



'''simple docstring'''





import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_lowerCAmelCase										=     logging.get_logger(__name__)






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
		'''simple docstring'''




		def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		None:
						warnings.warn(
						    """The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
						    """ use CLIPImageProcessor instead."""  ,__UpperCAmelCase  ,)
						super().__init__(*__UpperCAmelCase  ,**__UpperCAmelCase				)







'''simple docstring'''





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 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




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




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      return self._get_superresolution_dummy_components()




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase=0				)   ->		List[Any]:
      if str(__UpperCAmelCase				).startswith("""mps"""				):
          lowerCAmelCase__			:  Any											= torch.manual_seed(__UpperCAmelCase				)
      else:
          lowerCAmelCase__			:  Dict											= torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)

      lowerCAmelCase__			:  Tuple											= floats_tensor((1, 3, 16, 16)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  Union[str, Any]											= floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)

      lowerCAmelCase__			:  Dict											= {
          """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				)   ->		Union[str, Any]:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      self._test_save_load_optional_components()




  @unittest.skipIf(torch_device != """cuda"""  ,reason="""float16 requires CUDA"""				)
  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1E-1				)




  def 							UpperCAmelCase_     (   self				)   ->		str:
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      self._test_save_load_local()






  def 							UpperCAmelCase_     (   self				)   ->		int:
      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2  ,)


