






"""simple docstring"""


import timeit

import numpy as np

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




def     snake_case						(A_   :Dict   ):







  '''simple docstring'''

  def wrapper(*A_   :Any			,    **A_   :Union[str, Any]   ):
    a  :    Dict       =     timeit.default_timer()
    a  :    int       =     func(*A_			,    **A_   )
    a  :    Any       =     timeit.default_timer() - starttime
    return delta

  a  :    List[str]       =     func.__name__

  return wrapper




def     snake_case						(A_   :dict			,    A_   :List[str]=1_0_0			,    A_   :int=None   ):







  '''simple docstring'''

  a  :    List[Any]       =     []
  a  :    Union[str, Any]       =     seq_shapes or {}
  for i in range(A_   ):
    a  :    List[Any]       =     {}
    for col_id, (k, v) in enumerate(features.items()   ):
      if isinstance(A_			,    _ArrayXD   ):
        a  :    Any       =     np.random.rand(*v.shape   ).astype(v.dtype   )
      elif isinstance(A_			,    datasets.Value   ):
        if v.dtype == "string":
          a  :    Optional[Any]       =     'The small grey turtle was surprisingly fast when challenged.'
        else:
          a  :    List[Any]       =     np.random.randint(1_0			,    size=1   ).astype(v.dtype   ).item()
      elif isinstance(A_			,    datasets.Sequence   ):
        while isinstance(A_			,    datasets.Sequence   ):
          a  :    Optional[Any]       =     v.feature
        a  :    Tuple       =     seq_shapes[k]
        a  :    Any       =     np.random.rand(*A_   ).astype(v.dtype   )
      a  :    Dict       =     data

    dummy_data.append((i, example)   )

  return dummy_data






def     snake_case						(A_   :int			,    A_   :str			,    A_   :Union[str, Any]=1_0_0			,    A_   :Any=None   ):







  '''simple docstring'''

  a  :    List[str]       =     generate_examples(A_			,    num_examples=A_			,    seq_shapes=A_   )

  with ArrowWriter(features=A_			,    path=A_   ) as writer:
    for key, record in dummy_data:
      a  :    Tuple       =     features.encode_example(A_   )
      writer.write(A_   )

    a,  a  :    Optional[Any]       =     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}.'''   )

  a  :    Optional[int]       =     datasets.Dataset.from_file(filename=A_			,    info=datasets.DatasetInfo(features=A_   )   )

  return dataset












"""simple docstring"""


_UpperCamelCase       :					Tuple									=		'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_UpperCamelCase       :					str									=		[{'type': 'code', 'content': INSTALL_CONTENT}]
_UpperCamelCase       :					Tuple									=		{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




