



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel

def 	A_					(		__a   : str       ,						__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   : Union[str, Any]       ,						__a   : List[Any]       ,						__a   : Optional[Any]						):
								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   : Any       ,						__a   : Union[str, Any]       ,						__a   : Union[str, Any]						):
								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__":
	UpperCAmelCase							       =    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.""",
	)

	UpperCAmelCase							       =    parser.parse_args()

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

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

	UpperCAmelCase							       =    convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

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

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

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

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





from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers

class   A__							:




						def __init__(				self    :					List[Any] ,			_a    :					List[Any]     )							->   None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =vocab
										_SCREAMING_SNAKE_CASE	     =normalizers.BertNormalizer(
										    clean_text=_a ,			handle_chinese_chars=_a ,			strip_accents=_a ,			lowercase=_a ,			)
										try:
														import rjieba
										except ImportError:
														raise ImportError(
														    '''You need to install rjieba to use RoFormerTokenizer. '''
														    '''See https://pypi.org/project/rjieba/ for installation.'''     )
										_SCREAMING_SNAKE_CASE	     =rjieba




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					int ,			_a    :					NormalizedString     )							->   List[NormalizedString]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[]

										# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
										for token, start, end in self.jieba.tokenize(str(_a     ) ,			hmm=_a     ):
														if token in self.vocab:
																		splits.append(normalized_string[start:end]     )
														else:
																		_SCREAMING_SNAKE_CASE	     =self.normalizers.normalize_str(_a     ).split()
																		for token in token_list:
																						if token:
																										_SCREAMING_SNAKE_CASE	     =start + len(_a     )
																										splits.append(normalized_string[start:end]     )
																										_SCREAMING_SNAKE_CASE	     =end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

										return splits




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					PreTokenizedString     )							->   int:


										"""simple docstring"""





										pretok.split(self.jieba_split     )