






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


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{
    """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  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[int]			=	'''markuplm'''


     def __init__(				self       :	int					, UpperCAmelCase_       :	List[str]=3_0522					, UpperCAmelCase_       :	Optional[Any]=768					, UpperCAmelCase_       :	Any=12					, UpperCAmelCase_       :	Tuple=12					, UpperCAmelCase_       :	Union[str, Any]=3072					, UpperCAmelCase_       :	Dict="gelu"					, UpperCAmelCase_       :	Optional[int]=0.1					, UpperCAmelCase_       :	Tuple=0.1					, UpperCAmelCase_       :	Dict=512					, UpperCAmelCase_       :	Optional[int]=2					, UpperCAmelCase_       :	Any=0.02					, UpperCAmelCase_       :	Dict=1E-12					, UpperCAmelCase_       :	int=0					, UpperCAmelCase_       :	Optional[Any]=0					, UpperCAmelCase_       :	Optional[int]=2					, UpperCAmelCase_       :	str=256					, UpperCAmelCase_       :	str=1024					, UpperCAmelCase_       :	List[str]=216					, UpperCAmelCase_       :	List[Any]=1001					, UpperCAmelCase_       :	Union[str, Any]=32					, UpperCAmelCase_       :	Dict=50					, UpperCAmelCase_       :	Optional[int]="absolute"					, UpperCAmelCase_       :	Optional[int]=True					, UpperCAmelCase_       :	Any=None					, **UpperCAmelCase_       :	Any					, ):
       super().__init__(
           pad_token_id=UpperCAmelCase_					, bos_token_id=UpperCAmelCase_					, eos_token_id=UpperCAmelCase_					, **UpperCAmelCase_					, )
       SCREAMING_SNAKE_CASE       :      Any							=    vocab_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    hidden_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    num_hidden_layers
       SCREAMING_SNAKE_CASE       :      Tuple							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      List[str]							=    hidden_act
       SCREAMING_SNAKE_CASE       :      Tuple							=    intermediate_size
       SCREAMING_SNAKE_CASE       :      Any							=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE       :      int							=    max_position_embeddings
       SCREAMING_SNAKE_CASE       :      Tuple							=    type_vocab_size
       SCREAMING_SNAKE_CASE       :      Any							=    initializer_range
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    layer_norm_eps
       SCREAMING_SNAKE_CASE       :      int							=    position_embedding_type
       SCREAMING_SNAKE_CASE       :      List[str]							=    use_cache
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    classifier_dropout
       # additional properties
       SCREAMING_SNAKE_CASE       :      Any							=    max_depth
       SCREAMING_SNAKE_CASE       :      int							=    max_xpath_tag_unit_embeddings
       SCREAMING_SNAKE_CASE       :      List[str]							=    max_xpath_subs_unit_embeddings
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    tag_pad_id
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    subs_pad_id
       SCREAMING_SNAKE_CASE       :      List[Any]							=    xpath_unit_hidden_size














import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[Any]			=	IFImgaImgSuperResolutionPipeline
     UpperCamelCase_							:		Any			=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
     UpperCamelCase_							:		Optional[Any]			=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''original_image'''}					)
     UpperCamelCase_							:		str			=	PipelineTesterMixin.required_optional_params - {'''latents'''}


     def 				_A       (				self       :	List[Any]      ):
       return self._get_superresolution_dummy_components()


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	List[str]=0      ):
       if str(UpperCAmelCase_      ).startswith("mps"      ):
         SCREAMING_SNAKE_CASE       :      Any							=    torch.manual_seed(UpperCAmelCase_      )
       else:
         SCREAMING_SNAKE_CASE       :      List[str]							=    torch.Generator(device=UpperCAmelCase_      ).manual_seed(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Any							=    floats_tensor((1, 3, 32, 32)					, rng=random.Random(UpperCAmelCase_      )      ).to(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Any							=    floats_tensor((1, 3, 16, 16)					, rng=random.Random(UpperCAmelCase_      )      ).to(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    {
           "prompt": "A painting of a squirrel eating a burger",
           "image": image,
           "original_image": original_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      ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )


     def 				_A       (				self       :	Optional[Any]      ):
       self._test_save_load_optional_components()


     @unittest.skipIf(torch_device != "cuda"					, reason="float16 requires CUDA"      )
     def 				_A       (				self       :	List[Any]      ):
       # 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 				_A       (				self       :	Optional[int]      ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2      )


     def 				_A       (				self       :	Tuple      ):
       self._test_save_load_local()






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






