


'''simple docstring'''







# Lint as: python3
import itertools
import os
import re


a__  :					int        =       re.compile(R'([A-Z]+)([A-Z][a-z])')
a__  :					str        =       re.compile(R'([a-z\d])([A-Z])')

a__  :					Tuple        =       re.compile(R'(?<!_)_(?!_)')
a__  :					Union[str, Any]        =       re.compile(R'(_{2,})')

a__  :					Dict        =       R'^\w+(\.\w+)*$'

a__  :					Optional[Any]        =       R'<>:/\|?*'







def _UpperCamelCase       (     __A    )							->		Union[str, Any]:


      '''simple docstring'''



      UpperCamelCase__										=				_uppercase_uppercase_re.sub(R"\1_\2"      ,			__A    )
      UpperCamelCase__										=				_lowercase_uppercase_re.sub(R"\1_\2"      ,			__A    )
      return name.lower()







def _UpperCamelCase       (     __A    )							->		Optional[int]:


      '''simple docstring'''



      UpperCamelCase__										=				_single_underscore_re.split(__A    )
      UpperCamelCase__										=				[_multiple_underscores_re.split(__A    ) for n in name]
      return "".join(n.capitalize() for n in itertools.chain.from_iterable(__A    ) if n != ""    )







def _UpperCamelCase       (     __A    )							->		int:


      '''simple docstring'''



      if os.path.basename(__A    ) != name:
            raise ValueError(F'''Should be a dataset name, not a path: {name}'''    )
      return camelcase_to_snakecase(__A    )







def _UpperCamelCase       (     __A      ,			__A    )							->		str:


      '''simple docstring'''



      if os.path.basename(__A    ) != name:
            raise ValueError(F'''Should be a dataset name, not a path: {name}'''    )
      if not re.match(_split_re      ,			__A    ):
            raise ValueError(F'''Split name should match \'{_split_re}\'\' but got \'{split}\'.'''    )
      return F'''{filename_prefix_for_name(__A    )}-{split}'''







def _UpperCamelCase       (     __A      ,			__A      ,			__A      ,			__A=None    )							->		List[Any]:


      '''simple docstring'''



      UpperCamelCase__										=				filename_prefix_for_split(__A      ,			__A    )
      if filetype_suffix:
            prefix += F'''.{filetype_suffix}'''
      UpperCamelCase__										=				os.path.join(__A      ,			__A    )
      return F'''{filepath}*'''







def _UpperCamelCase       (     __A      ,			__A      ,			__A      ,			__A=None      ,			__A=None    )							->		Any:


      '''simple docstring'''



      UpperCamelCase__										=				filename_prefix_for_split(__A      ,			__A    )
      UpperCamelCase__										=				os.path.join(__A      ,			__A    )

      if shard_lengths:
            UpperCamelCase__										=				len(__A    )
            UpperCamelCase__										=				[F'''{prefix}-{shard_id:05d}-of-{num_shards:05d}''' for shard_id in range(__A    )]
            if filetype_suffix:
                  UpperCamelCase__										=				[filename + F'''.{filetype_suffix}''' for filename in filenames]
            return filenames
      else:
            UpperCamelCase__										=				prefix
            if filetype_suffix:
                  filename += F'''.{filetype_suffix}'''
            return [filename]









"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
	import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		@property
		def 							__A						(	self				)     ->							List[Any]:
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)







		@property
		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					ort.SessionOptions()
								SCREAMING_SNAKE_CASE													=					False
								return options







		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo.png'				)
								SCREAMING_SNAKE_CASE													=					load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'				)
								SCREAMING_SNAKE_CASE													=					load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'				)

								# using the PNDM scheduler by default
								SCREAMING_SNAKE_CASE													=					OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'						,       revision='onnx'						,       safety_checker=lowerCAmelCase__						,       feature_extractor=lowerCAmelCase__						,       provider=self.gpu_provider						,       sess_options=self.gpu_options						,       )
								pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					'A red cat sitting on a park bench'

								SCREAMING_SNAKE_CASE													=					np.random.RandomState(0				)
								SCREAMING_SNAKE_CASE													=					pipe(
								    prompt=lowerCAmelCase__						,       image=lowerCAmelCase__						,       mask_image=lowerCAmelCase__						,       strength=0.75						,       guidance_scale=7.5						,       num_inference_steps=15						,       generator=lowerCAmelCase__						,       output_type='np'						,       )

								SCREAMING_SNAKE_CASE													=					output.images[0]

								assert image.shape == (512, 512, 3)
								assert np.abs(expected_image - image				).max() < 1e-2


