
import timeit

import numpy as np

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






def 		_UpperCAmelCase   (	UpperCamelCase:     int  ):






	"""simple docstring"""


	def wrapper(*UpperCamelCase:     int			,							**UpperCamelCase:     int  ):
		__lowerCAmelCase         =							timeit.default_timer()
		__lowerCAmelCase         =							func(*_lowerCamelCase			,							**_lowerCamelCase  )
		__lowerCAmelCase         =							timeit.default_timer() - starttime
		return delta

	__lowerCAmelCase         =							func.__name__

	return wrapper






def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     List[str]=1_0_0			,							UpperCamelCase:     Dict=None  ):






	"""simple docstring"""


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

		dummy_data.append((i, example)  )

	return dummy_data







def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[int]			,							UpperCamelCase:     str			,							UpperCamelCase:     Dict=1_0_0			,							UpperCamelCase:     str=None  ):






	"""simple docstring"""


	__lowerCAmelCase         =							generate_examples(_lowerCamelCase			,							num_examples=_lowerCamelCase			,							seq_shapes=_lowerCamelCase  )

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

		__lowerCAmelCase         =							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}."  )

	__lowerCAmelCase         =							datasets.Dataset.from_file(filename=_lowerCamelCase			,							info=datasets.DatasetInfo(features=_lowerCamelCase  )  )

	return dataset








import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


UpperCamelCase_   	=	False

class       a		(   unittest.TestCase					):
							pass

@slow
@require_torch_gpu
class       a		(   unittest.TestCase					):







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion" )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
								__lowerCAmelCase         =							torch.manual_seed(0 )
								__lowerCAmelCase         =							pipe(
								    image=snake_case__      ,				generator=snake_case__      ,				guidance_scale=7.5      ,				num_inference_steps=50      ,				output_type="numpy"      ,				).images

								__lowerCAmelCase         =							image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								__lowerCAmelCase         =							np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5] )

								assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






