


"""simple docstring"""







import pytest

from datasets import inspect_metric, list_metrics, load_metric


@pytest.fixture
def      lowercase			(				A_       )->      List[Any]:
  '''simple docstring'''







  monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"  ,   set()       )


@pytest.fixture
def      lowercase			(				A_       )->      Tuple:
  '''simple docstring'''







  class 						_A  :







      """simple docstring"""




      def __init__(					self				:    str				, __UpperCAmelCase				:    int):
        a						:		List[Any]      							=			metric_id






  class 						_A  :







      """simple docstring"""




      UpperCAmelCase       :							Union[str, Any]	=       [MetricMock(_a  ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]






      def 	__snake_case							(					self				:    List[str]):
        return self._metrics






  monkeypatch.setattr("datasets.inspect.huggingface_hub"  ,   HfhMock()       )







@pytest.mark.parametrize(
    "func, args"  ,   [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))]       )
def      lowercase			(				A_  ,   A_  ,   A_  ,   A_  ,   A_       )->      Any:
  '''simple docstring'''







  if "tmp_path" in args:
    a						:		Union[str, Any]      							=			tuple(arg if arg != "tmp_path" else tmp_path for arg in args       )
  with pytest.warns(A_  ,   match="https://huggingface.co/docs/evaluate"       ):
    func(*A_       )









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 						__magic_name__			(							lowerCamelCase__		,       lowerCamelCase__		,       unittest.TestCase							):

   '''simple docstring'''




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



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     return self._get_superresolution_dummy_components()



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_=0       )							->    Tuple:






     """simple docstring"""






     if str(lowercase_       ).startswith('''mps'''       ):
       a__  =torch.manual_seed(lowercase_       )
     else:
       a__  =torch.Generator(device=lowercase_       ).manual_seed(lowercase_       )

     a__  =floats_tensor((1, 3, 16, 16),    rng=random.Random(lowercase_       )       ).to(lowercase_       )
     a__  =floats_tensor((1, 3, 32, 32),    rng=random.Random(lowercase_       )       ).to(lowercase_       )
     a__  =floats_tensor((1, 3, 32, 32),    rng=random.Random(lowercase_       )       ).to(lowercase_       )

     a__  ={
         '''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  _UpperCAmelCase (				self       )							->    List[str]:






     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     self._test_save_load_optional_components()



   @unittest.skipIf(torch_device != '''cuda''',    reason='''float16 requires CUDA'''       )
   def  _UpperCAmelCase (				self       )							->    List[Any]:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )



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






     """simple docstring"""






     self._test_save_load_local()



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






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