'''simple docstring'''


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 					__SCREAMING_SNAKE_CASE						(	SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Any						   =		IFInpaintingPipeline
  lowerCamelCase_		:Tuple						   =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
  lowerCamelCase_		:Optional[int]						   =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  lowerCamelCase_		:Dict						   =		PipelineTesterMixin.required_optional_params - {'latents'}


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return self._get_dummy_components()


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_=0						):




       '''simple docstring'''

       if str(a_						).startswith('mps'						):
            UpperCAmelCase_							:       Optional[int]       =				torch.manual_seed(a_						)
       else:
            UpperCAmelCase_							:       int       =				torch.Generator(device=a_						).manual_seed(a_						)

       UpperCAmelCase_							:       Optional[int]       =				floats_tensor((1, 3, 3_2, 3_2)				,		rng=random.Random(a_						)						).to(a_						)
       UpperCAmelCase_							:       List[str]       =				floats_tensor((1, 3, 3_2, 3_2)				,		rng=random.Random(a_						)						).to(a_						)

       UpperCAmelCase_							:       List[str]       =				{
           '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						):




       '''simple docstring'''

       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self._test_save_load_optional_components()


  @unittest.skipIf(torch_device != 'cuda'				,		reason='float16 requires CUDA'						)
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self._test_save_load_local()






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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







'''simple docstring'''


snake_case__					:      str				      =  '''Tobias Carryer'''

from time import time






class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_=int(time()						)						):  # noqa: B008




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				multiplier
       UpperCAmelCase_							:       Dict       =				increment
       UpperCAmelCase_							:       Tuple       =				modulo
       UpperCAmelCase_							:       Dict       =				seed






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				(self.multiplier * self.seed + self.increment) % self.modulo
       return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 snake_case__					:      Any				      =  LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
 while True:
  print(lcg.next_number())






