
import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def 		UpperCAmelCase    (			a_	,     a_		)	->			List[Any]:







				"""simple docstring"""



				__A         =				UNetaDModel(**a_		)
				__A         =				checkpoint["all_modules.0.W"].data
				__A         =				checkpoint["all_modules.0.W"].data
				__A         =				checkpoint["all_modules.1.weight"].data
				__A         =				checkpoint["all_modules.1.bias"].data

				__A         =				checkpoint["all_modules.2.weight"].data
				__A         =				checkpoint["all_modules.2.bias"].data

				__A         =				checkpoint["all_modules.3.weight"].data
				__A         =				checkpoint["all_modules.3.bias"].data

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

				__A         =				4

				def set_attention_weights(a_	,     a_	,     a_		):
								__A         =				old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
								__A         =				old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
								__A         =				old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

								__A         =				old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
								__A         =				old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
								__A         =				old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

								__A         =				old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
								__A         =				old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

								__A         =				old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
								__A         =				old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

				def set_resnet_weights(a_	,     a_	,     a_		):
								__A         =				old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
								__A         =				old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
								__A         =				old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
								__A         =				old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

								__A         =				old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
								__A         =				old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
								__A         =				old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
								__A         =				old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

								__A         =				old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
								__A         =				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:
												__A         =				old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
												__A         =				old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

				for i, block in enumerate(new_model_architecture.downsample_blocks		):
								__A         =				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
												__A         =				checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
												__A         =				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		):
								__A         =				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:
												__A         =				checkpoint[F'''all_modules.{module_index}.weight'''].data
												__A         =				checkpoint[F'''all_modules.{module_index}.bias'''].data
												module_index += 1
												__A         =				checkpoint[F'''all_modules.{module_index}.weight'''].data
												__A         =				checkpoint[F'''all_modules.{module_index}.bias'''].data
												module_index += 1
												set_resnet_weights(block.resnet_up	,     a_	,     a_		)
												module_index += 1

				__A         =				checkpoint[F'''all_modules.{module_index}.weight'''].data
				__A         =				checkpoint[F'''all_modules.{module_index}.bias'''].data
				module_index += 1
				__A         =				checkpoint[F'''all_modules.{module_index}.weight'''].data
				__A         =				checkpoint[F'''all_modules.{module_index}.bias'''].data

				return new_model_architecture.state_dict()


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:int											=       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							:Optional[int]											=       parser.parse_args()

					SCREAMING_SNAKE_CASE							:Union[str, Any]											=       torch.load(args.checkpoint_path, map_location='cpu')

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

					SCREAMING_SNAKE_CASE							:Optional[Any]											=       convert_ncsnpp_checkpoint(
					    checkpoint,
					    config,
					)

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

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

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

										SCREAMING_SNAKE_CASE							:List[str]											=       ScoreSdeVePipeline(unet=model, scheduler=scheduler)
										pipe.save_pretrained(args.dump_path)
					except:  # noqa: E722
										model.save_pretrained(args.dump_path)


import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 		UpperCAmelCase    (			a_		)	->			Any:







				"""simple docstring"""



				if "model" in orig_key:
								__A         =				orig_key.replace("model."	,     ""		)
				if "norm1" in orig_key:
								__A         =				orig_key.replace("norm1"	,     "attention.output.LayerNorm"		)
				if "norm2" in orig_key:
								__A         =				orig_key.replace("norm2"	,     "output.LayerNorm"		)
				if "norm" in orig_key:
								__A         =				orig_key.replace("norm"	,     "LayerNorm"		)
				if "transformer" in orig_key:
								__A         =				orig_key.split("."		)[0].split("_"		)[-1]
								__A         =				orig_key.replace(F'''transformer_{layer_num}'''	,     F'''encoder.layer.{layer_num}'''		)
				if "mha.attn" in orig_key:
								__A         =				orig_key.replace("mha.attn"	,     "attention.self"		)
				if "mha" in orig_key:
								__A         =				orig_key.replace("mha"	,     "attention"		)
				if "W_q" in orig_key:
								__A         =				orig_key.replace("W_q"	,     "self.query"		)
				if "W_k" in orig_key:
								__A         =				orig_key.replace("W_k"	,     "self.key"		)
				if "W_v" in orig_key:
								__A         =				orig_key.replace("W_v"	,     "self.value"		)
				if "ff.0" in orig_key:
								__A         =				orig_key.replace("ff.0"	,     "intermediate.dense"		)
				if "ff.2" in orig_key:
								__A         =				orig_key.replace("ff.2"	,     "output.dense"		)
				if "ff" in orig_key:
								__A         =				orig_key.replace("ff"	,     "output.dense"		)
				if "mlm_class" in orig_key:
								__A         =				orig_key.replace("mlm.mlm_class"	,     "cls.predictions.decoder"		)
				if "mlm" in orig_key:
								__A         =				orig_key.replace("mlm"	,     "cls.predictions.transform"		)
				if "backbone.backbone.encoders" in orig_key:
								__A         =				orig_key.replace("backbone.backbone.encoders"	,     "encoder.layer"		)
				if "cls" not in orig_key:
								__A         =				"mra." + orig_key

				return orig_key


def 		UpperCAmelCase    (			a_	,     a_		)	->			List[str]:







				"""simple docstring"""



				for key in orig_state_dict.copy().keys():
								__A         =				orig_state_dict.pop(a_		)

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												__A         =				val

				__A         =				orig_state_dict["cls.predictions.decoder.bias"]
				__A         =				torch.arange(a_		).expand((1, -1)		) + 2

				return orig_state_dict


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			Dict:







				"""simple docstring"""



				__A         =				torch.load(a_	,     map_location="cpu"		)["model_state_dict"]
				__A         =				MraConfig.from_json_file(a_		)
				__A         =				MraForMaskedLM(a_		)

				__A         =				convert_checkpoint_helper(config.max_position_embeddings	,     a_		)

				print(model.load_state_dict(a_		)		)
				model.eval()
				model.save_pretrained(a_		)

				print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''		)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Optional[Any]											=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for Mra model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					SCREAMING_SNAKE_CASE							:Any											=       parser.parse_args()
					convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
