






import timeit

import numpy as np

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


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  def wrapper(*lowercase						,     **lowercase				):
    SCREAMING_SNAKE_CASE       :      Tuple							=    timeit.default_timer()
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    func(*lowercase						,     **lowercase				)
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    timeit.default_timer() - starttime
    return delta

  SCREAMING_SNAKE_CASE       :      Any							=    func.__name__

  return wrapper


def   lowerCamelCase__			(     lowercase						,     lowercase=100						,     lowercase=None				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[str]							=    []
  SCREAMING_SNAKE_CASE       :      Any							=    seq_shapes or {}
  for i in range(lowercase				):
    SCREAMING_SNAKE_CASE       :      Dict							=    {}
    for col_id, (k, v) in enumerate(features.items()				):
      if isinstance(lowercase						,     _ArrayXD				):
        SCREAMING_SNAKE_CASE       :      str							=    np.random.rand(*v.shape				).astype(v.dtype				)
      elif isinstance(lowercase						,     datasets.Value				):
        if v.dtype == "string":
          SCREAMING_SNAKE_CASE       :      Dict							=    "The small grey turtle was surprisingly fast when challenged."
        else:
          SCREAMING_SNAKE_CASE       :      List[Any]							=    np.random.randint(10						,     size=1				).astype(v.dtype				).item()
      elif isinstance(lowercase						,     datasets.Sequence				):
        while isinstance(lowercase						,     datasets.Sequence				):
          SCREAMING_SNAKE_CASE       :      List[str]							=    v.feature
        SCREAMING_SNAKE_CASE       :      Any							=    seq_shapes[k]
        SCREAMING_SNAKE_CASE       :      Tuple							=    np.random.rand(*lowercase				).astype(v.dtype				)
      SCREAMING_SNAKE_CASE       :      int							=    data

    dummy_data.append((i, example)				)

  return dummy_data


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase=100						,     lowercase=None				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Any							=    generate_examples(lowercase						,     num_examples=lowercase						,     seq_shapes=lowercase				)

  with ArrowWriter(features=lowercase						,     path=lowercase				) as writer:
    for key, record in dummy_data:
      SCREAMING_SNAKE_CASE       :      List[str]							=    features.encode_example(lowercase				)
      writer.write(lowercase				)

    SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      List[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}.'''				)

  SCREAMING_SNAKE_CASE       :      Any							=    datasets.Dataset.from_file(filename=lowercase						,     info=datasets.DatasetInfo(features=lowercase				)				)

  return dataset














from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .multicontrolnet import MultiControlNetModel
   from .pipeline_controlnet import StableDiffusionControlNetPipeline
   from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
   from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
   from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline






