






"""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   _lowercase			(  lowerCAmelCase    ):


      def __call__(     self					:						Dict				, a					:						List[str]=None				, a					:						int=None				, **a					:						Union[str, 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(a				, a    ) and table.get('''query'''    ) is not None and table.get('''table'''    ) is not None:
                        __snake_case   :     Union[str, Any]	      =[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:
                              __snake_case   :     Dict	      =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:
                  __snake_case   :     Optional[int]	      =[{'''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   :     List[Any]	      =pd.DataFrame(tqa_pipeline_input['''table''']    )

            return tqa_pipeline_inputs



@add_end_docstrings(lowerCAmelCase    )
class   _lowercase			(  lowerCAmelCase    ):
      _a :  int										= '''table,query'''


      def __init__(     self					:						Union[str, Any]				, a					:						Any=TableQuestionAnsweringArgumentHandler()				, *a					:						Dict				, **a					:						Optional[int]    ):





            """simple docstring"""







            super().__init__(*a				, **a    )
            __snake_case   :     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()    )    )

            __snake_case   :     Union[str, Any]	      =bool(getattr(self.model.config				, '''aggregation_labels'''				, a    )    ) and bool(
                getattr(self.model.config				, '''num_aggregation_labels'''				, a    )    )
            __snake_case   :     Any	      ='''tapas''' if hasattr(self.model.config				, '''aggregation_labels'''    ) else None


      def _UpperCamelCase    (     self					:						Tuple				, **a					:						Any    ):





            """simple docstring"""







            return self.model(**a    )


      def _UpperCamelCase    (     self					:						List[Any]				, **a					:						str    ):





            """simple docstring"""







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

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

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

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

                              __snake_case   :     Tuple	      =torch.from_numpy(a    ).type(torch.long    ).to(self.device    )

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

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

                        all_logits.append(a    )

                        __snake_case   :     Union[str, Any]	      =torch.distributions.Bernoulli(logits=a    )
                        __snake_case   :     Optional[int]	      =dist_per_token.probs * attention_mask_example.type(torch.floataa    ).to(
                            dist_per_token.probs.device    )

                        __snake_case   :     Optional[int]	      =collections.defaultdict(a    )
                        for i, p in enumerate(probabilities.squeeze().tolist()    ):
                              __snake_case   :     Tuple	      =token_type_ids_example[:, 0].tolist()[i]
                              __snake_case   :     Dict	      =token_type_ids_example[:, 1].tolist()[i] - 1
                              __snake_case   :     Dict	      =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    )

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

                  __snake_case   :     List[Any]	      =torch.cat(tuple(a    )				, 0    )

                  return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(a    )				, 0    ))
            else:
                  __snake_case   :     Optional[int]	      =[]
                  __snake_case   :     Optional[Any]	      =[]
                  __snake_case   :     Tuple	      =None
                  __snake_case   :     List[Any]	      =inputs['''input_ids'''].shape[0]

                  __snake_case   :     List[Any]	      =inputs['''input_ids''']
                  __snake_case   :     Tuple	      =inputs['''attention_mask''']
                  __snake_case   :     Optional[int]	      =inputs['''token_type_ids'''].numpy()
                  __snake_case   :     int	      =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:
                              __snake_case   :     Union[str, Any]	      =token_type_ids_example[:, 3]  # shape (seq_len,)
                              __snake_case   :     List[str]	      =np.zeros_like(a				, dtype=np.intaa    )  # shape (seq_len,)

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

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

                              __snake_case   :     Union[str, Any]	      =model_labels

                        __snake_case   :     Dict	      =input_ids[index]
                        __snake_case   :     Any	      =attention_mask[index]  # shape (seq_len,)
                        __snake_case   :     int	      =token_type_ids[index]  # shape (seq_len, 7)
                        __snake_case   :     Tuple	      =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    )				, )
                        __snake_case   :     Union[str, Any]	      =outputs.logits

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

                        all_logits.append(a    )

                        __snake_case   :     str	      =tfp.distributions.Bernoulli(logits=a    )
                        __snake_case   :     Optional[Any]	      =dist_per_token.probs_parameter() * tf.cast(a				, tf.floataa    )

                        __snake_case   :     Union[str, Any]	      =collections.defaultdict(a    )
                        __snake_case   :     Any	      =token_type_ids_example
                        for i, p in enumerate(tf.squeeze(a    ).numpy().tolist()    ):
                              __snake_case   :     List[Any]	      =token_type_ids_example[:, 0].tolist()[i]
                              __snake_case   :     Any	      =token_type_ids_example[:, 1].tolist()[i] - 1
                              __snake_case   :     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(a    )

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

                  __snake_case   :     Any	      =tf.concat(tuple(a    )				, 0    )

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


      def __call__(     self					:						str				, *a					:						Any				, **a					:						List[Any]    ):





            """simple docstring"""







            __snake_case   :     str	      =self._args_parser(*a				, **a    )

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


      def _UpperCamelCase    (     self					:						List[str]				, a					:						int=None				, a					:						int=None				, a					:						Optional[int]=None				, **a					:						Tuple    ):





            """simple docstring"""







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

            __snake_case   :     List[str]	      ={}
            if sequential is not None:
                  __snake_case   :     Union[str, Any]	      =sequential
            return preprocess_params, forward_params, {}


      def _UpperCamelCase    (     self					:						Any				, a					:						Tuple				, a					:						int=None				, a					:						Tuple=True				, a					:						Union[str, Any]=None    ):





            """simple docstring"""







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

            __snake_case      ,					__snake_case   :     Union[str, Any]	      =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   :     Any	      =self.tokenizer(a				, a				, return_tensors=self.framework				, truncation=a				, padding=a    )
            __snake_case   :     List[Any]	      =table
            return inputs


      def _UpperCamelCase    (     self					:						Union[str, Any]				, a					:						Dict				, a					:						List[Any]=False    ):





            """simple docstring"""







            __snake_case   :     str	      =model_inputs.pop('''table'''    )

            if self.type == "tapas":
                  if sequential:
                        __snake_case   :     Dict	      =self.sequential_inference(**a    )
                  else:
                        __snake_case   :     Tuple	      =self.batch_inference(**a    )
            else:
                  __snake_case   :     List[Any]	      =self.model.generate(**a    )
            __snake_case   :     Dict	      ={'''model_inputs''': model_inputs, '''table''': table, '''outputs''': outputs}
            return model_outputs







      def _UpperCamelCase    (     self					:						Optional[Any]				, a					:						List[Any]    ):





            """simple docstring"""







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

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

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

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










"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase_						:					Tuple      				=			{"""configuration_detr""": ["""DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetrConfig""", """DetrOnnxConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Tuple      				=			["""DetrFeatureExtractor"""]
    UpperCamelCase_						:					int      				=			["""DetrImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Dict      				=			[
        """DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """DetrForObjectDetection""",
        """DetrForSegmentation""",
        """DetrModel""",
        """DetrPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_detr import DetrFeatureExtractor
        from .image_processing_detr import DetrImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_detr import (
            DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
            DetrForObjectDetection,
            DetrForSegmentation,
            DetrModel,
            DetrPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase_						:					List[str]      				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


