



'''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 		lowerCamelCase__      (						A	,   A	,   unittest.TestCase     ):




					'''simple docstring'''



					A_						    =						IFInpaintingPipeline
					A_						    =						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
					A_						    =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					A_						    =						PipelineTesterMixin.required_optional_params - {"""latents"""}




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Any:



						'''simple docstring'''





						return self._get_dummy_components()




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	str=0					)  ->       Dict:



						'''simple docstring'''





						if str(UpperCamelCase_					).startswith('mps'					):
							_lowercase					:       List[str]       =		torch.manual_seed(UpperCamelCase_					)
						else:
							_lowercase					:       List[str]       =		torch.Generator(device=UpperCamelCase_					).manual_seed(UpperCamelCase_					)

						_lowercase					:       str       =		floats_tensor((1, 3, 32, 32)     ,  rng=random.Random(UpperCamelCase_					)					).to(UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		floats_tensor((1, 3, 32, 32)     ,  rng=random.Random(UpperCamelCase_					)					).to(UpperCamelCase_					)

						_lowercase					:       List[str]       =		{
						    '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				:	Tuple					)  ->       str:



						'''simple docstring'''





						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)




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



						'''simple docstring'''





						self._test_save_load_optional_components()




					@unittest.skipIf(torch_device != 'cuda'     ,  reason='float16 requires CUDA'					)
					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Optional[Any]:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	int					)  ->       Any:



						'''simple docstring'''





						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Optional[int]:



						'''simple docstring'''





						self._test_save_load_local()




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Optional[Any]:



						'''simple docstring'''





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








'''simple docstring'''

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


_A				:    Optional[Any]     						=logging.get_logger(__name__)

_A				:    Optional[int]     						={
    '''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
    '''microsoft/markuplm-large''': '''https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json''',
}



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""markuplm"""




					def __init__(  self				:	int     ,  UpperCamelCase_				:	Optional[Any]=3_0522     ,  UpperCamelCase_				:	Optional[Any]=768     ,  UpperCamelCase_				:	Tuple=12     ,  UpperCamelCase_				:	Union[str, Any]=12     ,  UpperCamelCase_				:	Tuple=3072     ,  UpperCamelCase_				:	Union[str, Any]="gelu"     ,  UpperCamelCase_				:	List[Any]=0.1     ,  UpperCamelCase_				:	List[Any]=0.1     ,  UpperCamelCase_				:	Dict=512     ,  UpperCamelCase_				:	Optional[int]=2     ,  UpperCamelCase_				:	Any=0.02     ,  UpperCamelCase_				:	Optional[Any]=1E-12     ,  UpperCamelCase_				:	List[str]=0     ,  UpperCamelCase_				:	Optional[int]=0     ,  UpperCamelCase_				:	Tuple=2     ,  UpperCamelCase_				:	str=256     ,  UpperCamelCase_				:	Optional[Any]=1024     ,  UpperCamelCase_				:	Union[str, Any]=216     ,  UpperCamelCase_				:	int=1001     ,  UpperCamelCase_				:	int=32     ,  UpperCamelCase_				:	int=50     ,  UpperCamelCase_				:	str="absolute"     ,  UpperCamelCase_				:	Union[str, Any]=True     ,  UpperCamelCase_				:	Optional[int]=None     ,  **UpperCamelCase_				:	Any     ,  )  ->       Optional[int]:



						'''simple docstring'''





						super().__init__(
						    pad_token_id=UpperCamelCase_     ,  bos_token_id=UpperCamelCase_     ,  eos_token_id=UpperCamelCase_     ,  **UpperCamelCase_     ,  )
						_lowercase					:       List[Any]       =		vocab_size
						_lowercase					:       Union[str, Any]       =		hidden_size
						_lowercase					:       Dict       =		num_hidden_layers
						_lowercase					:       Optional[Any]       =		num_attention_heads
						_lowercase					:       Dict       =		hidden_act
						_lowercase					:       Optional[Any]       =		intermediate_size
						_lowercase					:       Optional[int]       =		hidden_dropout_prob
						_lowercase					:       Union[str, Any]       =		attention_probs_dropout_prob
						_lowercase					:       Any       =		max_position_embeddings
						_lowercase					:       List[Any]       =		type_vocab_size
						_lowercase					:       Union[str, Any]       =		initializer_range
						_lowercase					:       Optional[int]       =		layer_norm_eps
						_lowercase					:       Optional[Any]       =		position_embedding_type
						_lowercase					:       str       =		use_cache
						_lowercase					:       str       =		classifier_dropout
						# additional properties
						_lowercase					:       int       =		max_depth
						_lowercase					:       Dict       =		max_xpath_tag_unit_embeddings
						_lowercase					:       str       =		max_xpath_subs_unit_embeddings
						_lowercase					:       List[str]       =		tag_pad_id
						_lowercase					:       Optional[int]       =		subs_pad_id
						_lowercase					:       Any       =		xpath_unit_hidden_size
