






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      (	lowercase_			,      lowercase_			,      unittest.TestCase  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				IFInpaintingPipeline
     SCREAMING_SNAKE_CASE   						=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
     SCREAMING_SNAKE_CASE   						=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     SCREAMING_SNAKE_CASE   						=				PipelineTesterMixin.required_optional_params - {"latents"}




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         return self._get_dummy_components()




     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			Any=0				):


         """simple docstring"""



         if str(UpperCamelCase_				).startswith("""mps"""				):
             __A         =	torch.manual_seed(UpperCamelCase_				)
         else:
             __A         =	torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)

         __A         =	floats_tensor((1, 3, 32, 32) ,	rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)
         __A         =	floats_tensor((1, 3, 32, 32) ,	rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)

         __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 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3				)




     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         self._test_save_load_optional_components()




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


         """simple docstring"""



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




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


         """simple docstring"""



         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2				)




     def 		lowerCAmelCase_     (				self       :			List[Any]				):


         """simple docstring"""



         self._test_save_load_local()





     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



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












from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				["image_processor", "tokenizer"]
     SCREAMING_SNAKE_CASE   						=				"AutoImageProcessor"
     SCREAMING_SNAKE_CASE   						=				"AutoTokenizer"




     def __init__(				self       :			str ,	UpperCamelCase_       :			Any ,	UpperCamelCase_       :			str				):


         """simple docstring"""



         super().__init__(UpperCamelCase_ ,	UpperCamelCase_				)
         __A         =	self.image_processor




     def __call__(				self       :			Optional[Any] ,	UpperCamelCase_       :			Optional[Any]=None ,	UpperCamelCase_       :			Any=None ,	UpperCamelCase_       :			List[Any]=None ,	**UpperCamelCase_       :			int				):


         """simple docstring"""



         if text is None and images is None:
             raise ValueError("""You have to specify either text or images. Both cannot be none."""				)

         if text is not None:
             __A         =	self.tokenizer(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

         if images is not None:
             __A         =	self.image_processor(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

         if text is not None and images is not None:
             __A         =	image_features.pixel_values
             return encoding
         elif text is not None:
             return encoding
         else:
             return BatchEncoding(data=dict(**UpperCamelCase_				) ,	tensor_type=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			List[str] ,	*UpperCamelCase_       :			Tuple ,	**UpperCamelCase_       :			List[str]				):


         """simple docstring"""



         return self.tokenizer.batch_decode(*UpperCamelCase_ ,	**UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any ,	*UpperCamelCase_       :			int ,	**UpperCamelCase_       :			Tuple				):


         """simple docstring"""



         return self.tokenizer.decode(*UpperCamelCase_ ,	**UpperCamelCase_				)





     @property
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         return ["input_ids", "attention_mask", "pixel_values"]




