






'''simple docstring'''





import logging

from transformers import PretrainedConfig


__UpperCamelCase      :      Union[str, Any]     						=						logging.getLogger(__name__)


__UpperCamelCase      :      Tuple     						=						{
    """bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json""",
}


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:Tuple   =       'bertabs'







	def __init__(  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							Any=3_0522       ,				_lowerCAmelCase      :							Optional[Any]=512       ,				_lowerCAmelCase      :							str=6       ,				_lowerCAmelCase      :							Union[str, Any]=512       ,				_lowerCAmelCase      :							List[str]=8       ,				_lowerCAmelCase      :							Any=512       ,				_lowerCAmelCase      :							Any=0.2       ,				_lowerCAmelCase      :							Union[str, Any]=6       ,				_lowerCAmelCase      :							Dict=768       ,				_lowerCAmelCase      :							List[Any]=8       ,				_lowerCAmelCase      :							str=2048       ,				_lowerCAmelCase      :							int=0.2       ,				**_lowerCAmelCase      :							List[str]       ,				)		-> Tuple:
		"""simple docstring"""







		super().__init__(**_lowerCAmelCase			)

		__lowercase             =  vocab_size
		__lowercase             =  max_pos

		__lowercase             =  enc_layers
		__lowercase             =  enc_hidden_size
		__lowercase             =  enc_heads
		__lowercase             =  enc_ff_size
		__lowercase             =  enc_dropout

		__lowercase             =  dec_layers
		__lowercase             =  dec_hidden_size
		__lowercase             =  dec_heads
		__lowercase             =  dec_ff_size
		__lowercase             =  dec_dropout









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







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		return self._get_dummy_components()







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Dict=0			)		-> Any:
		"""simple docstring"""







		if str(_lowerCAmelCase			).startswith("""mps"""			):
			__lowercase             =  torch.manual_seed(_lowerCAmelCase			)
		else:
			__lowercase             =  torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)

		__lowercase             =  floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_lowerCAmelCase			)			).to(_lowerCAmelCase			)
		__lowercase             =  floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_lowerCAmelCase			)			).to(_lowerCAmelCase			)

		__lowercase             =  {
		    """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      :							Tuple			)		-> Dict:
		"""simple docstring"""







		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)







	def  _a   (  self      :							Union[str, Any]			)		-> Dict:
		"""simple docstring"""







		self._test_save_load_optional_components()







	@unittest.skipIf(torch_device != """cuda"""       ,				reason="""float16 requires CUDA"""			)
	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







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







	def  _a   (  self      :							Tuple			)		-> Optional[Any]:
		"""simple docstring"""







		self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)







	def  _a   (  self      :							str			)		-> Optional[int]:
		"""simple docstring"""







		self._test_save_load_local()







	def  _a   (  self      :							List[str]			)		-> List[Any]:
		"""simple docstring"""







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

