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   __lowercase				(  __snake_case ,					__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        _lowercase				(		self						:					Any       )    ->   int:






         """simple docstring"""







         return self._get_dummy_components()





      def        _lowercase				(		self						:					Optional[Any]					,       __lowerCamelCase						:					Optional[int]					,       __lowerCamelCase						:					str=0       )    ->   Dict:






         """simple docstring"""







         if str(__lowerCamelCase       ).startswith("""mps"""       ):
            UpperCAmelCase							=     torch.manual_seed(__lowerCamelCase       )
         else:
            UpperCAmelCase							=     torch.Generator(device=__lowerCamelCase       ).manual_seed(__lowerCamelCase       )

         UpperCAmelCase							=     floats_tensor((1, 3, 3_2, 3_2)					,       rng=random.Random(__lowerCamelCase       )       ).to(__lowerCamelCase       )
         UpperCAmelCase							=     floats_tensor((1, 3, 3_2, 3_2)					,       rng=random.Random(__lowerCamelCase       )       ).to(__lowerCamelCase       )

         UpperCAmelCase							=     {
             """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        _lowercase				(		self						:					Dict       )    ->   List[Any]:






         """simple docstring"""







         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )





      def        _lowercase				(		self						:					Union[str, Any]       )    ->   Optional[int]:






         """simple docstring"""







         self._test_save_load_optional_components()





      @unittest.skipIf(torch_device != """cuda"""					,       reason="""float16 requires CUDA"""       )
      def        _lowercase				(		self						:					int       )    ->   List[str]:






         """simple docstring"""







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





      def        _lowercase				(		self						:					Dict       )    ->   List[str]:






         """simple docstring"""







         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Any:






         """simple docstring"""







         self._test_save_load_local()





      def        _lowercase				(		self						:					Tuple       )    ->   str:






         """simple docstring"""







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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


__a     =			{"""tokenization_herbert""": ["""HerbertTokenizer"""]}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a     =			["""HerbertTokenizerFast"""]


if TYPE_CHECKING:
 from .tokenization_herbert import HerbertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_herbert_fast import HerbertTokenizerFast

else:
 import sys

 __a     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



