

'''simple docstring'''






import logging
import random

import ray

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


lowerCAmelCase__      		=			logging.getLogger(__name__)






class     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Dict    ):
   __lowercase													=				False






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Dict      ,lowercase__     :  str      ,lowercase__     :  int    ):
   if not self.initialized:
    __lowercase													=				RagRetriever(
        lowercase__      ,question_encoder_tokenizer=lowercase__      ,generator_tokenizer=lowercase__      ,index=lowercase__      ,init_retrieval=lowercase__      ,)
    __lowercase													=				True






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   self.retriever.index.init_index()






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  int      ,lowercase__     :  Union[str, Any]    ):
   __lowercase						,			__lowercase													=				self.retriever._main_retrieve(lowercase__      ,lowercase__    )
   return doc_ids, retrieved_doc_embeds






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  def __init__(   self     :  Optional[int]      ,lowercase__     :  List[str]      ,lowercase__     :  Any      ,lowercase__     :  str      ,lowercase__     :  Optional[int]      ,lowercase__     :  Union[str, Any]=None    ):
   if index is not None and index.is_initialized() and len(lowercase__    ) > 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__(
       lowercase__      ,question_encoder_tokenizer=lowercase__      ,generator_tokenizer=lowercase__      ,index=lowercase__      ,init_retrieval=lowercase__      ,)
   __lowercase													=				retrieval_workers
   if len(self.retrieval_workers    ) > 0:
    ray.get(
        [
            worker.create_rag_retriever.remote(lowercase__      ,lowercase__      ,lowercase__      ,lowercase__    )
            for worker in self.retrieval_workers
        ]    )






  def       SCREAMING_SNAKE_CASE   (   self     :  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       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  List[str]      ,lowercase__     :  Optional[Any]    ):
   if len(self.retrieval_workers    ) > 0:
    # Select a random retrieval actor.
    __lowercase													=				self.retrieval_workers[random.randint(0      ,len(self.retrieval_workers    ) - 1    )]
    __lowercase						,			__lowercase													=				ray.get(random_worker.retrieve.remote(lowercase__      ,lowercase__    )    )
   else:
    __lowercase						,			__lowercase													=				self._main_retrieve(lowercase__      ,lowercase__    )
   return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowercase__    )






  @classmethod
  def       SCREAMING_SNAKE_CASE   (   cls     :  Optional[int]      ,lowercase__     :  Any      ,lowercase__     :  int=None      ,**lowercase__     :  List[Any]    ):
   return super(lowercase__      ,cls    ).get_tokenizers(lowercase__      ,lowercase__      ,**lowercase__    )






  @classmethod
  def       SCREAMING_SNAKE_CASE   (   cls     :  Optional[Any]      ,lowercase__     :  int      ,lowercase__     :  int      ,lowercase__     :  Any=None      ,**lowercase__     :  Optional[int]    ):
   __lowercase													=				kwargs.pop('''config'''      ,lowercase__    ) or RagConfig.from_pretrained(lowercase__      ,**lowercase__    )
   __lowercase													=				RagTokenizer.from_pretrained(lowercase__      ,config=lowercase__    )
   __lowercase													=				rag_tokenizer.question_encoder
   __lowercase													=				rag_tokenizer.generator
   if indexed_dataset is not None:
    __lowercase													=				'''custom'''
    __lowercase													=				CustomHFIndex(config.retrieval_vector_size      ,lowercase__    )
   else:
    __lowercase													=				cls._build_index(lowercase__    )
   return cls(
       lowercase__      ,question_encoder_tokenizer=lowercase__      ,generator_tokenizer=lowercase__      ,retrieval_workers=lowercase__      ,index=lowercase__      ,)




'''simple docstring'''






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCAmelCase__      		=			False
lowerCAmelCase__      		=			True
lowerCAmelCase__      		=			False


if __name__ == "__main__":
 lowerCAmelCase__      		=			argparse.ArgumentParser()

 parser.add_argument(
     '''--repo_path''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the architecture.''',
 )

 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

 lowerCAmelCase__      		=			parser.parse_args()

 lowerCAmelCase__      		=			{
     '''image_size''': '''sample_size''',
     '''num_res_blocks''': '''layers_per_block''',
     '''block_channels''': '''block_out_channels''',
     '''down_blocks''': '''down_block_types''',
     '''up_blocks''': '''up_block_types''',
     '''downscale_freq_shift''': '''freq_shift''',
     '''resnet_num_groups''': '''norm_num_groups''',
     '''resnet_act_fn''': '''act_fn''',
     '''resnet_eps''': '''norm_eps''',
     '''num_head_channels''': '''attention_head_dim''',
 }

 lowerCAmelCase__      		=			{
     '''time_steps''': '''time_proj''',
     '''mid''': '''mid_block''',
     '''downsample_blocks''': '''down_blocks''',
     '''upsample_blocks''': '''up_blocks''',
 }

 lowerCAmelCase__      		=			'''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

 with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
  lowerCAmelCase__      		=			reader.read()
  lowerCAmelCase__      		=			json.loads(text)

 if do_only_config:
  for key in config_parameters_to_change.keys():
   config.pop(key, None)

 if has_file(args.repo_path, '''config.json'''):
  lowerCAmelCase__      		=			UNetaDModel(**config)
 else:
  lowerCAmelCase__      		=			UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
  lowerCAmelCase__      		=			class_name(**config)

 if do_only_config:
  model.save_config(os.path.join(args.repo_path, subfolder))

 lowerCAmelCase__      		=			dict(model.config)

 if do_only_renaming:
  for key, value in config_parameters_to_change.items():
   if key in config:
    lowerCAmelCase__      		=			config[key]
    del config[key]

  lowerCAmelCase__      		=			[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
  lowerCAmelCase__      		=			[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

 if do_only_weights:
  lowerCAmelCase__      		=			torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

  lowerCAmelCase__      		=			{}
  for param_key, param_value in state_dict.items():
   if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
    continue
   lowerCAmelCase__      		=			False
   for key, new_key in key_parameters_to_change.items():
    if not has_changed and param_key.split('''.''')[0] == key:
     lowerCAmelCase__      		=			param_value
     lowerCAmelCase__      		=			True
   if not has_changed:
    lowerCAmelCase__      		=			param_value

  model.load_state_dict(new_state_dict)
  model.save_pretrained(os.path.join(args.repo_path, subfolder))

