





'''simple docstring'''



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


_SCREAMING_SNAKE_CASE    =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    =			{
    '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json''',
}






class 							__lowercase				(					lowerCAmelCase__  ):







	'''simple docstring'''

	a			: Optional[int]      =				"mpnet"


	def __init__(self   ,_lowerCamelCase=30527   ,_lowerCamelCase=768   ,_lowerCamelCase=12   ,_lowerCamelCase=12   ,_lowerCamelCase=3072   ,_lowerCamelCase="gelu"   ,_lowerCamelCase=0.1   ,_lowerCamelCase=0.1   ,_lowerCamelCase=512   ,_lowerCamelCase=0.0_2   ,_lowerCamelCase=1E-1_2   ,_lowerCamelCase=32   ,_lowerCamelCase=1   ,_lowerCamelCase=0   ,_lowerCamelCase=2   ,**_lowerCamelCase   ,)							->							Optional[Any]:







				'''simple docstring'''





				super().__init__(pad_token_id=_lowerCamelCase   ,bos_token_id=_lowerCamelCase   ,eos_token_id=_lowerCamelCase   ,**_lowerCamelCase       )

				__lowercase   =							vocab_size
				__lowercase   =							hidden_size
				__lowercase   =							num_hidden_layers
				__lowercase   =							num_attention_heads
				__lowercase   =							hidden_act
				__lowercase   =							intermediate_size
				__lowercase   =							hidden_dropout_prob
				__lowercase   =							attention_probs_dropout_prob
				__lowercase   =							max_position_embeddings
				__lowercase   =							initializer_range
				__lowercase   =							layer_norm_eps
				__lowercase   =							relative_attention_num_buckets












'''simple docstring'''



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def       _lowerCAmelCase  ( lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     List[Any] ):
			__lowercase   =							UNetaDModel(**lowerCamelCase_ )
			__lowercase   =							checkpoint['''all_modules.0.W'''].data
			__lowercase   =							checkpoint['''all_modules.0.W'''].data
			__lowercase   =							checkpoint['''all_modules.1.weight'''].data
			__lowercase   =							checkpoint['''all_modules.1.bias'''].data

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

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

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

			__lowercase   =							4

			def set_attention_weights(lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     int						,				lowerCamelCase_	:     str ):
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

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

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

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

			def set_resnet_weights(lowerCamelCase_	:     str						,				lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     Any ):
						__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

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

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

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

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

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

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

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

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

			return new_model_architecture.state_dict()


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE    =			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.''',
	)

	_SCREAMING_SNAKE_CASE    =			parser.parse_args()

	_SCREAMING_SNAKE_CASE    =			torch.load(args.checkpoint_path, map_location='''cpu''')

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

	_SCREAMING_SNAKE_CASE    =			convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

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

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

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

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





