

import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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   UpperCamelCase			(  SCREAMING_SNAKE_CASE						,      SCREAMING_SNAKE_CASE						,      unittest.TestCase ):
    __UpperCamelCase    =IFInpaintingPipeline
    __UpperCamelCase    =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
    __UpperCamelCase    =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    __UpperCamelCase    =PipelineTesterMixin.required_optional_params - {"latents"}

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     return self._get_dummy_components()

    def UpperCamelCase      (      self			:   Union[str, Any]				,     snake_case__			:   int				,     snake_case__			:   Optional[int]=0     ):

     """simple docstring"""






     if str(snake_case__     ).startswith('mps'     ):
      SCREAMING_SNAKE_CASE		    =      torch.manual_seed(snake_case__     )
     else:
      SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      floats_tensor((1, 3, 3_2, 3_2)				,     rng=random.Random(snake_case__     )     ).to(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      floats_tensor((1, 3, 3_2, 3_2)				,     rng=random.Random(snake_case__     )     ).to(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      {
         'prompt': 'A painting of a squirrel eating a burger',
         'image': 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 UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     self._test_save_load_optional_components()

    @unittest.skipIf(torch_device != 'cuda'				,     reason='float16 requires CUDA'     )
    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     super().test_save_load_floataa(expected_max_diff=1E-1     )

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     self._test_save_load_local()




    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     self._test_inference_batch_single_identical(
         expected_max_diff=1E-2				,     )




import re

from filelock import FileLock


try:
      import nltk

      a_     :    Optional[Any]     		=   True
except (ImportError, ModuleNotFoundError):
      a_     :    Union[str, Any]     		=   False

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				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						)						)

