





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 					A__   (       __snake_case       ,   __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:Optional[Any]						    =   IFInpaintingSuperResolutionPipeline
      _UpperCAmelCase	:List[str]						    =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
      _UpperCAmelCase	:List[Any]						    =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}			)
      _UpperCAmelCase	:Optional[Any]						    =   PipelineTesterMixin.required_optional_params - {'latents'}





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return self._get_superresolution_dummy_components()





      def        __UpperCamelCase( self       ,							A_       ,							A_=0   ):
       '''simple docstring'''




       if str(A_   ).startswith("mps"   ):
        UpperCamelCase	:					Any       	=   torch.manual_seed(A_   )
       else:
        UpperCamelCase	:					Optional[int]       	=   torch.Generator(device=A_   ).manual_seed(A_   )

       UpperCamelCase	:					Optional[int]       	=   floats_tensor((1, 3, 16, 16)       ,							rng=random.Random(A_   )   ).to(A_   )
       UpperCamelCase	:					List[str]       	=   floats_tensor((1, 3, 32, 32)       ,							rng=random.Random(A_   )   ).to(A_   )
       UpperCamelCase	:					str       	=   floats_tensor((1, 3, 32, 32)       ,							rng=random.Random(A_   )   ).to(A_   )

       UpperCamelCase	:					str       	=   {
           "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   ):
       '''simple docstring'''




       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self._test_save_load_optional_components()





      @unittest.skipIf(torch_device != "cuda"       ,							reason="float16 requires CUDA"   )
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self._test_save_load_local()





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCamelCase       :     str   =     logging.get_logger(__name__)

__lowerCamelCase       :     str   =     {
    """facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}



class 					A__   (       __snake_case       ,   __snake_case			):
      _UpperCAmelCase	:Optional[int]						    =   'convnextv2'





      def __init__( self       ,							A_=3       ,							A_=4       ,							A_=4       ,							A_=None       ,							A_=None       ,							A_="gelu"       ,							A_=0.02       ,							A_=1e-12       ,							A_=0.0       ,							A_=224       ,							A_=None       ,							A_=None       ,							**A_       ,							):
       '''simple docstring'''




       super().__init__(**A_   )

       UpperCamelCase	:					Dict       	=   num_channels
       UpperCamelCase	:					Union[str, Any]       	=   patch_size
       UpperCamelCase	:					Union[str, Any]       	=   num_stages
       UpperCamelCase	:					List[Any]       	=   [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
       UpperCamelCase	:					List[str]       	=   [3, 3, 9, 3] if depths is None else depths
       UpperCamelCase	:					Dict       	=   hidden_act
       UpperCamelCase	:					Union[str, Any]       	=   initializer_range
       UpperCamelCase	:					Tuple       	=   layer_norm_eps
       UpperCamelCase	:					str       	=   drop_path_rate
       UpperCamelCase	:					List[str]       	=   image_size
       UpperCamelCase	:					List[str]       	=   ["stem"] + [F"""stage{idx}""" for idx in range(1       ,							len(self.depths   ) + 1   )]
       UpperCamelCase	,    UpperCamelCase	:					str       	=   get_aligned_output_features_output_indices(
           out_features=A_       ,							out_indices=A_       ,							stage_names=self.stage_names   )
