

"""simple docstring"""
import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD




def 					a__						(						lowerCAmelCase    )	-> int:
      def wrapper(*lowerCAmelCase			,	**lowerCAmelCase    ):
            UpperCAmelCase__			:							Union[str, Any]							=    timeit.default_timer()
            UpperCAmelCase__			:							Dict							=    func(*lowerCAmelCase			,	**lowerCAmelCase    )
            UpperCAmelCase__			:							int							=    timeit.default_timer() - starttime
            return delta

      UpperCAmelCase__			:							str							=    func.__name__

      return wrapper




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase=1_00			,	lowerCAmelCase=None    )	-> Optional[Any]:
      UpperCAmelCase__			:							str							=    []
      UpperCAmelCase__			:							List[Any]							=    seq_shapes or {}
      for i in range(lowerCAmelCase    ):
            UpperCAmelCase__			:							str							=    {}
            for col_id, (k, v) in enumerate(features.items()    ):
                  if isinstance(lowerCAmelCase			,	_ArrayXD    ):
                        UpperCAmelCase__			:							List[Any]							=    np.random.rand(*v.shape    ).astype(v.dtype    )
                  elif isinstance(lowerCAmelCase			,	datasets.Value    ):
                        if v.dtype == "string":
                              UpperCAmelCase__			:							Tuple							=    """The small grey turtle was surprisingly fast when challenged."""
                        else:
                              UpperCAmelCase__			:							int							=    np.random.randint(10			,	size=1    ).astype(v.dtype    ).item()
                  elif isinstance(lowerCAmelCase			,	datasets.Sequence    ):
                        while isinstance(lowerCAmelCase			,	datasets.Sequence    ):
                              UpperCAmelCase__			:							List[Any]							=    v.feature
                        UpperCAmelCase__			:							Dict							=    seq_shapes[k]
                        UpperCAmelCase__			:							Optional[int]							=    np.random.rand(*lowerCAmelCase    ).astype(v.dtype    )
                  UpperCAmelCase__			:							List[Any]							=    data

            dummy_data.append((i, example)    )

      return dummy_data





def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase=1_00			,	lowerCAmelCase=None    )	-> Dict:
      UpperCAmelCase__			:							Union[str, Any]							=    generate_examples(lowerCAmelCase			,	num_examples=lowerCAmelCase			,	seq_shapes=lowerCAmelCase    )

      with ArrowWriter(features=lowerCAmelCase			,	path=lowerCAmelCase    ) as writer:
            for key, record in dummy_data:
                  UpperCAmelCase__			:							Optional[Any]							=    features.encode_example(lowerCAmelCase    )
                  writer.write(lowerCAmelCase    )

            UpperCAmelCase__					,		UpperCAmelCase__			:							str							=    writer.finalize()

      if not num_final_examples == num_examples:
            raise ValueError(
                F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""    )

      UpperCAmelCase__			:							str							=    datasets.Dataset.from_file(filename=lowerCAmelCase			,	info=datasets.DatasetInfo(features=lowerCAmelCase    )    )

      return dataset




"""simple docstring"""
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels

