





import timeit

import numpy as np

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

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Any     )      ->						Optional[int]:
  def wrapper(*snake_case__  :Dict			,							**snake_case__  :Any     ):
    _lowercase							= timeit.default_timer()
    _lowercase							= func(*snake_case__			,							**snake_case__     )
    _lowercase							= timeit.default_timer() - starttime
    return delta

  _lowercase							= func.__name__

  return wrapper

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

    dummy_data.append((i, example)     )

  return dummy_data







def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Any			,							snake_case__  :Tuple			,							snake_case__  :str=100			,							snake_case__  :Optional[Any]=None     )      ->						Any:
  _lowercase							= generate_examples(snake_case__			,							num_examples=snake_case__			,							seq_shapes=snake_case__     )

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

    _lowercase						,		_lowercase							= 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}."""     )

  _lowercase							= datasets.Dataset.from_file(filename=snake_case__			,							info=datasets.DatasetInfo(features=snake_case__     )     )

  return dataset










# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)