


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


lowerCAmelCase__ :			Union[str, Any]         =logging.get_logger(__name__)

lowerCAmelCase__ :			int         ={
    '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   __lowercase						(_UpperCAmelCase     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	"""layoutlmv2"""






							def __init__(			self ,	lowerCAmelCase__=3_0_5_2_2 ,	lowerCAmelCase__=7_6_8 ,	lowerCAmelCase__=1_2 ,	lowerCAmelCase__=1_2 ,	lowerCAmelCase__=3_0_7_2 ,	lowerCAmelCase__="gelu" ,	lowerCAmelCase__=0.1 ,	lowerCAmelCase__=0.1 ,	lowerCAmelCase__=5_1_2 ,	lowerCAmelCase__=2 ,	lowerCAmelCase__=0.02 ,	lowerCAmelCase__=1E-12 ,	lowerCAmelCase__=0 ,	lowerCAmelCase__=1_0_2_4 ,	lowerCAmelCase__=1_2_8 ,	lowerCAmelCase__=3_2 ,	lowerCAmelCase__=True ,	lowerCAmelCase__=2_5_6 ,	lowerCAmelCase__=6_4 ,	lowerCAmelCase__=True ,	lowerCAmelCase__=[7, 7, 2_5_6] ,	lowerCAmelCase__=1_2_8 ,	lowerCAmelCase__=1_2_8 ,	lowerCAmelCase__=True ,	lowerCAmelCase__=True ,	lowerCAmelCase__=False ,	lowerCAmelCase__=None ,	**lowerCAmelCase__ ,	):





											"""simple docstring"""




											super().__init__(
											    vocab_size=lowerCAmelCase__ ,	hidden_size=lowerCAmelCase__ ,	num_hidden_layers=lowerCAmelCase__ ,	num_attention_heads=lowerCAmelCase__ ,	intermediate_size=lowerCAmelCase__ ,	hidden_act=lowerCAmelCase__ ,	hidden_dropout_prob=lowerCAmelCase__ ,	attention_probs_dropout_prob=lowerCAmelCase__ ,	max_position_embeddings=lowerCAmelCase__ ,	type_vocab_size=lowerCAmelCase__ ,	initializer_range=lowerCAmelCase__ ,	layer_norm_eps=lowerCAmelCase__ ,	pad_token_id=lowerCAmelCase__ ,	**lowerCAmelCase__ ,	)
											SCREAMING_SNAKE_CASE_		:    int 		=				max_ad_position_embeddings
											SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				max_rel_pos
											SCREAMING_SNAKE_CASE_		:    Any 		=				rel_pos_bins
											SCREAMING_SNAKE_CASE_		:    str 		=				fast_qkv
											SCREAMING_SNAKE_CASE_		:    Tuple 		=				max_rel_ad_pos
											SCREAMING_SNAKE_CASE_		:    str 		=				rel_ad_pos_bins
											SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				convert_sync_batchnorm
											SCREAMING_SNAKE_CASE_		:    int 		=				image_feature_pool_shape
											SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				coordinate_size
											SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				shape_size
											SCREAMING_SNAKE_CASE_		:    str 		=				has_relative_attention_bias
											SCREAMING_SNAKE_CASE_		:    List[str] 		=				has_spatial_attention_bias
											SCREAMING_SNAKE_CASE_		:    int 		=				has_visual_segment_embedding
											SCREAMING_SNAKE_CASE_		:    Dict 		=				(
											    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
											)






							@classmethod
							def 				UpperCamelCase__    (			self					):





											"""simple docstring"""




											return {
											    "MODEL.MASK_ON": True,
											    "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
											    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
											    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
											    "MODEL.ANCHOR_GENERATOR.SIZES": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
											    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
											    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_0_0_0,
											    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_0_0_0,
											    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_0_0_0,
											    "MODEL.POST_NMS_TOPK_TEST": 1_0_0_0,
											    "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": 1_4,
											    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
											    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
											    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
											    "MODEL.RESNETS.DEPTH": 1_0_1,
											    "MODEL.RESNETS.SIZES": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
											    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
											    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
											    "MODEL.RESNETS.NUM_GROUPS": 3_2,
											    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
											    "MODEL.RESNETS.STRIDE_IN_1X1": False,
											}






							def 				UpperCamelCase__    (			self					):





											"""simple docstring"""




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

											return detectrona_config









from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	42
							_UpperCAmelCase						     =	42






							def __init__(			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										super().__init__()

										SCREAMING_SNAKE_CASE_		:    str 		=				PNDMScheduler.from_config(scheduler.config					)

										self.register_modules(unet=lowerCAmelCase__ ,	scheduler=lowerCAmelCase__					)






							@torch.no_grad()
							def __call__(			self ,	lowerCAmelCase__ = 1 ,	lowerCAmelCase__ = 5_0 ,	lowerCAmelCase__ = None ,	lowerCAmelCase__ = "pil" ,	lowerCAmelCase__ = True ,	**lowerCAmelCase__ ,	):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Tuple 		=				randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,	generator=lowerCAmelCase__ ,	device=self.device ,	)

										self.scheduler.set_timesteps(lowerCAmelCase__					)
										for t in self.progress_bar(self.scheduler.timesteps					):
													SCREAMING_SNAKE_CASE_		:    Dict 		=				self.unet(lowerCAmelCase__ ,	lowerCAmelCase__					).sample

													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				self.scheduler.step(lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					).prev_sample

										SCREAMING_SNAKE_CASE_		:    Dict 		=				(image / 2 + 0.5).clamp(0 ,	1					)
										SCREAMING_SNAKE_CASE_		:    Any 		=				image.cpu().permute(0 ,	2 ,	3 ,	1					).numpy()
										if output_type == "pil":
													SCREAMING_SNAKE_CASE_		:    str 		=				self.numpy_to_pil(lowerCAmelCase__					)

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase__					)





