

"""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        a    (							__snake_case					):



    """simple docstring"""



    def __call__(  self:			str     ,    UpperCamelCase:			str=None     ,    UpperCamelCase:			int=None     ,    **UpperCamelCase:			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(UpperCamelCase     ,    UpperCamelCase							) and table.get("""query"""							) is not None and table.get("""table"""							) is not None:
                   A__				      =	[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:
                        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(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:
              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(__snake_case					)
class        a    (							__snake_case					):



    """simple docstring"""



    UpperCAmelCase    			=       'table,query'

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






         """simple docstring"""


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

    def 					UpperCamelCase						(  self:			int     ,    **UpperCamelCase:			List[Any]							):






         """simple docstring"""


         return self.model(**UpperCamelCase							)

    def 					UpperCamelCase						(  self:			Dict     ,    **UpperCamelCase:			Dict							):






         """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(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:
                        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(UpperCamelCase							).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(UpperCamelCase							)

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

                   A__				      =	collections.defaultdict(UpperCamelCase							)
                   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(UpperCamelCase							)

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

              A__				      =	torch.cat(tuple(UpperCamelCase							)     ,    0							)

              return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(UpperCamelCase							)     ,    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(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:
                        A__				      =	token_type_ids_example[:, 3]  # shape (seq_len,)
                        A__				      =	np.zeros_like(UpperCamelCase     ,    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(UpperCamelCase     ,    axis=0							)     ,    attention_mask=np.expand_dims(UpperCamelCase     ,    axis=0							)     ,    token_type_ids=np.expand_dims(UpperCamelCase     ,    axis=0							)     ,    )
                   A__				      =	outputs.logits

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

                   all_logits.append(UpperCamelCase							)

                   A__				      =	tfp.distributions.Bernoulli(logits=UpperCamelCase							)
                   A__				      =	dist_per_token.probs_parameter() * tf.cast(UpperCamelCase     ,    tf.floataa							)

                   A__				      =	collections.defaultdict(UpperCamelCase							)
                   A__				      =	token_type_ids_example
                   for i, p in enumerate(tf.squeeze(UpperCamelCase							).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(UpperCamelCase							)

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

              A__				      =	tf.concat(tuple(UpperCamelCase							)     ,    0							)

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

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






         """simple docstring"""


         A__				      =	self._args_parser(*UpperCamelCase     ,    **UpperCamelCase							)

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

    def 					UpperCamelCase						(  self:			Tuple     ,    UpperCamelCase:			Optional[Any]=None     ,    UpperCamelCase:			Optional[Any]=None     ,    UpperCamelCase:			Optional[Any]=None     ,    **UpperCamelCase:			Dict							):






         """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 					UpperCamelCase						(  self:			Optional[Any]     ,    UpperCamelCase:			List[str]     ,    UpperCamelCase:			List[str]=None     ,    UpperCamelCase:			List[str]=True     ,    UpperCamelCase:			List[str]=None							):






         """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(UpperCamelCase     ,    UpperCamelCase     ,    return_tensors=self.framework     ,    truncation=UpperCamelCase     ,    padding=UpperCamelCase							)
         A__				      =	table
         return inputs

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






         """simple docstring"""


         A__				      =	model_inputs.pop("""table"""							)

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





    def 					UpperCamelCase						(  self:			Any     ,    UpperCamelCase:			Union[str, 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(UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase							)
                   A__      ,					A__				      =	predictions
                   A__				      =	{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(UpperCamelCase							)}

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

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

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









import unittest

from diffusers import __version__
from diffusers.utils import deprecate







class 					__lowerCamelCase							(      unittest.TestCase      ):
      lowerCamelCase_      : Dict  			=   '.'.join([str(int(__version__.split('.'      )[0]      ) + 1      )] + __version__.split('.'      )[1:]      )
      lowerCamelCase_      : int  			=   '0.0.1'







      def       lowerCAmelCase_       (	self  )		->			Optional[int]:
        snake_case_					= {"""deprecated_arg""": 4}

        with self.assertWarns(lowerCamelCase  ) as warning:
          snake_case_					= deprecate("""deprecated_arg"""				,    self.higher_version				,    """message"""				,    take_from=lowerCamelCase  )

        assert output == 4
        assert (
            str(warning.warning  )
            == f'''The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}.'''
            " message"
        )







      def       lowerCAmelCase_       (	self  )		->			List[Any]:
        snake_case_					= {"""deprecated_arg""": 4}

        with self.assertWarns(lowerCamelCase  ) as warning:
          snake_case_					= deprecate(("""deprecated_arg""", self.higher_version, """message""")				,    take_from=lowerCamelCase  )

        assert output == 4
        assert (
            str(warning.warning  )
            == f'''The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}.'''
            " message"
        )







      def       lowerCAmelCase_       (	self  )		->			int:
        snake_case_					= {"""deprecated_arg_1""": 4, """deprecated_arg_2""": 8}
        with self.assertWarns(lowerCamelCase  ) as warning:
          snake_case_		,							snake_case_					= deprecate(
              ("""deprecated_arg_1""", self.higher_version, """Hey""")				,    ("""deprecated_arg_2""", self.higher_version, """Hey""")				,    take_from=lowerCamelCase				,    )
        assert output_a == 4
        assert output_a == 8
        assert (
            str(warning.warnings[0].message  )
            == "The `deprecated_arg_1` argument is deprecated and will be removed in version"
            f''' {self.higher_version}. Hey'''
        )
        assert (
            str(warning.warnings[1].message  )
            == "The `deprecated_arg_2` argument is deprecated and will be removed in version"
            f''' {self.higher_version}. Hey'''
        )







      def       lowerCAmelCase_       (	self  )		->			Any:
        snake_case_					= {"""deprecated_arg""": 4}

        with self.assertRaises(lowerCamelCase  ) as error:
          deprecate(("""wrong_arg""", self.higher_version, """message""")				,    take_from=lowerCamelCase  )

        assert "test_deprecate_function_incorrect_arg in" in str(error.exception  )
        assert "line" in str(error.exception  )
        assert "got an unexpected keyword argument `deprecated_arg`" in str(error.exception  )







      def       lowerCAmelCase_       (	self  )		->			Optional[Any]:
        with self.assertWarns(lowerCamelCase  ) as warning:
          deprecate(("""deprecated_arg""", self.higher_version, """message""")  )

        assert (
            str(warning.warning  )
            == f'''`deprecated_arg` is deprecated and will be removed in version {self.higher_version}. message'''
        )







      def       lowerCAmelCase_       (	self  )		->			Any:
        with self.assertWarns(lowerCamelCase  ) as warning:
          deprecate(
              ("""deprecated_arg_1""", self.higher_version, """Hey""")				,    ("""deprecated_arg_2""", self.higher_version, """Hey""")				,    )
        assert (
            str(warning.warnings[0].message  )
            == f'''`deprecated_arg_1` is deprecated and will be removed in version {self.higher_version}. Hey'''
        )
        assert (
            str(warning.warnings[1].message  )
            == f'''`deprecated_arg_2` is deprecated and will be removed in version {self.higher_version}. Hey'''
        )







      def       lowerCAmelCase_       (	self  )		->			Any:







        class 					__lowerCamelCase							:
              lowerCamelCase_      : Dict  			=   5

        with self.assertWarns(lowerCamelCase  ) as warning:
          snake_case_					= deprecate(("""arg""", self.higher_version, """message""")				,    take_from=Args()  )

        assert arg == 5
        assert (
            str(warning.warning  )
            == f'''The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'''
        )







      def       lowerCAmelCase_       (	self  )		->			Any:







        class 					__lowerCamelCase							:
              lowerCamelCase_      : str  			=   5
              lowerCamelCase_      : List[str]  			=   7

        with self.assertWarns(lowerCamelCase  ) as warning:
          snake_case_		,							snake_case_					= deprecate(
              ("""arg""", self.higher_version, """message""")				,    ("""foo""", self.higher_version, """message""")				,    ("""does not exist""", self.higher_version, """message""")				,    take_from=Args()				,    )

        assert arg_a == 5
        assert arg_a == 7
        assert (
            str(warning.warning  )
            == f'''The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'''
        )
        assert (
            str(warning.warnings[0].message  )
            == f'''The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'''
        )
        assert (
            str(warning.warnings[1].message  )
            == f'''The `foo` attribute is deprecated and will be removed in version {self.higher_version}. message'''
        )







      def       lowerCAmelCase_       (	self  )		->			int:
        snake_case_					= {"""deprecated_arg""": 4}

        with self.assertRaises(lowerCamelCase  ) as error:
          deprecate(("""wrong_arg""", self.lower_version, """message""")				,    take_from=lowerCamelCase  )

        assert (
            str(error.exception  )
            == "The deprecation tuple ('wrong_arg', '0.0.1', 'message') should be removed since diffusers' version"
            f''' {__version__} is >= {self.lower_version}'''
        )







      def       lowerCAmelCase_       (	self  )		->			List[Any]:
        with self.assertWarns(lowerCamelCase  ) as warning:
          deprecate(("""deprecated_arg""", self.higher_version, """This message is better!!!""")				,    standard_warn=lowerCamelCase  )

        assert str(warning.warning  ) == "This message is better!!!"







      def       lowerCAmelCase_       (	self  )		->			List[Any]:
        with self.assertWarns(lowerCamelCase  ) as warning:
          deprecate(("""deprecated_arg""", self.higher_version, """This message is better!!!""")				,    standard_warn=lowerCamelCase  )
        assert str(warning.warning  ) == "This message is better!!!"
        assert "diffusers/tests/others/test_utils.py" in warning.filename