



"""simple docstring"""







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__		(      __SCREAMING_SNAKE_CASE   ,__SCREAMING_SNAKE_CASE   ,unittest.TestCase     ):
		lowerCAmelCase_       :	Union[str, Any] 							=     IFInpaintingPipeline
		lowerCAmelCase_       :	str 							=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
		lowerCAmelCase_       :	int 							=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		lowerCAmelCase_       :	Optional[Any] 							=     PipelineTesterMixin.required_optional_params - {"""latents"""}



		def 					A_		( self  :    int				)			->						str:






									'''simple docstring'''

									return self._get_dummy_components()



		def 					A_		( self  :    List[str]      ,							snake_case  :    Tuple      ,							snake_case  :    Any=0				)			->						Optional[int]:






									'''simple docstring'''

									if str(_a				).startswith('mps'				):
																A         =					torch.manual_seed(_a				)
									else:
																A         =					torch.Generator(device=_a				).manual_seed(_a				)

									A         =					floats_tensor((1, 3, 32, 32)      ,							rng=random.Random(_a				)				).to(_a				)
									A         =					floats_tensor((1, 3, 32, 32)      ,							rng=random.Random(_a				)				).to(_a				)

									A         =					{
									    """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 					A_		( self  :    List[str]				)			->						Dict:






									'''simple docstring'''

									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)



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






									'''simple docstring'''

									self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != 'cuda'      ,							reason='float16 requires CUDA'				)
		def 					A_		( self  :    List[str]				)			->						Optional[int]:






									'''simple docstring'''

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



		def 					A_		( self  :    Dict				)			->						Dict:






									'''simple docstring'''

									self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)



		def 					A_		( self  :    Any				)			->						int:






									'''simple docstring'''

									self._test_save_load_local()



		def 					A_		( self  :    Dict				)			->						Union[str, Any]:






									'''simple docstring'''

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











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A    =			{
    'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'GPTNeoForCausalLM',
							    'GPTNeoForQuestionAnswering',
							    'GPTNeoForSequenceClassification',
							    'GPTNeoForTokenClassification',
							    'GPTNeoModel',
							    'GPTNeoPreTrainedModel',
							    'load_tf_weights_in_gpt_neo',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'FlaxGPTNeoForCausalLM',
							    'FlaxGPTNeoModel',
							    'FlaxGPTNeoPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neo import (
														    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoForCausalLM,
														    GPTNeoForQuestionAnswering,
														    GPTNeoForSequenceClassification,
														    GPTNeoForTokenClassification,
														    GPTNeoModel,
														    GPTNeoPreTrainedModel,
														    load_tf_weights_in_gpt_neo,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



