpperCamelCase__ 						=						inputs['''attention_mask''']
         UpperCamelCase__ 						=						inputs['''token_type_ids'''].numpy()
         UpperCamelCase__ 						=						None
  
         for index in range(UpperCamelCase__					):
            # 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__ 						=						token_type_ids_example[:, 3]  # shape (seq_len,)
               UpperCamelCase__ 						=						np.zeros_like(UpperCamelCase__,					dtype=np.intaa					)  # shape (seq_len,)
  
               UpperCamelCase__ 						=						token_type_ids[index]  # shape (seq_len, 7)
               for i in range(model_labels.shape[0]					):
                  UpperCamelCase__ 						=						token_type_ids_example[:, 0].tolist()[i]
                  UpperCamelCase__ 						=						token_type_ids_example[:, 1].tolist()[i] - 1
                  UpperCamelCase__ 						=						token_type_ids_example[:, 2].tolist()[i] - 1
  
                  if row_id >= 0 and col_id >= 0 and segment_id == 1:
                     UpperCamelCase__ 						=						int(prev_answers[(col_id, row_id)]					)
  
               UpperCamelCase__ 						=						model_labels
  
            UpperCamelCase__ 						=						input_ids[index]
            UpperCamelCase__ 						=						attention_mask[index]  # shape (seq_len,)
            UpperCamelCase__ 						=						token_type_ids[index]  # shape (seq_len, 7)
            UpperCamelCase__ 						=						self.model(
                input_ids=np.expand_dims(UpperCamelCase__,					axis=0					),					attention_mask=np.expand_dims(UpperCamelCase__,					axis=0					),					token_type_ids=np.expand_dims(UpperCamelCase__,					axis=0					),					)
            UpperCamelCase__ 						=						outputs.logits
  
            if self.aggregate:
               all_aggregations.append(outputs.logits_aggregation					)
  
            all_logits.append(UpperCamelCase__					)
  
            UpperCamelCase__ 						=						tfp.distributions.Bernoulli(logits=UpperCamelCase__					)
            UpperCamelCase__ 						=						dist_per_token.probs_parameter() * tf.cast(UpperCamelCase__,					tf.floataa					)
  
            UpperCamelCase__ 						=						collections.defaultdict(UpperCamelCase__					)
            UpperCamelCase__ 						=						token_type_ids_example
            for i, p in enumerate(tf.squeeze(UpperCamelCase__					).numpy().tolist()					):
               UpperCamelCase__ 						=						token_type_ids_example[:, 0].tolist()[i]
               UpperCamelCase__ 						=						token_type_ids_example[:, 1].tolist()[i] - 1
               UpperCamelCase__ 						=						token_type_ids_example[:, 2].tolist()[i] - 1
               if col >= 0 and row >= 0 and segment_id == 1:
                  coords_to_probs[(col, row)].append(UpperCamelCase__					)
  
            UpperCamelCase__ 						=						{key: np.array(coords_to_probs[key]					).mean() > 0.5 for key in coords_to_probs}
  
         UpperCamelCase__ 						=						tf.concat(tuple(UpperCamelCase__					),					0					)
  
         return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(UpperCamelCase__					),					0					))
  
   def __call__( self :	List[Any],					*a_ :	Union[str, Any],					**a_ :	List[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self._args_parser(*UpperCamelCase__,					**UpperCamelCase__					)
  
      UpperCamelCase__ 						=						super().__call__(UpperCamelCase__,					**UpperCamelCase__					)
      if len(UpperCamelCase__					) == 1:
         return results[0]
      return results
  
   def 		lowercase_						( self :	Union[str, Any],					a_ :	Optional[int]=None,					a_ :	int=None,					a_ :	str=None,					**a_ :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						{}
      if padding is not None:
         UpperCamelCase__ 						=						padding
      if truncation is not None:
         UpperCamelCase__ 						=						truncation
  
      UpperCamelCase__ 						=						{}
      if sequential is not None:
         UpperCamelCase__ 						=						sequential
      return preprocess_params, forward_params, {}
  
   def 		lowercase_						( self :	Union[str, Any],					a_ :	Optional[int],					a_ :	Dict=None,					a_ :	Union[str, Any]=True,					a_ :	Optional[Any]=None					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if truncation is None:
         if self.type == "tapas":
            UpperCamelCase__ 						=						'''drop_rows_to_fit'''
         else:
            UpperCamelCase__ 						=						'''do_not_truncate'''
  
      UpperCamelCase__ 						=						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__ 						=						self.tokenizer(UpperCamelCase__,					UpperCamelCase__,					return_tensors=self.framework,					truncation=UpperCamelCase__,					padding=UpperCamelCase__					)
      UpperCamelCase__ 						=						table
      return inputs
  
   def 		lowercase_						( self :	str,					a_ :	int,					a_ :	Dict=False					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						model_inputs.pop("table"					)
  
      if self.type == "tapas":
         if sequential:
            UpperCamelCase__ 						=						self.sequential_inference(**UpperCamelCase__					)
         else:
            UpperCamelCase__ 						=						self.batch_inference(**UpperCamelCase__					)
      else:
         UpperCamelCase__ 						=						self.model.generate(**UpperCamelCase__					)
      UpperCamelCase__ 						=						{'''model_inputs''': model_inputs, '''table''': table, '''outputs''': outputs}
      return model_outputs
  
   def 		lowercase_						( self :	str,					a_ :	Dict					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						model_outputs['''model_inputs''']
      UpperCamelCase__ 						=						model_outputs['''table''']
      UpperCamelCase__ 						=						model_outputs['''outputs''']
      if self.type == "tapas":
         if self.aggregate:
            UpperCamelCase__ 						=						outputs[:2]
            UpperCamelCase__ 						=						self.tokenizer.convert_logits_to_predictions(UpperCamelCase__,					UpperCamelCase__,					UpperCamelCase__					)
            UpperCamelCase__ 						=						predictions
            UpperCamelCase__ 						=						{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(UpperCamelCase__					)}
  
            UpperCamelCase__ 						=						self.model.config.no_aggregation_label_index
            UpperCamelCase__ 						=						{
                i: aggregators[i] + ''' > ''' for i, pred in enumerate(UpperCamelCase__					) if pred != no_agg_label_index
            }
         else:
            UpperCamelCase__ 						=						outputs[0]
            UpperCamelCase__ 						=						self.tokenizer.convert_logits_to_predictions(UpperCamelCase__,					UpperCamelCase__					)
            UpperCamelCase__ 						=						predictions[0]
            UpperCamelCase__ 						=						{}
            UpperCamelCase__ 						=						{}
         UpperCamelCase__ 						=						[]
         for index, coordinates in enumerate(UpperCamelCase__					):
            UpperCamelCase__ 						=						[table.iat[coordinate] for coordinate in coordinates]
            UpperCamelCase__ 						=						aggregators.get(UpperCamelCase__,					""					)
            UpperCamelCase__ 						=						aggregators_prefix.get(UpperCamelCase__,					""					)
            UpperCamelCase__ 						=						{
                '''answer''': aggregator_prefix + ''', '''.join(UpperCamelCase__					),
                '''coordinates''': coordinates,
                '''cells''': [table.iat[coordinate] for coordinate in coordinates],
            }
            if aggregator:
               UpperCamelCase__ 						=						aggregator
  
            answers.append(UpperCamelCase__					)
         if len(UpperCamelCase__					) == 0:
            raise PipelineException("Empty answer"					)
      else:
         UpperCamelCase__ 						=						[{'''answer''': answer} for answer in self.tokenizer.batch_decode(UpperCamelCase__,					skip_special_tokens=UpperCamelCase__					)]
  
      return answers if len(UpperCamelCase__					) > 1 else answers[0]
style_context: 
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __lowercase:		int							=     {
      "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
  }
  
  try:
         if not is_sentencepiece_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __lowercase:		str							=     ["LlamaTokenizer"]
  
  try:
         if not is_tokenizers_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __lowercase:		List[Any]							=     ["LlamaTokenizerFast"]
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __lowercase:		Union[str, Any]							=     [
             "OpenLlamaForCausalLM",
             "OpenLlamaModel",
             "OpenLlamaPreTrainedModel",
             "OpenLlamaForSequenceClassification",
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
         try:
                if not is_sentencepiece_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from transformers import LlamaTokenizer
  
         try:
                if not is_tokenizers_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from transformers import LlamaTokenizerFast
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_open_llama import (
                    OpenLlamaForCausalLM,
                    OpenLlamaForSequenceClassification,
                    OpenLlamaModel,
                    OpenLlamaPreTrainedModel,
                )
  
  
  else:
         import sys
  
         __lowercase:		Tuple							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)