





import copy

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


_lowerCamelCase       =	logging.get_logger(__name__)

_lowerCamelCase       =	{
    'facebook/sam-vit-huge': 'https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json',
    'facebook/sam-vit-large': 'https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json',
    'facebook/sam-vit-base': 'https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json',
}




class      UpperCamelCase_				(   UpperCamelCase__			):


							def __init__(							self			:int							, __A			:Dict=256							, __A			:str=1024							, __A			:Optional[Any]=16							, __A			:Any=16							, __A			:Optional[int]=4							, __A			:Any="gelu"							, __A			:Union[str, Any]=1E-6							, **__A			:int							, )      ->      Tuple:



									"""simple docstring"""



									super().__init__(**__A						)
									SCREAMING_SNAKE_CASE__								=					hidden_size
									SCREAMING_SNAKE_CASE__								=					image_size
									SCREAMING_SNAKE_CASE__								=					patch_size
									SCREAMING_SNAKE_CASE__								=					image_size // patch_size
									SCREAMING_SNAKE_CASE__								=					mask_input_channels
									SCREAMING_SNAKE_CASE__								=					num_point_embeddings
									SCREAMING_SNAKE_CASE__								=					hidden_act
									SCREAMING_SNAKE_CASE__								=					layer_norm_eps




class      UpperCamelCase_				(   UpperCamelCase__			):


							def __init__(							self			:Union[str, Any]							, __A			:Optional[int]=256							, __A			:Optional[int]="relu"							, __A			:Any=2048							, __A			:List[Any]=2							, __A			:Dict=8							, __A			:Optional[Any]=2							, __A			:Tuple=3							, __A			:int=3							, __A			:str=256							, __A			:str=1E-6							, **__A			:Tuple							, )      ->      int:



									"""simple docstring"""



									super().__init__(**__A						)
									SCREAMING_SNAKE_CASE__								=					hidden_size
									SCREAMING_SNAKE_CASE__								=					hidden_act
									SCREAMING_SNAKE_CASE__								=					mlp_dim
									SCREAMING_SNAKE_CASE__								=					num_hidden_layers
									SCREAMING_SNAKE_CASE__								=					num_attention_heads
									SCREAMING_SNAKE_CASE__								=					attention_downsample_rate
									SCREAMING_SNAKE_CASE__								=					num_multimask_outputs
									SCREAMING_SNAKE_CASE__								=					iou_head_depth
									SCREAMING_SNAKE_CASE__								=					iou_head_hidden_dim
									SCREAMING_SNAKE_CASE__								=					layer_norm_eps




class      UpperCamelCase_				(   UpperCamelCase__			):


							def __init__(							self			:List[Any]							, __A			:List[Any]=768							, __A			:Dict=256							, __A			:Optional[int]=12							, __A			:Optional[int]=12							, __A			:str=3							, __A			:Union[str, Any]=1024							, __A			:List[str]=16							, __A			:Dict="gelu"							, __A			:Dict=1E-06							, __A			:List[str]=0.0							, __A			:Dict=1E-10							, __A			:List[Any]=True							, __A			:Tuple=4.0							, __A			:int=True							, __A			:List[str]=True							, __A			:Optional[int]=14							, __A			:int=[2, 5, 8, 11]							, __A			:Tuple=128							, __A			:Optional[int]=None							, **__A			:int							, )      ->      Any:



									"""simple docstring"""



									super().__init__(**__A						)

									SCREAMING_SNAKE_CASE__								=					hidden_size
									SCREAMING_SNAKE_CASE__								=					output_channels
									SCREAMING_SNAKE_CASE__								=					num_hidden_layers
									SCREAMING_SNAKE_CASE__								=					num_attention_heads
									SCREAMING_SNAKE_CASE__								=					num_channels
									SCREAMING_SNAKE_CASE__								=					image_size
									SCREAMING_SNAKE_CASE__								=					patch_size
									SCREAMING_SNAKE_CASE__								=					hidden_act
									SCREAMING_SNAKE_CASE__								=					layer_norm_eps
									SCREAMING_SNAKE_CASE__								=					attention_dropout
									SCREAMING_SNAKE_CASE__								=					initializer_range
									SCREAMING_SNAKE_CASE__								=					qkv_bias
									SCREAMING_SNAKE_CASE__								=					mlp_ratio
									SCREAMING_SNAKE_CASE__								=					use_abs_pos
									SCREAMING_SNAKE_CASE__								=					use_rel_pos
									SCREAMING_SNAKE_CASE__								=					window_size
									SCREAMING_SNAKE_CASE__								=					global_attn_indexes
									SCREAMING_SNAKE_CASE__								=					num_pos_feats
									SCREAMING_SNAKE_CASE__								=					int(hidden_size * mlp_ratio						) if mlp_dim is None else mlp_dim




class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    "sam"
							lowerCamelCase_							=    True


							def __init__(							self			:Union[str, Any]							, __A			:Union[str, Any]=None							, __A			:str=None							, __A			:Dict=None							, __A			:str=0.0_2							, **__A			:List[str]							, )      ->      List[Any]:



									"""simple docstring"""



									super().__init__(**__A						)
									SCREAMING_SNAKE_CASE__								=					vision_config if vision_config is not None else {}
									SCREAMING_SNAKE_CASE__								=					prompt_encoder_config if prompt_encoder_config is not None else {}
									SCREAMING_SNAKE_CASE__								=					mask_decoder_config if mask_decoder_config is not None else {}

									if isinstance(__A							, __A						):
											SCREAMING_SNAKE_CASE__								=					vision_config.to_dict()
									if isinstance(__A							, __A						):
											SCREAMING_SNAKE_CASE__								=					prompt_encoder_config.to_dict()
									if isinstance(__A							, __A						):
											SCREAMING_SNAKE_CASE__								=					mask_decoder_config.to_dict()

									SCREAMING_SNAKE_CASE__								=					SamVisionConfig(**__A						)
									SCREAMING_SNAKE_CASE__								=					SamPromptEncoderConfig(**__A						)
									SCREAMING_SNAKE_CASE__								=					SamMaskDecoderConfig(**__A						)
									SCREAMING_SNAKE_CASE__								=					initializer_range


							def    _snake_case     (							self			:str						)      ->      Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					copy.deepcopy(self.__dict__						)
									SCREAMING_SNAKE_CASE__								=					self.vision_config.to_dict()
									SCREAMING_SNAKE_CASE__								=					self.prompt_encoder_config.to_dict()
									SCREAMING_SNAKE_CASE__								=					self.mask_decoder_config.to_dict()
									SCREAMING_SNAKE_CASE__								=					self.__class__.model_type
									return output



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =UNetaDModel(**a__)
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.0.W'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.0.W'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.1.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.1.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.2.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.2.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.3.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.3.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-4]].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-3]].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-2]].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-1]].data

				_SCREAMING_SNAKE_CASE	     =4

				def set_attention_weights(a__		,a__		,a__):
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

				def set_resnet_weights(a__		,a__		,a__):
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =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:
												_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
												_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

				for i, block in enumerate(new_model_architecture.downsample_blocks):
								_SCREAMING_SNAKE_CASE	     =hasattr(a__		,'''attentions''')
								for j in range(len(block.resnets)):
												set_resnet_weights(block.resnets[j]		,a__		,a__)
												module_index += 1
												if has_attentions:
																set_attention_weights(block.attentions[j]		,a__		,a__)
																module_index += 1

								if hasattr(a__		,'''downsamplers''') and block.downsamplers is not None:
												set_resnet_weights(block.resnet_down		,a__		,a__)
												module_index += 1
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
												module_index += 1

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

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

								if hasattr(a__		,'''resnet_up''') and block.resnet_up is not None:
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
												module_index += 1
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
												module_index += 1
												set_resnet_weights(block.resnet_up		,a__		,a__)
												module_index += 1

				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
				module_index += 1
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data

				return new_model_architecture.state_dict()


if __name__ == "__main__":
							snake_case_  :	Tuple							  =							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.''',
							)

							snake_case_  :	List[str]							  =							parser.parse_args()

							snake_case_  :	List[Any]							  =							torch.load(args.checkpoint_path, map_location='''cpu''')

							with open(args.config_file) as f:
														snake_case_  :	List[str]							  =							json.loads(f.read())

							snake_case_  :	Optional[int]							  =							convert_ncsnpp_checkpoint(
							    checkpoint,
							    config,
							)

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

							snake_case_  :	Union[str, Any]							  =							UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														snake_case_  :	List[Any]							  =							ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

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