


"""simple docstring"""


import timeit

import numpy as np

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






def      a__ (				__lowercase   )	->	Optional[Any]:
				def wrapper(*__lowercase     ,			**__lowercase   ):
								_A					  =			timeit.default_timer()
								_A					  =			func(*__lowercase     ,			**__lowercase   )
								_A					  =			timeit.default_timer() - starttime
								return delta

				_A					  =			func.__name__

				return wrapper






def      a__ (				__lowercase     ,			__lowercase=100     ,			__lowercase=None   )	->	Any:
				_A					  =			[]
				_A					  =			seq_shapes or {}
				for i in range(__lowercase   ):
								_A					  =			{}
								for col_id, (k, v) in enumerate(features.items()   ):
												if isinstance(__lowercase     ,			_ArrayXD   ):
																_A					  =			np.random.rand(*v.shape   ).astype(v.dtype   )
												elif isinstance(__lowercase     ,			datasets.Value   ):
																if v.dtype == "string":
																				_A					  =			"The small grey turtle was surprisingly fast when challenged."
																else:
																				_A					  =			np.random.randint(10     ,			size=1   ).astype(v.dtype   ).item()
												elif isinstance(__lowercase     ,			datasets.Sequence   ):
																while isinstance(__lowercase     ,			datasets.Sequence   ):
																				_A					  =			v.feature
																_A					  =			seq_shapes[k]
																_A					  =			np.random.rand(*__lowercase   ).astype(v.dtype   )
												_A					  =			data

								dummy_data.append((i, example)   )

				return dummy_data






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase=100     ,			__lowercase=None   )	->	Union[str, Any]:
				_A					  =			generate_examples(__lowercase     ,			num_examples=__lowercase     ,			seq_shapes=__lowercase   )

				with ArrowWriter(features=__lowercase     ,			path=__lowercase   ) as writer:
								for key, record in dummy_data:
												_A					  =			features.encode_example(__lowercase   )
												writer.write(__lowercase   )

								_A  ,		_A					  =			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					  =			datasets.Dataset.from_file(filename=__lowercase     ,			info=datasets.DatasetInfo(features=__lowercase   )   )

				return dataset



"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


a_       							=    logging.get_logger(__name__)
class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       str    ,   *a__      :       Dict    ,   **a__      :       Optional[int]		)       ->						None:





						'''simple docstring'''







						warnings.warn(
						    "The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
						    " use GLPNImageProcessor instead."    ,   a__    ,   )
						super().__init__(*a__    ,   **a__		)