






"""simple docstring"""

import re

from filelock import FileLock


try:
					import nltk

					lowercase__          = True
except (ImportError, ModuleNotFoundError):
					lowercase__          = False

if NLTK_AVAILABLE:
					with FileLock(""".lock""") as lock:
										nltk.download("""punkt""", quiet=True)

def 		__lowerCamelCase	(  __UpperCamelCase       )						->    str:

		"""simple docstring"""







		re.sub("<n>"      ,       ""      ,       __UpperCamelCase       )  # remove pegasus newline char
		assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
		return "\n".join(nltk.sent_tokenize(__UpperCamelCase       )       )








"""simple docstring"""

import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 		__lowerCamelCase				( A__ ,							A__ ,							unittest.TestCase			):





		'''simple docstring'''






		a_	:				Optional[Any]            =							IFInpaintingSuperResolutionPipeline
		a_	:				int            =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
		a_	:				List[str]            =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}			)
		a_	:				int            =							PipelineTesterMixin.required_optional_params - {"""latents"""}





		def    lowerCamelCase       (   self							:	Optional[Any]	):
				return self._get_superresolution_dummy_components()





		def    lowerCamelCase       (   self							:	Optional[Any]						,			a_							:	List[str]						,			a_							:	Union[str, Any]=0	):
				if str(a_	).startswith("mps"	):
						lowerCAmelCase_   :							List[Any]									=			torch.manual_seed(a_	)
				else:
						lowerCAmelCase_   :							str									=			torch.Generator(device=a_	).manual_seed(a_	)

				lowerCAmelCase_   :							List[str]									=			floats_tensor((1, 3, 16, 16)						,			rng=random.Random(a_	)	).to(a_	)
				lowerCAmelCase_   :							Tuple									=			floats_tensor((1, 3, 32, 32)						,			rng=random.Random(a_	)	).to(a_	)
				lowerCAmelCase_   :							int									=			floats_tensor((1, 3, 32, 32)						,			rng=random.Random(a_	)	).to(a_	)

				lowerCAmelCase_   :							Any									=			{
				    "prompt": "A painting of a squirrel eating a burger",
				    "image": image,
				    "original_image": original_image,
				    "mask_image": mask_image,
				    "generator": generator,
				    "num_inference_steps": 2,
				    "output_type": "numpy",
				}

				return inputs





		@unittest.skipIf(
		    torch_device != "cuda" or not is_xformers_available()						,			reason="XFormers attention is only available with CUDA and `xformers` installed"						,			)
		def    lowerCamelCase       (   self							:	List[Any]	):
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)





		def    lowerCamelCase       (   self							:	Optional[int]	):
				self._test_save_load_optional_components()





		@unittest.skipIf(torch_device != "cuda"						,			reason="float16 requires CUDA"	)
		def    lowerCamelCase       (   self							:	Optional[Any]	):
				# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
				super().test_save_load_floataa(expected_max_diff=1e-1	)





		def    lowerCamelCase       (   self							:	Tuple	):
				self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)





		def    lowerCamelCase       (   self							:	List[str]	):
				self._test_save_load_local()





		def    lowerCamelCase       (   self							:	Optional[int]	):
				self._test_inference_batch_single_identical(
				    expected_max_diff=1e-2						,			)
