

'''simple docstring'''




import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


_lowerCAmelCase            =		HUGGINGFACE_HUB_CACHE


_lowerCAmelCase            =		"config.json"
_lowerCAmelCase            =		"diffusion_pytorch_model.bin"
_lowerCAmelCase            =		"diffusion_flax_model.msgpack"
_lowerCAmelCase            =		"model.onnx"
_lowerCAmelCase            =		"diffusion_pytorch_model.safetensors"
_lowerCAmelCase            =		"weights.pb"
_lowerCAmelCase            =		"https://huggingface.co"
_lowerCAmelCase            =		default_cache_path
_lowerCAmelCase            =		"diffusers_modules"
_lowerCAmelCase            =		os.getenv("HF_MODULES_CACHE", os.path.join(hf_cache_home, "modules"))
_lowerCAmelCase            =		["fp16", "non-ema"]
_lowerCAmelCase            =		".self_attn"












'''simple docstring'''






import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


A_			      =		logging.getLogger(__name__)






class        lowercase_      (						lowerCAmelCase_							):





     def __init__(     self     :					List[Any]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					int=None			):
           super().__init__(
               __lowerCamelCase     ,							question_encoder_tokenizer=__lowerCamelCase     ,							generator_tokenizer=__lowerCamelCase     ,							index=__lowerCamelCase     ,							init_retrieval=__lowerCamelCase     ,							)
           snake_case__       :   Optional[int]	      =    None





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							__lowerCamelCase     :					int			):
           logger.info('initializing retrieval'			)

           # initializing a separate process group for retrieval as the default
           # nccl backend doesn't support gather/scatter operations while gloo
           # is too slow to replace nccl for the core gpu communication
           if dist.is_initialized():
                 logger.info('dist initialized'			)
                 # needs to be set manually
                 snake_case__       :   Any	      =    self._infer_socket_ifname()
                 # avoid clash with the NCCL port
                 snake_case__       :   int	      =    str(distributed_port + 1			)
                 snake_case__       :   Any	      =    dist.new_group(ranks=__lowerCamelCase     ,							backend='gloo'			)

           # initialize retriever only on the main worker
           if not dist.is_initialized() or self._is_main():
                 logger.info('dist not initialized / main'			)
                 self.index.init_index()

           # all processes wait untill the retriever is initialized by the main process
           if dist.is_initialized():
                 torch.distributed.barrier(group=self.process_group			)





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           return dist.get_rank(group=self.process_group			) == 0





     def 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Union[str, Any]=torch.floataa			):
           snake_case__       :   Optional[int]	      =    torch.empty(__lowerCamelCase     ,							dtype=__lowerCamelCase			)
           dist.scatter(__lowerCamelCase     ,							src=0     ,							scatter_list=__lowerCamelCase     ,							group=self.process_group			)
           return target_tensor





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   Union[str, Any]	      =    psutil.net_if_addrs()
           # a hacky way to deal with varying network interface names
           snake_case__       :   Any	      =    next((addr for addr in addrs if addr.startswith('e'			))     ,							__lowerCamelCase			)
           return ifname





     def 			_lowerCAmelCase      (     self     :					Tuple     ,							__lowerCamelCase     :					np.ndarray     ,							__lowerCamelCase     :					int			):

           # single GPU training
           if not dist.is_initialized():
                 snake_case__      ,							snake_case__       :   Optional[int]	      =    self._main_retrieve(__lowerCamelCase     ,							__lowerCamelCase			)
                 return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCamelCase			)

           # distributed training
           snake_case__       :   Optional[Any]	      =    dist.get_world_size(group=self.process_group			)

           # gather logic
           snake_case__       :   Optional[int]	      =    None
           if self._is_main():
                 snake_case__       :   Optional[int]	      =    [torch.empty(question_hidden_states.shape     ,							dtype=torch.floataa			) for _ in range(__lowerCamelCase			)]
           dist.gather(torch.tensor(__lowerCamelCase			)     ,							dst=0     ,							gather_list=__lowerCamelCase     ,							group=self.process_group			)

           # scatter logic
           snake_case__       :   Union[str, Any]	      =    question_hidden_states.shape[0]
           snake_case__       :   Tuple	      =    []
           snake_case__       :   List[Any]	      =    []
           if self._is_main():
                 assert len(__lowerCamelCase			) == world_size
                 snake_case__      ,							snake_case__       :   Optional[int]	      =    self._main_retrieve(torch.cat(__lowerCamelCase			).numpy()     ,							__lowerCamelCase			)
                 snake_case__      ,							snake_case__       :   Optional[Any]	      =    torch.tensor(__lowerCamelCase			), torch.tensor(__lowerCamelCase			)
                 snake_case__       :   str	      =    self._chunk_tensor(__lowerCamelCase     ,							__lowerCamelCase			)
                 snake_case__       :   Dict	      =    self._chunk_tensor(__lowerCamelCase     ,							__lowerCamelCase			)
           snake_case__       :   Tuple	      =    self._scattered(__lowerCamelCase     ,							[n_queries, n_docs]     ,							target_type=torch.intaa			)
           snake_case__       :   Dict	      =    self._scattered(__lowerCamelCase     ,							[n_queries, n_docs, question_hidden_states.shape[1]]			)

           return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(__lowerCamelCase			)
