




import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     Any:







				'''simple docstring'''


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

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

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

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

				__lowerCAmelCase					=  4

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

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

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

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

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

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

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

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

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

				return new_model_architecture.state_dict()


if __name__ == "__main__":
	__A						:		Any          =						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.",
	)

	__A						:		str          =						parser.parse_args()

	__A						:		List[str]          =						torch.load(args.checkpoint_path, map_location="cpu")

	with open(args.config_file) as f:
		__A						:		Union[str, Any]          =						json.loads(f.read())

	__A						:		Union[str, Any]          =						convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

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

	__A						:		Tuple          =						UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		__A						:		Union[str, Any]          =						ScoreSdeVeScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

		__A						:		Optional[Any]          =						ScoreSdeVePipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)












import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


UpperCAmelCase__    :      int       					=logging.get_logger(__name__)

UpperCAmelCase__    :      Optional[Any]       					={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__    :      List[str]       					={
    '''vocab_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase__    :      Dict       					={
    '''microsoft/layoutlm-base-uncased''': 5_12,
    '''microsoft/layoutlm-large-uncased''': 5_12,
}

UpperCAmelCase__    :      List[Any]       					={
    '''microsoft/layoutlm-base-uncased''': {'''do_lower_case''': True},
    '''microsoft/layoutlm-large-uncased''': {'''do_lower_case''': True},
}

class    __A  (    a     ):
				__A  =					VOCAB_FILES_NAMES
				__A  =					PRETRAINED_VOCAB_FILES_MAP
				__A  =					PRETRAINED_INIT_CONFIGURATION
				__A  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A  =					LayoutLMTokenizer


				def __init__(		self ,			UpperCAmelCase_=None ,			UpperCAmelCase_=None ,			UpperCAmelCase_=True ,			UpperCAmelCase_="[UNK]" ,			UpperCAmelCase_="[SEP]" ,			UpperCAmelCase_="[PAD]" ,			UpperCAmelCase_="[CLS]" ,			UpperCAmelCase_="[MASK]" ,			UpperCAmelCase_=True ,			UpperCAmelCase_=None ,			**UpperCAmelCase_ ,			):
								super().__init__(
								    UpperCAmelCase_ ,			tokenizer_file=UpperCAmelCase_ ,			do_lower_case=UpperCAmelCase_ ,			unk_token=UpperCAmelCase_ ,			sep_token=UpperCAmelCase_ ,			pad_token=UpperCAmelCase_ ,			cls_token=UpperCAmelCase_ ,			mask_token=UpperCAmelCase_ ,			tokenize_chinese_chars=UpperCAmelCase_ ,			strip_accents=UpperCAmelCase_ ,			**UpperCAmelCase_ ,			)

								lowerCamelCase									=json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
								if (
								    normalizer_state.get("""lowercase""" ,			UpperCAmelCase_				) != do_lower_case
								    or normalizer_state.get("""strip_accents""" ,			UpperCAmelCase_				) != strip_accents
								    or normalizer_state.get("""handle_chinese_chars""" ,			UpperCAmelCase_				) != tokenize_chinese_chars
								):
												lowerCamelCase									=getattr(UpperCAmelCase_ ,			normalizer_state.pop("""type"""				)				)
												lowerCamelCase									=do_lower_case
												lowerCamelCase									=strip_accents
												lowerCamelCase									=tokenize_chinese_chars
												lowerCamelCase									=normalizer_class(**UpperCAmelCase_				)

								lowerCamelCase									=do_lower_case


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_=None				):
								lowerCamelCase									=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

								return output


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None				):
								lowerCamelCase									=[self.sep_token_id]
								lowerCamelCase									=[self.cls_token_id]
								if token_ids_a is None:
												return len(cls + token_ids_a + sep				) * [0]
								return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None				):
								lowerCamelCase									=self._tokenizer.model.save(UpperCAmelCase_ ,			name=UpperCAmelCase_				)
								return tuple(UpperCAmelCase_				)





