


"""simple docstring"""



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel






def 					lowerCamelCase    (a_  :Dict , a_  :Tuple)   ->							Union[str, Any]:
       lowercase						:Optional[Any]											= UNetaDModel(**a_)
       lowercase						:Tuple											= checkpoint['all_modules.0.W'].data
       lowercase						:Union[str, Any]											= checkpoint['all_modules.0.W'].data
       lowercase						:Tuple											= checkpoint['all_modules.1.weight'].data
       lowercase						:Dict											= checkpoint['all_modules.1.bias'].data

       lowercase						:List[Any]											= checkpoint['all_modules.2.weight'].data
       lowercase						:List[str]											= checkpoint['all_modules.2.bias'].data

       lowercase						:Any											= checkpoint['all_modules.3.weight'].data
       lowercase						:List[str]											= checkpoint['all_modules.3.bias'].data

       lowercase						:List[str]											= checkpoint[list(checkpoint.keys())[-4]].data
       lowercase						:int											= checkpoint[list(checkpoint.keys())[-3]].data
       lowercase						:Any											= checkpoint[list(checkpoint.keys())[-2]].data
       lowercase						:List[str]											= checkpoint[list(checkpoint.keys())[-1]].data

       lowercase						:Tuple											= 4

       def set_attention_weights(a_  :Optional[Any] , a_  :str , a_  :List[str]):
              lowercase						:Union[str, Any]											= old_checkpoint[F"""all_modules.{index}.NIN_0.W"""].data.T
              lowercase						:Dict											= old_checkpoint[F"""all_modules.{index}.NIN_1.W"""].data.T
              lowercase						:Optional[Any]											= old_checkpoint[F"""all_modules.{index}.NIN_2.W"""].data.T

              lowercase						:List[str]											= old_checkpoint[F"""all_modules.{index}.NIN_0.b"""].data
              lowercase						:Optional[Any]											= old_checkpoint[F"""all_modules.{index}.NIN_1.b"""].data
              lowercase						:List[Any]											= old_checkpoint[F"""all_modules.{index}.NIN_2.b"""].data

              lowercase						:List[str]											= old_checkpoint[F"""all_modules.{index}.NIN_3.W"""].data.T
              lowercase						:Optional[int]											= old_checkpoint[F"""all_modules.{index}.NIN_3.b"""].data

              lowercase						:Dict											= old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
              lowercase						:str											= old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

       def set_resnet_weights(a_  :str , a_  :Any , a_  :int):
              lowercase						:Optional[Any]											= old_checkpoint[F"""all_modules.{index}.Conv_0.weight"""].data
              lowercase						:Tuple											= old_checkpoint[F"""all_modules.{index}.Conv_0.bias"""].data
              lowercase						:Any											= old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
              lowercase						:Tuple											= old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

              lowercase						:int											= old_checkpoint[F"""all_modules.{index}.Conv_1.weight"""].data
              lowercase						:int											= old_checkpoint[F"""all_modules.{index}.Conv_1.bias"""].data
              lowercase						:List[Any]											= old_checkpoint[F"""all_modules.{index}.GroupNorm_1.weight"""].data
              lowercase						:Any											= old_checkpoint[F"""all_modules.{index}.GroupNorm_1.bias"""].data

              lowercase						:List[Any]											= old_checkpoint[F"""all_modules.{index}.Dense_0.weight"""].data
              lowercase						:List[Any]											= 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						:Optional[int]											= old_checkpoint[F"""all_modules.{index}.Conv_2.weight"""].data
                     lowercase						:Tuple											= old_checkpoint[F"""all_modules.{index}.Conv_2.bias"""].data

       for i, block in enumerate(new_model_architecture.downsample_blocks):
              lowercase						:Optional[int]											= 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
                     lowercase						:Union[str, Any]											= checkpoint[F"""all_modules.{module_index}.Conv_0.weight"""].data
                     lowercase						:Any											= 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):
              lowercase						:Tuple											= 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:
                     lowercase						:Any											= checkpoint[F"""all_modules.{module_index}.weight"""].data
                     lowercase						:Optional[int]											= checkpoint[F"""all_modules.{module_index}.bias"""].data
                     module_index += 1
                     lowercase						:Optional[int]											= checkpoint[F"""all_modules.{module_index}.weight"""].data
                     lowercase						:Optional[int]											= checkpoint[F"""all_modules.{module_index}.bias"""].data
                     module_index += 1
                     set_resnet_weights(block.resnet_up , a_ , a_)
                     module_index += 1

       lowercase						:Tuple											= checkpoint[F"""all_modules.{module_index}.weight"""].data
       lowercase						:Union[str, Any]											= checkpoint[F"""all_modules.{module_index}.bias"""].data
       module_index += 1
       lowercase						:Tuple											= checkpoint[F"""all_modules.{module_index}.weight"""].data
       lowercase						:Optional[int]											= 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)







# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					@classmethod
					def 	lowercase_							(				cls					):







						'''simple docstring'''




						return cls





					@slow
					@require_torch_gpu
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							self.get_auto_remove_tmp_dir()
						lowerCamelCase_							:		Tuple							   =							f"\n            --model_name Helsinki-NLP/opus-mt-en-ro\n            --save_dir {output_dir}\n            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro\n            --num_beams 2\n            --task translation\n        ".split()

						# we want this test to run even if there is only one GPU, but if there are more we use them all
						lowerCamelCase_							:		List[Any]							   =							get_gpu_count()
						lowerCamelCase_							:		Union[str, Any]							   =							f"\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        ".split()
						lowerCamelCase_							:		int							   =							[sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase ,       env=self.get_env()					)

						lowerCamelCase_							:		Any							   =							os.path.join(__lowercase ,       'test_bleu.json'					)
						lowerCamelCase_							:		List[Any]							   =							load_json(__lowercase					)
						# print(metrics)
						self.assertGreaterEqual(metrics['bleu'] ,       25					)
