

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   __lowercase				( UpperCAmelCase_		,       UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







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



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									return self._get_superresolution_dummy_components()



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     str=0):
									if str(lowerCAmelCase__).startswith("mps"):
															SCREAMING_SNAKE_CASE_: Tuple  		=       torch.manual_seed(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_: str  		=       torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       floats_tensor((1, 3, 16, 16)   ,						rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       floats_tensor((1, 3, 32, 32)   ,						rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       floats_tensor((1, 3, 32, 32)   ,						rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Any  		=       {
									    "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        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									self._test_save_load_optional_components()



			@unittest.skipIf(torch_device != "cuda"   ,						reason="float16 requires CUDA")
			def        _SCREAMING_SNAKE_CASE			( self  :     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        _SCREAMING_SNAKE_CASE			( self  :     int):
									self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									self._test_save_load_local()




			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									self._test_inference_batch_single_identical(
									    expected_max_diff=1E-2   ,						)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:  Optional[int]   	=						{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Any   	=						[
						    """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """WavLMForAudioFrameClassification""",
						    """WavLMForCTC""",
						    """WavLMForSequenceClassification""",
						    """WavLMForXVector""",
						    """WavLMModel""",
						    """WavLMPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavlm import (
												    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavLMForAudioFrameClassification,
												    WavLMForCTC,
												    WavLMForSequenceClassification,
												    WavLMForXVector,
												    WavLMModel,
												    WavLMPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase							:  Union[str, Any]   	=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






