


"""simple docstring"""






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel



def 			A__		(					_UpperCAmelCase    :      Union[str, Any]				,    _UpperCAmelCase    :      Dict	)							->	int:

	'''simple docstring'''


	snake_case__  :						Any		=      UNetaDModel(**_UpperCAmelCase	)
	snake_case__  :						List[str]		=      checkpoint["all_modules.0.W"].data
	snake_case__  :						Union[str, Any]		=      checkpoint["all_modules.0.W"].data
	snake_case__  :						int		=      checkpoint["all_modules.1.weight"].data
	snake_case__  :						int		=      checkpoint["all_modules.1.bias"].data

	snake_case__  :						str		=      checkpoint["all_modules.2.weight"].data
	snake_case__  :						int		=      checkpoint["all_modules.2.bias"].data

	snake_case__  :						Tuple		=      checkpoint["all_modules.3.weight"].data
	snake_case__  :						Any		=      checkpoint["all_modules.3.bias"].data

	snake_case__  :						Any		=      checkpoint[list(checkpoint.keys()	)[-4]].data
	snake_case__  :						Dict		=      checkpoint[list(checkpoint.keys()	)[-3]].data
	snake_case__  :						List[str]		=      checkpoint[list(checkpoint.keys()	)[-2]].data
	snake_case__  :						Union[str, Any]		=      checkpoint[list(checkpoint.keys()	)[-1]].data

	snake_case__  :						Tuple		=      4

	def set_attention_weights(_UpperCAmelCase    :      List[Any]				,    _UpperCAmelCase    :      int				,    _UpperCAmelCase    :      Dict	):
		snake_case__  :						int		=      old_checkpoint[F"""all_modules.{index}.NIN_0.W"""].data.T
		snake_case__  :						Any		=      old_checkpoint[F"""all_modules.{index}.NIN_1.W"""].data.T
		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.NIN_2.W"""].data.T

		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.NIN_0.b"""].data
		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.NIN_1.b"""].data
		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.NIN_2.b"""].data

		snake_case__  :						Dict		=      old_checkpoint[F"""all_modules.{index}.NIN_3.W"""].data.T
		snake_case__  :						Any		=      old_checkpoint[F"""all_modules.{index}.NIN_3.b"""].data

		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
		snake_case__  :						Tuple		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

	def set_resnet_weights(_UpperCAmelCase    :      Dict				,    _UpperCAmelCase    :      Optional[Any]				,    _UpperCAmelCase    :      int	):
		snake_case__  :						Optional[Any]		=      old_checkpoint[F"""all_modules.{index}.Conv_0.weight"""].data
		snake_case__  :						int		=      old_checkpoint[F"""all_modules.{index}.Conv_0.bias"""].data
		snake_case__  :						Optional[int]		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
		snake_case__  :						Optional[Any]		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

		snake_case__  :						Optional[Any]		=      old_checkpoint[F"""all_modules.{index}.Conv_1.weight"""].data
		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.Conv_1.bias"""].data
		snake_case__  :						int		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_1.weight"""].data
		snake_case__  :						Optional[int]		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_1.bias"""].data

		snake_case__  :						Any		=      old_checkpoint[F"""all_modules.{index}.Dense_0.weight"""].data
		snake_case__  :						Optional[Any]		=      old_checkpoint[F"""all_modules.{index}.Dense_0.bias"""].data

		if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
			snake_case__  :						List[str]		=      old_checkpoint[F"""all_modules.{index}.Conv_2.weight"""].data
			snake_case__  :						int		=      old_checkpoint[F"""all_modules.{index}.Conv_2.bias"""].data

	for i, block in enumerate(new_model_architecture.downsample_blocks	):
		snake_case__  :						Optional[int]		=      hasattr(_UpperCAmelCase				,    "attentions"	)
		for j in range(len(block.resnets	)	):
			set_resnet_weights(block.resnets[j]				,    _UpperCAmelCase				,    _UpperCAmelCase	)
			module_index += 1
			if has_attentions:
				set_attention_weights(block.attentions[j]				,    _UpperCAmelCase				,    _UpperCAmelCase	)
				module_index += 1

		if hasattr(_UpperCAmelCase				,    "downsamplers"	) and block.downsamplers is not None:
			set_resnet_weights(block.resnet_down				,    _UpperCAmelCase				,    _UpperCAmelCase	)
			module_index += 1
			snake_case__  :						Optional[Any]		=      checkpoint[F"""all_modules.{module_index}.Conv_0.weight"""].data
			snake_case__  :						Union[str, Any]		=      checkpoint[F"""all_modules.{module_index}.Conv_0.bias"""].data
			module_index += 1

	set_resnet_weights(new_model_architecture.mid_block.resnets[0]				,    _UpperCAmelCase				,    _UpperCAmelCase	)
	module_index += 1
	set_attention_weights(new_model_architecture.mid_block.attentions[0]				,    _UpperCAmelCase				,    _UpperCAmelCase	)
	module_index += 1
	set_resnet_weights(new_model_architecture.mid_block.resnets[1]				,    _UpperCAmelCase				,    _UpperCAmelCase	)
	module_index += 1

	for i, block in enumerate(new_model_architecture.up_blocks	):
		snake_case__  :						str		=      hasattr(_UpperCAmelCase				,    "attentions"	)
		for j in range(len(block.resnets	)	):
			set_resnet_weights(block.resnets[j]				,    _UpperCAmelCase				,    _UpperCAmelCase	)
			module_index += 1
		if has_attentions:
			set_attention_weights(
			    block.attentions[0]				,    _UpperCAmelCase				,    _UpperCAmelCase	)  # why can there only be a single attention layer for up?
			module_index += 1

		if hasattr(_UpperCAmelCase				,    "resnet_up"	) and block.resnet_up is not None:
			snake_case__  :						Optional[int]		=      checkpoint[F"""all_modules.{module_index}.weight"""].data
			snake_case__  :						List[Any]		=      checkpoint[F"""all_modules.{module_index}.bias"""].data
			module_index += 1
			snake_case__  :						str		=      checkpoint[F"""all_modules.{module_index}.weight"""].data
			snake_case__  :						Tuple		=      checkpoint[F"""all_modules.{module_index}.bias"""].data
			module_index += 1
			set_resnet_weights(block.resnet_up				,    _UpperCAmelCase				,    _UpperCAmelCase	)
			module_index += 1

	snake_case__  :						List[str]		=      checkpoint[F"""all_modules.{module_index}.weight"""].data
	snake_case__  :						Any		=      checkpoint[F"""all_modules.{module_index}.bias"""].data
	module_index += 1
	snake_case__  :						List[str]		=      checkpoint[F"""all_modules.{module_index}.weight"""].data
	snake_case__  :						Union[str, Any]		=      checkpoint[F"""all_modules.{module_index}.bias"""].data

	return new_model_architecture.state_dict()


if __name__ == "__main__":
					lowercase  		=			argparse.ArgumentParser()

					parser.add_argument(
					    """--checkpoint_path""",
					    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin""",
					    type=str,
					    required=False,
					    help="""Path to the checkpoint to convert.""",
					)

					parser.add_argument(
					    """--config_file""",
					    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/config.json""",
					    type=str,
					    required=False,
					    help="""The config json file corresponding to the architecture.""",
					)

					parser.add_argument(
					    """--dump_path""",
					    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt""",
					    type=str,
					    required=False,
					    help="""Path to the output model.""",
					)

					lowercase  		=			parser.parse_args()

					lowercase  		=			torch.load(args.checkpoint_path, map_location="""cpu""")

					with open(args.config_file) as f:
										lowercase  		=			json.loads(f.read())

					lowercase  		=			convert_ncsnpp_checkpoint(
					    checkpoint,
					    config,
					)

					if "sde" in config:
										del config["sde"]

					lowercase  		=			UNetaDModel(**config)
					model.load_state_dict(converted_checkpoint)

					try:
										lowercase  		=			ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

										lowercase  		=			ScoreSdeVePipeline(unet=model, scheduler=scheduler)
										pipe.save_pretrained(args.dump_path)
					except:  # noqa: E722
										model.save_pretrained(args.dump_path)







"""simple docstring"""






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


__UpperCAmelCase 					=    logging.get_logger(__name__)

__UpperCAmelCase 					=    {
    '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   (     A__				):
			UpperCAmelCase_    :Optional[int]												=		"layoutlmv2"






			def __init__(   self			,  __A=3_0522			,  __A=768			,  __A=12			,  __A=12			,  __A=3072			,  __A="gelu"			,  __A=0.1			,  __A=0.1			,  __A=512			,  __A=2			,  __A=0.0_2			,  __A=1E-12			,  __A=0			,  __A=1024			,  __A=128			,  __A=32			,  __A=True			,  __A=256			,  __A=64			,  __A=True			,  __A=[7, 7, 256]			,  __A=128			,  __A=128			,  __A=True			,  __A=True			,  __A=False			,  __A=None			,  **__A			,  )     ->	List[str]:
						super().__init__(
						    vocab_size=__A			,  hidden_size=__A			,  num_hidden_layers=__A			,  num_attention_heads=__A			,  intermediate_size=__A			,  hidden_act=__A			,  hidden_dropout_prob=__A			,  attention_probs_dropout_prob=__A			,  max_position_embeddings=__A			,  type_vocab_size=__A			,  initializer_range=__A			,  layer_norm_eps=__A			,  pad_token_id=__A			,  **__A			,  )
						lowerCAmelCase_       :Optional[Any]     	=    max_ad_position_embeddings
						lowerCAmelCase_       :Dict     	=    max_rel_pos
						lowerCAmelCase_       :str     	=    rel_pos_bins
						lowerCAmelCase_       :List[str]     	=    fast_qkv
						lowerCAmelCase_       :Union[str, Any]     	=    max_rel_ad_pos
						lowerCAmelCase_       :List[str]     	=    rel_ad_pos_bins
						lowerCAmelCase_       :Any     	=    convert_sync_batchnorm
						lowerCAmelCase_       :Dict     	=    image_feature_pool_shape
						lowerCAmelCase_       :str     	=    coordinate_size
						lowerCAmelCase_       :Tuple     	=    shape_size
						lowerCAmelCase_       :Tuple     	=    has_relative_attention_bias
						lowerCAmelCase_       :Union[str, Any]     	=    has_spatial_attention_bias
						lowerCAmelCase_       :int     	=    has_visual_segment_embedding
						lowerCAmelCase_       :Any     	=    (
						    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
						)






			@classmethod
			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						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 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						lowerCAmelCase_       :Any     	=    detectrona.config.get_cfg()
						for k, v in self.detectrona_config_args.items():
									lowerCAmelCase_       :int     	=    k.split("""."""			)
									lowerCAmelCase_       :List[Any]     	=    detectrona_config
									for attribute in attributes[:-1]:
												lowerCAmelCase_       :List[Any]     	=    getattr(__A			,  __A			)
									setattr(__A			,  attributes[-1]			,  __A			)

						return detectrona_config






