
from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


lowercase__				:				int						=      logging.get_logger(__name__)

lowercase__				:				List[Any]						=      {
    "layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json",
    "layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json",
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
				import detectrona






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	'layoutlmv2'



							def __init__(	self  ,						SCREAMING_SNAKE_CASE_=30522  ,						SCREAMING_SNAKE_CASE_=768  ,						SCREAMING_SNAKE_CASE_=12  ,						SCREAMING_SNAKE_CASE_=12  ,						SCREAMING_SNAKE_CASE_=3072  ,						SCREAMING_SNAKE_CASE_="gelu"  ,						SCREAMING_SNAKE_CASE_=0.1  ,						SCREAMING_SNAKE_CASE_=0.1  ,						SCREAMING_SNAKE_CASE_=512  ,						SCREAMING_SNAKE_CASE_=2  ,						SCREAMING_SNAKE_CASE_=0.0_2  ,						SCREAMING_SNAKE_CASE_=1E-12  ,						SCREAMING_SNAKE_CASE_=0  ,						SCREAMING_SNAKE_CASE_=1024  ,						SCREAMING_SNAKE_CASE_=128  ,						SCREAMING_SNAKE_CASE_=32  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=256  ,						SCREAMING_SNAKE_CASE_=64  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=[7, 7, 256]  ,						SCREAMING_SNAKE_CASE_=128  ,						SCREAMING_SNAKE_CASE_=128  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=False  ,						SCREAMING_SNAKE_CASE_=None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> int:





													'''simple docstring'''






													super().__init__(
													    vocab_size=SCREAMING_SNAKE_CASE_  ,						hidden_size=SCREAMING_SNAKE_CASE_  ,						num_hidden_layers=SCREAMING_SNAKE_CASE_  ,						num_attention_heads=SCREAMING_SNAKE_CASE_  ,						intermediate_size=SCREAMING_SNAKE_CASE_  ,						hidden_act=SCREAMING_SNAKE_CASE_  ,						hidden_dropout_prob=SCREAMING_SNAKE_CASE_  ,						attention_probs_dropout_prob=SCREAMING_SNAKE_CASE_  ,						max_position_embeddings=SCREAMING_SNAKE_CASE_  ,						type_vocab_size=SCREAMING_SNAKE_CASE_  ,						initializer_range=SCREAMING_SNAKE_CASE_  ,						layer_norm_eps=SCREAMING_SNAKE_CASE_  ,						pad_token_id=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_  ,						)
													__UpperCamelCase  	=      max_ad_position_embeddings
													__UpperCamelCase  	=      max_rel_pos
													__UpperCamelCase  	=      rel_pos_bins
													__UpperCamelCase  	=      fast_qkv
													__UpperCamelCase  	=      max_rel_ad_pos
													__UpperCamelCase  	=      rel_ad_pos_bins
													__UpperCamelCase  	=      convert_sync_batchnorm
													__UpperCamelCase  	=      image_feature_pool_shape
													__UpperCamelCase  	=      coordinate_size
													__UpperCamelCase  	=      shape_size
													__UpperCamelCase  	=      has_relative_attention_bias
													__UpperCamelCase  	=      has_spatial_attention_bias
													__UpperCamelCase  	=      has_visual_segment_embedding
													__UpperCamelCase  	=      (
													    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
													)



							@classmethod
							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													return {
													    "MODEL.MASK_ON": True,
													    "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
													    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
													    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
													    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
													    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
													    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
													    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
													    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
													    "MODEL.POST_NMS_TOPK_TEST": 1000,
													    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
													    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
													    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
													    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
													    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
													    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
													    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
													    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
													    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
													    "MODEL.RESNETS.DEPTH": 101,
													    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
													    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
													    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
													    "MODEL.RESNETS.NUM_GROUPS": 32,
													    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
													    "MODEL.RESNETS.STRIDE_IN_1X1": False,
													}



							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      detectrona.config.get_cfg()
													for k, v in self.detectrona_config_args.items():
																			__UpperCamelCase  	=      k.split('''.'''      )
																			__UpperCamelCase  	=      detectrona_config
																			for attribute in attributes[:-1]:
																									__UpperCamelCase  	=      getattr(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
																			setattr(SCREAMING_SNAKE_CASE_  ,						attributes[-1]  ,						SCREAMING_SNAKE_CASE_      )

													return detectrona_config








'''simple docstring'''

import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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			):
     UpperCAmelCase			       =					IFImgaImgPipeline
     UpperCAmelCase			       =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
     UpperCAmelCase			       =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     UpperCAmelCase			       =					PipelineTesterMixin.required_optional_params - {'''latents'''}






     def 			a_       (      self		)							->     Optional[Any]:
          return self._get_dummy_components()






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=0		)							->     Any:
          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            =		{
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }

          return inputs






     def 			a_       (      self		)							->     Any:
          self._test_save_load_optional_components()






     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()  ,				reason="XFormers attention is only available with CUDA and `xformers` installed"  ,				)
     def 			a_       (      self		)							->     Dict:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)






     @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		)






     @unittest.skipIf(torch_device != "cuda"  ,				reason="float16 requires CUDA"		)
     def 			a_       (      self		)							->     int:
          super().test_floataa_inference(expected_max_diff=1e-1		)






     def 			a_       (      self		)							->     Dict:
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)






     def 			a_       (      self		)							->     Optional[Any]:
          self._test_save_load_local()






     def 			a_       (      self		)							->     Union[str, Any]:
          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2  ,				)

