






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       UNetaDModel(**_lowercase       )
					_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(_lowercase    ,	_lowercase    ,	_lowercase       ):
										_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(_lowercase    ,	_lowercase    ,	_lowercase       ):
										_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(_lowercase    ,	'''attentions'''       )
										for j in range(len(block.resnets       )       ):
															set_resnet_weights(block.resnets[j]    ,	_lowercase    ,	_lowercase       )
															module_index += 1
															if has_attentions:
																				set_attention_weights(block.attentions[j]    ,	_lowercase    ,	_lowercase       )
																				module_index += 1

										if hasattr(_lowercase    ,	'''downsamplers'''       ) and block.downsamplers is not None:
															set_resnet_weights(block.resnet_down    ,	_lowercase    ,	_lowercase       )
															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]    ,	_lowercase    ,	_lowercase       )
					module_index += 1
					set_attention_weights(new_model_architecture.mid_block.attentions[0]    ,	_lowercase    ,	_lowercase       )
					module_index += 1
					set_resnet_weights(new_model_architecture.mid_block.resnets[1]    ,	_lowercase    ,	_lowercase       )
					module_index += 1

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

										if hasattr(_lowercase    ,	'''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    ,	_lowercase    ,	_lowercase       )
															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__":
				__A          = 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          = parser.parse_args()

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

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

				__A          = convert_ncsnpp_checkpoint(
				    checkpoint,
				    config,
				)

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

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

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

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




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__   = logging.get_logger(__name__)

lowerCamelCase__   = {
    '''nghuyong/ernie-1.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-base-en''': '''https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-large-en''': '''https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-medium-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-mini-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-micro-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-nano-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json''',
    '''nghuyong/ernie-gram-zh''': '''https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json''',
    '''nghuyong/ernie-health-zh''': '''https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json''',
}




class  _UpperCAmelCase       (			lowerCAmelCase   ):



 '''simple docstring'''




 __A           = '''ernie'''







 def __init__(						self							:					Dict				,      lowercase_							:					List[Any]=30522				,      lowercase_							:					Optional[int]=768				,      lowercase_							:					Tuple=12				,      lowercase_							:					Optional[int]=12				,      lowercase_							:					Any=3072				,      lowercase_							:					int="gelu"				,      lowercase_							:					List[Any]=0.1				,      lowercase_							:					Any=0.1				,      lowercase_							:					str=512				,      lowercase_							:					Any=2				,      lowercase_							:					Any=3				,      lowercase_							:					List[Any]=False				,      lowercase_							:					List[Any]=0.02				,      lowercase_							:					str=1e-1_2				,      lowercase_							:					int=0				,      lowercase_							:					str="absolute"				,      lowercase_							:					Dict=True				,      lowercase_							:					List[str]=None				,      **lowercase_							:					Dict				,      )		->     Union[str, Any]:


   """simple docstring"""







   super().__init__(pad_token_id=lowercase_				,      **lowercase_)

   _UpperCamelCase				=  vocab_size
   _UpperCamelCase				=  hidden_size
   _UpperCamelCase				=  num_hidden_layers
   _UpperCamelCase				=  num_attention_heads
   _UpperCamelCase				=  hidden_act
   _UpperCamelCase				=  intermediate_size
   _UpperCamelCase				=  hidden_dropout_prob
   _UpperCamelCase				=  attention_probs_dropout_prob
   _UpperCamelCase				=  max_position_embeddings
   _UpperCamelCase				=  type_vocab_size
   _UpperCamelCase				=  task_type_vocab_size
   _UpperCamelCase				=  use_task_id
   _UpperCamelCase				=  initializer_range
   _UpperCamelCase				=  layer_norm_eps
   _UpperCamelCase				=  position_embedding_type
   _UpperCamelCase				=  use_cache
   _UpperCamelCase				=  classifier_dropout




class  _UpperCAmelCase       (			lowerCAmelCase   ):



 '''simple docstring'''




 @property
 def 						__UpperCAmelCase							(						self							:					Optional[int])		->     Mapping[str, Mapping[int, str]]:


   """simple docstring"""







   if self.task == "multiple-choice":
     _UpperCamelCase				=  {0: "batch", 1: "choice", 2: "sequence"}
   else:
     _UpperCamelCase				=  {0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
           ("token_type_ids", dynamic_axis),
           ("task_type_ids", dynamic_axis),
       ])




