


"""simple docstring"""






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     List[str]:
   a   :	Optional[Any]						=    UNetaDModel(**UpperCAmelCase__				)
   a   :	Any						=    checkpoint['all_modules.0.W'].data
   a   :	Optional[int]						=    checkpoint['all_modules.0.W'].data
   a   :	Tuple						=    checkpoint['all_modules.1.weight'].data
   a   :	Optional[int]						=    checkpoint['all_modules.1.bias'].data

   a   :	str						=    checkpoint['all_modules.2.weight'].data
   a   :	Tuple						=    checkpoint['all_modules.2.bias'].data

   a   :	Dict						=    checkpoint['all_modules.3.weight'].data
   a   :	Optional[int]						=    checkpoint['all_modules.3.bias'].data

   a   :	List[Any]						=    checkpoint[list(checkpoint.keys()				)[-4]].data
   a   :	str						=    checkpoint[list(checkpoint.keys()				)[-3]].data
   a   :	Union[str, Any]						=    checkpoint[list(checkpoint.keys()				)[-2]].data
   a   :	List[str]						=    checkpoint[list(checkpoint.keys()				)[-1]].data

   a   :	str						=    4

   def set_attention_weights(UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__				):
      a   :	List[str]						=    old_checkpoint[F'all_modules.{index}.NIN_0.W'].data.T
      a   :	Optional[Any]						=    old_checkpoint[F'all_modules.{index}.NIN_1.W'].data.T
      a   :	Any						=    old_checkpoint[F'all_modules.{index}.NIN_2.W'].data.T

      a   :	int						=    old_checkpoint[F'all_modules.{index}.NIN_0.b'].data
      a   :	Tuple						=    old_checkpoint[F'all_modules.{index}.NIN_1.b'].data
      a   :	List[Any]						=    old_checkpoint[F'all_modules.{index}.NIN_2.b'].data

      a   :	Optional[int]						=    old_checkpoint[F'all_modules.{index}.NIN_3.W'].data.T
      a   :	Tuple						=    old_checkpoint[F'all_modules.{index}.NIN_3.b'].data

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

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

      a   :	Optional[int]						=    old_checkpoint[F'all_modules.{index}.Conv_1.weight'].data
      a   :	Dict						=    old_checkpoint[F'all_modules.{index}.Conv_1.bias'].data
      a   :	Optional[Any]						=    old_checkpoint[F'all_modules.{index}.GroupNorm_1.weight'].data
      a   :	Optional[Any]						=    old_checkpoint[F'all_modules.{index}.GroupNorm_1.bias'].data

      a   :	int						=    old_checkpoint[F'all_modules.{index}.Dense_0.weight'].data
      a   :	List[str]						=    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   :	int						=    old_checkpoint[F'all_modules.{index}.Conv_2.weight'].data
         a   :	Tuple						=    old_checkpoint[F'all_modules.{index}.Conv_2.bias'].data

   for i, block in enumerate(new_model_architecture.downsample_blocks				):
      a   :	List[str]						=    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
         a   :	Any						=    checkpoint[F'all_modules.{module_index}.Conv_0.weight'].data
         a   :	Tuple						=    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				):
      a   :	str						=    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:
         a   :	Dict						=    checkpoint[F'all_modules.{module_index}.weight'].data
         a   :	Optional[int]						=    checkpoint[F'all_modules.{module_index}.bias'].data
         module_index += 1
         a   :	Optional[int]						=    checkpoint[F'all_modules.{module_index}.weight'].data
         a   :	List[str]						=    checkpoint[F'all_modules.{module_index}.bias'].data
         module_index += 1
         set_resnet_weights(block.resnet_up       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)
         module_index += 1

   a   :	int						=    checkpoint[F'all_modules.{module_index}.weight'].data
   a   :	Dict						=    checkpoint[F'all_modules.{module_index}.bias'].data
   module_index += 1
   a   :	Tuple						=    checkpoint[F'all_modules.{module_index}.weight'].data
   a   :	Any						=    checkpoint[F'all_modules.{module_index}.bias'].data

   return new_model_architecture.state_dict()


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__				:  str						= 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.",
 )

 SCREAMING_SNAKE_CASE__				:  str						= parser.parse_args()

 SCREAMING_SNAKE_CASE__				:  Any						= torch.load(args.checkpoint_path, map_location="cpu")

 with open(args.config_file) as f:
  SCREAMING_SNAKE_CASE__				:  List[Any]						= json.loads(f.read())

 SCREAMING_SNAKE_CASE__				:  Dict						= convert_ncsnpp_checkpoint(
     checkpoint,
     config,
 )

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

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

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

  SCREAMING_SNAKE_CASE__				:  str						= 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


SCREAMING_SNAKE_CASE__				:  Optional[Any]						= logging.getLogger(__name__)


class 	A_       :





  """simple docstring"""

  def __init__(						self						)				->					Dict:
     a   :	List[str]						=    False
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					List[str]:
     if not self.initialized:
        a   :	Dict						=    RagRetriever(
            __UpperCAmelCase  ,					question_encoder_tokenizer=__UpperCAmelCase  ,					generator_tokenizer=__UpperCAmelCase  ,					index=__UpperCAmelCase  ,					init_retrieval=__UpperCAmelCase  ,					)
        a   :	Dict						=    True
  def 							lowercase_     (						self						)				->					List[Any]:
     self.retriever.index.init_index()
  def 							lowercase_     (						self						)				->					List[Any]:
     # delete the old self.retriever object before assigning the new index
     del self.retriever
     a   :	Dict						=    False






  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Optional[int]:
     a	, a   :	List[str]						=    self.retriever._main_retrieve(__UpperCAmelCase  ,					__UpperCAmelCase						)
     a   :	List[Any]						=    self.retriever.index.get_doc_dicts(__UpperCAmelCase						)
     return doc_ids, retrieved_doc_embeds, doc_dicts



class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None						)				->					Tuple:
     if index is not None and index.is_initialized() and len(__UpperCAmelCase						) > 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__(
         __UpperCAmelCase  ,					question_encoder_tokenizer=__UpperCAmelCase  ,					generator_tokenizer=__UpperCAmelCase  ,					index=__UpperCAmelCase  ,					init_retrieval=__UpperCAmelCase  ,					)

     a   :	Tuple						=    retrieval_workers
     a   :	List[str]						=    question_encoder_tokenizer
     a   :	Union[str, Any]						=    generator_tokenizer
     if len(self.retrieval_workers						) > 0:
        ray.get(
            [
                worker.create_rag_retriever.remote(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)
                for worker in self.retrieval_workers
            ]						)
  def 							lowercase_     (						self						)				->					Tuple:
     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 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Tuple:
     if len(self.retrieval_workers						) > 0:
        # Select a random retrieval actor.
        a   :	Dict						=    self.retrieval_workers[random.randint(0  ,					len(self.retrieval_workers						) - 1						)]
        a	, a	, a   :	Any						=    ray.get(
            random_worker.retrieve.remote(__UpperCAmelCase  ,					__UpperCAmelCase						)						)
     else:
        a	, a   :	str						=    self._main_retrieve(__UpperCAmelCase  ,					__UpperCAmelCase						)
        a   :	str						=    self.index.get_doc_dicts(__UpperCAmelCase						)
     return retrieved_doc_embeds, doc_ids, doc_dicts
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					**__UpperCAmelCase						)				->					int:
     return super(__UpperCAmelCase  ,					cls						).get_tokenizers(__UpperCAmelCase  ,					__UpperCAmelCase  ,					**__UpperCAmelCase						)
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					**__UpperCAmelCase						)				->					str:
     a   :	Union[str, Any]						=    kwargs.pop('config'  ,					__UpperCAmelCase						) or RagConfig.from_pretrained(__UpperCAmelCase  ,					**__UpperCAmelCase						)
     a   :	Optional[Any]						=    RagTokenizer.from_pretrained(__UpperCAmelCase  ,					config=__UpperCAmelCase						)
     a   :	List[str]						=    rag_tokenizer.question_encoder
     a   :	int						=    rag_tokenizer.generator

     if indexed_dataset is not None:
        a   :	Optional[int]						=    'custom'
        a   :	str						=    CustomHFIndex(config.retrieval_vector_size  ,					__UpperCAmelCase						)
     else:
        a   :	Dict						=    cls._build_index(__UpperCAmelCase						)

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






  def 							lowercase_     (						self						)				->					List[Any]:
     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   :	Optional[Any]						=    self._build_index(self.config						)

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





