
'''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  A  (						SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   unittest.TestCase     ):
	snake_case__   :str   =  IFInpaintingPipeline
	snake_case__   :List[str]   =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
	snake_case__   :Optional[Any]   =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	snake_case__   :int   =  PipelineTesterMixin.required_optional_params - {'latents'}





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				return self._get_dummy_components()





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str   ,				__magic_name__      :							Union[str, Any]   ,				__magic_name__      :							Tuple=0				):


				"""simple docstring"""

				if str(__magic_name__				).startswith("mps"				):
							lowerCAmelCase__										=				torch.manual_seed(__magic_name__				)
				else:
							lowerCAmelCase__										=				torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)

				lowerCAmelCase__										=				floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__magic_name__				)				).to(__magic_name__				)
				lowerCAmelCase__										=				floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__magic_name__				)				).to(__magic_name__				)

				lowerCAmelCase__										=				{
				    "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 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				self._test_save_load_optional_components()





	@unittest.skipIf(torch_device != "cuda"   ,				reason="float16 requires CUDA"				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				self._test_save_load_local()







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

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










"""simple docstring"""







from typing import TYPE_CHECKING

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


a_							=    {
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXJapaneseForCausalLM""",
	    """GPTNeoXJapaneseLayer""",
	    """GPTNeoXJapaneseModel""",
	    """GPTNeoXJapanesePreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	a_							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





