
"""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 	_lowercase       (    __UpperCAmelCase    ,	__UpperCAmelCase    ,	unittest.TestCase		):
	lowercase_				=     IFInpaintingPipeline
	lowercase_				=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
	lowercase_				=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	lowercase_				=     PipelineTesterMixin.required_optional_params - {'latents'}




	def       _UpperCamelCase   (			self )		->							str:
			return self._get_dummy_components()




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=0 )		->							Union[str, Any]:
			if str(UpperCAmelCase_ ).startswith('mps' ):
					lowerCamelCase					:      List[Any]          =   torch.manual_seed(UpperCAmelCase_ )
			else:
					lowerCamelCase					:      int          =   torch.Generator(device=UpperCAmelCase_ ).manual_seed(UpperCAmelCase_ )

			lowerCamelCase					:      int          =   floats_tensor((1, 3, 32, 32)		,			rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
			lowerCamelCase					:      List[Any]          =   floats_tensor((1, 3, 32, 32)		,			rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )

			lowerCamelCase					:      Dict          =   {
			    '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       _UpperCamelCase   (			self )		->							Any:
			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )




	def       _UpperCamelCase   (			self )		->							Dict:
			self._test_save_load_optional_components()




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




	def       _UpperCamelCase   (			self )		->							int:
			self._test_save_load_local()




	def       _UpperCamelCase   (			self )		->							List[str]:
			self._test_inference_batch_single_identical(
			    expected_max_diff=1E-2		,			)

"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A						=			logging.get_logger(__name__)






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'encoder-decoder'
	lowercase_				=     True




	def __init__(			self		,			**UpperCAmelCase_ )		->							str:
			super().__init__(**UpperCAmelCase_ )
			assert (
			    "encoder" in kwargs and "decoder" in kwargs
			), "Config has to be initialized with encoder and decoder config"
			lowerCamelCase					:      List[Any]          =   kwargs.pop('encoder' )
			lowerCamelCase					:      Optional[int]          =   encoder_config.pop('model_type' )
			lowerCamelCase					:      str          =   kwargs.pop('decoder' )
			lowerCamelCase					:      Dict          =   decoder_config.pop('model_type' )

			from ..auto.configuration_auto import AutoConfig

			lowerCamelCase					:      int          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   True




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )		->							PretrainedConfig:
			logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config' )
			lowerCamelCase					:      str          =   True
			lowerCamelCase					:      Optional[Any]          =   True

			return cls(encoder=encoder_config.to_dict()		,			decoder=decoder_config.to_dict()		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Union[str, Any]          =   copy.deepcopy(self.__dict__ )
			lowerCamelCase					:      Union[str, Any]          =   self.encoder.to_dict()
			lowerCamelCase					:      List[Any]          =   self.decoder.to_dict()
			lowerCamelCase					:      Tuple          =   self.__class__.model_type
			return output






