"""simple docstring"""
import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->List[str]:
  a__:	Any     				=      UNetaDModel(**_SCREAMING_SNAKE_CASE  )
  a__:	Union[str, Any]     				=      checkpoint['all_modules.0.W'].data
  a__:	Optional[Any]     				=      checkpoint['all_modules.0.W'].data
  a__:	int     				=      checkpoint['all_modules.1.weight'].data
  a__:	Dict     				=      checkpoint['all_modules.1.bias'].data

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

  a__:	Tuple     				=      checkpoint['all_modules.3.weight'].data
  a__:	Optional[int]     				=      checkpoint['all_modules.3.bias'].data

  a__:	int     				=      checkpoint[list(checkpoint.keys()  )[-4]].data
  a__:	Optional[Any]     				=      checkpoint[list(checkpoint.keys()  )[-3]].data
  a__:	Any     				=      checkpoint[list(checkpoint.keys()  )[-2]].data
  a__:	int     				=      checkpoint[list(checkpoint.keys()  )[-1]].data

  a__:	Tuple     				=      4

  def set_attention_weights(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ):
    a__:	Dict     				=      old_checkpoint[F'all_modules.{index}.NIN_0.W'].data.T
    a__:	Dict     				=      old_checkpoint[F'all_modules.{index}.NIN_1.W'].data.T
    a__:	str     				=      old_checkpoint[F'all_modules.{index}.NIN_2.W'].data.T

    a__:	List[str]     				=      old_checkpoint[F'all_modules.{index}.NIN_0.b'].data
    a__:	Union[str, Any]     				=      old_checkpoint[F'all_modules.{index}.NIN_1.b'].data
    a__:	str     				=      old_checkpoint[F'all_modules.{index}.NIN_2.b'].data

    a__:	Union[str, Any]     				=      old_checkpoint[F'all_modules.{index}.NIN_3.W'].data.T
    a__:	Optional[int]     				=      old_checkpoint[F'all_modules.{index}.NIN_3.b'].data

    a__:	Optional[Any]     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_0.weight'].data
    a__:	Dict     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_0.bias'].data

  def set_resnet_weights(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ):
    a__:	int     				=      old_checkpoint[F'all_modules.{index}.Conv_0.weight'].data
    a__:	Dict     				=      old_checkpoint[F'all_modules.{index}.Conv_0.bias'].data
    a__:	str     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_0.weight'].data
    a__:	Any     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_0.bias'].data

    a__:	Any     				=      old_checkpoint[F'all_modules.{index}.Conv_1.weight'].data
    a__:	Union[str, Any]     				=      old_checkpoint[F'all_modules.{index}.Conv_1.bias'].data
    a__:	int     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_1.weight'].data
    a__:	Optional[int]     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_1.bias'].data

    a__:	str     				=      old_checkpoint[F'all_modules.{index}.Dense_0.weight'].data
    a__:	Union[str, 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:
      a__:	Union[str, Any]     				=      old_checkpoint[F'all_modules.{index}.Conv_2.weight'].data
      a__:	Any     				=      old_checkpoint[F'all_modules.{index}.Conv_2.bias'].data

  for i, block in enumerate(new_model_architecture.downsample_blocks  ):
    a__:	Tuple     				=      hasattr(_SCREAMING_SNAKE_CASE						,	'attentions'  )
    for j in range(len(block.resnets  )  ):
      set_resnet_weights(block.resnets[j]						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
      module_index += 1
      if has_attentions:
        set_attention_weights(block.attentions[j]						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
        module_index += 1

    if hasattr(_SCREAMING_SNAKE_CASE						,	'downsamplers'  ) and block.downsamplers is not None:
      set_resnet_weights(block.resnet_down						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
      module_index += 1
      a__:	Any     				=      checkpoint[F'all_modules.{module_index}.Conv_0.weight'].data
      a__:	str     				=      checkpoint[F'all_modules.{module_index}.Conv_0.bias'].data
      module_index += 1

  set_resnet_weights(new_model_architecture.mid_block.resnets[0]						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  module_index += 1
  set_attention_weights(new_model_architecture.mid_block.attentions[0]						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  module_index += 1
  set_resnet_weights(new_model_architecture.mid_block.resnets[1]						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  module_index += 1

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

    if hasattr(_SCREAMING_SNAKE_CASE						,	'resnet_up'  ) and block.resnet_up is not None:
      a__:	List[Any]     				=      checkpoint[F'all_modules.{module_index}.weight'].data
      a__:	Dict     				=      checkpoint[F'all_modules.{module_index}.bias'].data
      module_index += 1
      a__:	Dict     				=      checkpoint[F'all_modules.{module_index}.weight'].data
      a__:	Optional[int]     				=      checkpoint[F'all_modules.{module_index}.bias'].data
      module_index += 1
      set_resnet_weights(block.resnet_up						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
      module_index += 1

  a__:	str     				=      checkpoint[F'all_modules.{module_index}.weight'].data
  a__:	str     				=      checkpoint[F'all_modules.{module_index}.bias'].data
  module_index += 1
  a__:	Union[str, Any]     				=      checkpoint[F'all_modules.{module_index}.weight'].data
  a__:	Tuple     				=      checkpoint[F'all_modules.{module_index}.bias'].data

  return new_model_architecture.state_dict()


if __name__ == "__main__":
 lowercase__   			=      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.',
 )

 lowercase__   			=      parser.parse_args()

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

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

 lowercase__   			=      convert_ncsnpp_checkpoint(
     checkpoint,
     config,
 )

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

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

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

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



'''simple docstring'''




import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__snake_case  : Optional[int]		       =    logging.getLogger(__name__)




class       __UpperCAmelCase							:





       '''simple docstring'''



       def __init__( self					)						->	List[str]:
           A_      =  False

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	str:
           if not self.initialized:
               A_      =  RagRetriever(
                   _SCREAMING_SNAKE_CASE       ,	question_encoder_tokenizer=_SCREAMING_SNAKE_CASE       ,	generator_tokenizer=_SCREAMING_SNAKE_CASE       ,	index=_SCREAMING_SNAKE_CASE       ,	init_retrieval=_SCREAMING_SNAKE_CASE       ,	)
               A_      =  True

       def    __A			( self					)						->	List[Any]:
           self.retriever.index.init_index()

       def    __A			( self					)						->	Dict:
           # delete the old self.retriever object before assigning the new index
           del self.retriever
           A_      =  False

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	Optional[int]:
           A_  ,A_      =  self.retriever._main_retrieve(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
           A_      =  self.retriever.index.get_doc_dicts(_SCREAMING_SNAKE_CASE					)
           return doc_ids, retrieved_doc_embeds, doc_dicts




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None					)						->	List[Any]:
           if index is not None and index.is_initialized() and len(_SCREAMING_SNAKE_CASE					) > 0:
               raise ValueError(
                   '''When using Ray for distributed fine-tuning, '''
                   '''you\'ll need to provide the paths instead, '''
                   '''as the dataset and the index are loaded '''
                   '''separately. More info in examples/rag/use_own_knowledge_dataset.py '''					)

           super().__init__(
               _SCREAMING_SNAKE_CASE       ,	question_encoder_tokenizer=_SCREAMING_SNAKE_CASE       ,	generator_tokenizer=_SCREAMING_SNAKE_CASE       ,	index=_SCREAMING_SNAKE_CASE       ,	init_retrieval=_SCREAMING_SNAKE_CASE       ,	)

           A_      =  retrieval_workers
           A_      =  question_encoder_tokenizer
           A_      =  generator_tokenizer
           if len(self.retrieval_workers					) > 0:
               ray.get(
                   [
                       worker.create_rag_retriever.remote(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
                       for worker in self.retrieval_workers
                   ]					)

       def    __A			( self					)						->	List[Any]:
           logger.info('''initializing retrieval'''					)

           if len(self.retrieval_workers					) > 0:
               ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]					)
           else:
               # Non-distributed training. Load index into this same process.
               self.index.init_index()

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	Optional[int]:
           if len(self.retrieval_workers					) > 0:
               # Select a random retrieval actor.
               A_      =  self.retrieval_workers[random.randint(0       ,	len(self.retrieval_workers					) - 1					)]
               A_  ,A_  ,A_      =  ray.get(
                   random_worker.retrieve.remote(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)					)
           else:
               A_  ,A_      =  self._main_retrieve(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
               A_      =  self.index.get_doc_dicts(_SCREAMING_SNAKE_CASE					)
           return retrieved_doc_embeds, doc_ids, doc_dicts

       @classmethod
       def    __A			( cls       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None       ,	**_SCREAMING_SNAKE_CASE					)						->	List[Any]:
           return super(_SCREAMING_SNAKE_CASE       ,	cls					).get_tokenizers(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)

       @classmethod
       def    __A			( cls       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None       ,	**_SCREAMING_SNAKE_CASE					)						->	int:
           A_      =  kwargs.pop('''config'''       ,	_SCREAMING_SNAKE_CASE					) or RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
           A_      =  RagTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE       ,	config=_SCREAMING_SNAKE_CASE					)
           A_      =  rag_tokenizer.question_encoder
           A_      =  rag_tokenizer.generator

           if indexed_dataset is not None:
               A_      =  '''custom'''
               A_      =  CustomHFIndex(config.retrieval_vector_size       ,	_SCREAMING_SNAKE_CASE					)
           else:
               A_      =  cls._build_index(_SCREAMING_SNAKE_CASE					)

           return cls(
               _SCREAMING_SNAKE_CASE       ,	question_encoder_tokenizer=_SCREAMING_SNAKE_CASE       ,	generator_tokenizer=_SCREAMING_SNAKE_CASE       ,	retrieval_workers=_SCREAMING_SNAKE_CASE       ,	index=_SCREAMING_SNAKE_CASE       ,	)

       def    __A			( self					)						->	Optional[int]:
           logger.info('''re-loading the new dataset with embeddings'''					)
           # access from the training loop

           ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]					)

           # build the index object again
           A_      =  self._build_index(self.config					)

           ray.get(
               [
                   worker.create_rag_retriever.remote(
                       self.config       ,	self.question_encoder_tokenizer       ,	self.generator_tokenizer       ,	_SCREAMING_SNAKE_CASE					)
                   for worker in self.retrieval_workers
               ]					)






