


import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 		_snake_case  (				_snake_case						,   _snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	IFImgaImgSuperResolutionPipeline
 SCREAMING_SNAKE_CASE__				=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
 SCREAMING_SNAKE_CASE__				=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}							)
 SCREAMING_SNAKE_CASE__				=	PipelineTesterMixin.required_optional_params - {'''latents'''}




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return self._get_superresolution_dummy_components()




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=0   ):
       if str(_a   ).startswith('''mps'''   ):
             a						:Union[str, Any]										=	torch.manual_seed(_a   )
       else:
             a						:Union[str, Any]										=	torch.Generator(device=_a   ).manual_seed(_a   )

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

       a						:Any										=	{
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': image,
           '''original_image''': original_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   ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       self._test_save_load_optional_components()




 @unittest.skipIf(torch_device != '''cuda''' ,  reason='''float16 requires CUDA'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # 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 	SCREAMING_SNAKE_CASE__    (		self   ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       self._test_save_load_local()




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









from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
     if not (is_transformers_available() and is_torch_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .multicontrolnet import MultiControlNetModel
     from .pipeline_controlnet import StableDiffusionControlNetPipeline
     from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
     from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
     from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline





