



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    _A	(     __UpperCAmelCase				,__UpperCAmelCase				,unittest.TestCase			):
       UpperCamelCase__  :   List[Any]						 =						IFInpaintingPipeline
       UpperCamelCase__  :   List[Any]						 =						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
       UpperCamelCase__  :   int						 =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       UpperCamelCase__  :   int						 =						PipelineTesterMixin.required_optional_params - {'''latents'''}



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           return self._get_dummy_components()



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Dict=0):


           '''simple docstring'''

           if str(__SCREAMING_SNAKE_CASE).startswith('''mps'''):
               __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           else:
               __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)

           __a							=    floats_tensor((1, 3, 32, 32)					,		rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)
           __a							=    floats_tensor((1, 3, 32, 32)					,		rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)

           __a							=    {
               '''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 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           self._test_save_load_optional_components()



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


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           self._test_save_load_local()





       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case						:Tuple						=					{
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case						:Optional[int]						=					[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 __snake_case						:Dict						=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





