






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}


class  SCREAMING_SNAKE_CASE			(			_UpperCamelCase					):
					'''simple docstring'''


					UpperCamelCase_							:		str			=	"table-transformer"
					UpperCamelCase_							:		Optional[int]			=	["past_key_values"]
					UpperCamelCase_							:		Union[str, Any]			=	{
					    "hidden_size": "d_model",
					    "num_attention_heads": "encoder_attention_heads",
					}


					def __init__(				self       :	Optional[int]					, UpperCAmelCase_       :	Dict=True					, UpperCAmelCase_       :	int=None					, UpperCAmelCase_       :	Optional[Any]=3					, UpperCAmelCase_       :	Any=100					, UpperCAmelCase_       :	List[str]=6					, UpperCAmelCase_       :	Optional[int]=2048					, UpperCAmelCase_       :	List[Any]=8					, UpperCAmelCase_       :	str=6					, UpperCAmelCase_       :	Union[str, Any]=2048					, UpperCAmelCase_       :	Tuple=8					, UpperCAmelCase_       :	Dict=0.0					, UpperCAmelCase_       :	Any=0.0					, UpperCAmelCase_       :	int=True					, UpperCAmelCase_       :	int="relu"					, UpperCAmelCase_       :	Tuple=256					, UpperCAmelCase_       :	List[Any]=0.1					, UpperCAmelCase_       :	Optional[int]=0.0					, UpperCAmelCase_       :	Union[str, Any]=0.0					, UpperCAmelCase_       :	List[str]=0.02					, UpperCAmelCase_       :	Dict=1.0					, UpperCAmelCase_       :	Union[str, Any]=False					, UpperCAmelCase_       :	Optional[Any]="sine"					, UpperCAmelCase_       :	List[Any]="resnet50"					, UpperCAmelCase_       :	List[Any]=True					, UpperCAmelCase_       :	List[str]=False					, UpperCAmelCase_       :	Union[str, Any]=1					, UpperCAmelCase_       :	str=5					, UpperCAmelCase_       :	str=2					, UpperCAmelCase_       :	Optional[Any]=1					, UpperCAmelCase_       :	Tuple=1					, UpperCAmelCase_       :	Dict=5					, UpperCAmelCase_       :	Tuple=2					, UpperCAmelCase_       :	Any=0.1					, **UpperCAmelCase_       :	str					, ):
							if backbone_config is not None and use_timm_backbone:
									raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."      )

							if not use_timm_backbone:
									if backbone_config is None:
											logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."      )
											SCREAMING_SNAKE_CASE       :      Dict							=    CONFIG_MAPPING["resnet"](out_features=["stage4"]      )
									elif isinstance(__a					, __a      ):
											SCREAMING_SNAKE_CASE       :      List[Any]							=    backbone_config.get("model_type"      )
											SCREAMING_SNAKE_CASE       :      int							=    CONFIG_MAPPING[backbone_model_type]
											SCREAMING_SNAKE_CASE       :      str							=    config_class.from_dict(__a      )
									# set timm attributes to None
									SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    None, None, None

							SCREAMING_SNAKE_CASE       :      List[str]							=    use_timm_backbone
							SCREAMING_SNAKE_CASE       :      Optional[int]							=    backbone_config
							SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    num_channels
							SCREAMING_SNAKE_CASE       :      List[str]							=    num_queries
							SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    d_model
							SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    encoder_ffn_dim
							SCREAMING_SNAKE_CASE       :      Tuple							=    encoder_layers
							SCREAMING_SNAKE_CASE       :      str							=    encoder_attention_heads
							SCREAMING_SNAKE_CASE       :      Dict							=    decoder_ffn_dim
							SCREAMING_SNAKE_CASE       :      str							=    decoder_layers
							SCREAMING_SNAKE_CASE       :      Optional[Any]							=    decoder_attention_heads
							SCREAMING_SNAKE_CASE       :      Any							=    dropout
							SCREAMING_SNAKE_CASE       :      Dict							=    attention_dropout
							SCREAMING_SNAKE_CASE       :      Any							=    activation_dropout
							SCREAMING_SNAKE_CASE       :      Dict							=    activation_function
							SCREAMING_SNAKE_CASE       :      Optional[Any]							=    init_std
							SCREAMING_SNAKE_CASE       :      int							=    init_xavier_std
							SCREAMING_SNAKE_CASE       :      int							=    encoder_layerdrop
							SCREAMING_SNAKE_CASE       :      Optional[int]							=    decoder_layerdrop
							SCREAMING_SNAKE_CASE       :      Tuple							=    encoder_layers
							SCREAMING_SNAKE_CASE       :      str							=    auxiliary_loss
							SCREAMING_SNAKE_CASE       :      str							=    position_embedding_type
							SCREAMING_SNAKE_CASE       :      Optional[Any]							=    backbone
							SCREAMING_SNAKE_CASE       :      Dict							=    use_pretrained_backbone
							SCREAMING_SNAKE_CASE       :      List[Any]							=    dilation
							# Hungarian matcher
							SCREAMING_SNAKE_CASE       :      List[str]							=    class_cost
							SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    bbox_cost
							SCREAMING_SNAKE_CASE       :      Any							=    giou_cost
							# Loss coefficients
							SCREAMING_SNAKE_CASE       :      List[Any]							=    mask_loss_coefficient
							SCREAMING_SNAKE_CASE       :      Dict							=    dice_loss_coefficient
							SCREAMING_SNAKE_CASE       :      Dict							=    bbox_loss_coefficient
							SCREAMING_SNAKE_CASE       :      Any							=    giou_loss_coefficient
							SCREAMING_SNAKE_CASE       :      Optional[int]							=    eos_coefficient
							super().__init__(is_encoder_decoder=__a					, **__a      )


					@property
					def 				_A       (				self       :	str      ):
							return self.encoder_attention_heads






					@property
					def 				_A       (				self       :	Tuple      ):
							return self.d_model





class  SCREAMING_SNAKE_CASE			(			_UpperCamelCase					):
					'''simple docstring'''


					UpperCamelCase_							:		Dict			=	version.parse('''1.11'''					)


					@property
					def 				_A       (				self       :	List[Any]      ):
							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							        ("pixel_mask", {0: "batch"}),
							    ]      )


					@property
					def 				_A       (				self       :	str      ):
							return 1E-5






					@property
					def 				_A       (				self       :	str      ):
							return 12














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					, )






