
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		(						A_       ,A_       ,unittest.TestCase ):
   A__					:		List[Any]						     =     IFInpaintingPipeline
   A__					:		List[str]						     =     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
   A__					:		int						     =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   A__					:		Optional[Any]						     =     PipelineTesterMixin.required_optional_params - {"latents"}



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Optional[int]:


          '''simple docstring'''


          return self._get_dummy_components()



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					Any    ,						snake_case__		:					int=0 )		->	Tuple:


          '''simple docstring'''


          if str(snake_case__ ).startswith("mps" ):
                 snake_case       :   Union[str, Any]							=      torch.manual_seed(snake_case__ )
          else:
                 snake_case       :   List[Any]							=      torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )

          snake_case       :   str							=      floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case__ ) ).to(snake_case__ )
          snake_case       :   Optional[Any]							=      floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case__ ) ).to(snake_case__ )

          snake_case       :   Any							=      {
              "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 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	int:


          '''simple docstring'''


          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Optional[Any]:


          '''simple docstring'''


          self._test_save_load_optional_components()



   @unittest.skipIf(torch_device != "cuda"    ,						reason="float16 requires CUDA" )
   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Tuple:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	List[Any]:


          '''simple docstring'''


          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Any:


          '''simple docstring'''


          self._test_save_load_local()



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	int:


          '''simple docstring'''


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







import pytest

from datasets import inspect_metric, list_metrics, load_metric


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]    ):
       monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"				,					set()    )


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : str    ):






       class   UpperCAmelCase		:



          def __init__(self		:					Optional[int]    ,						snake_case__		:					str )		->	Any:


                 '''simple docstring'''


                 snake_case       :   List[str]							=      metric_id






       class   UpperCAmelCase		:
          A__					:		List[str]						     =     [MetricMock(A_ ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]



          def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	List[str]:


                 '''simple docstring'''


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





