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


lowerCAmelCase			:							List[str]     =logging.getLogger(__name__)




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def __init__(					self     :				List[str]    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				str=None)				->str:


          """simple docstring"""


          super().__init__(
              _UpperCamelCase    ,  question_encoder_tokenizer=_UpperCamelCase    ,  generator_tokenizer=_UpperCamelCase    ,  index=_UpperCamelCase    ,  init_retrieval=_UpperCamelCase    ,  )
          _lowerCamelCase     :		Optional[Any] 						=						None



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				int)				->List[Any]:


          """simple docstring"""


          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
              _lowerCamelCase     :		str 						=						self._infer_socket_ifname()
              # avoid clash with the NCCL port
              _lowerCamelCase     :		Dict 						=						str(distributed_port + 1)
              _lowerCamelCase     :		str 						=						dist.new_group(ranks=_UpperCamelCase    ,  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 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[Any]:


          """simple docstring"""


          return dist.get_rank(group=self.process_group) == 0



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Optional[Any]=torch.floataa)				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						torch.empty(_UpperCamelCase    ,  dtype=_UpperCamelCase)
          dist.scatter(_UpperCamelCase    ,  src=0    ,  scatter_list=_UpperCamelCase    ,  group=self.process_group)
          return target_tensor



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						psutil.net_if_addrs()
          # a hacky way to deal with varying network interface names
          _lowerCamelCase     :		Union[str, Any] 						=						next((addr for addr in addrs if addr.startswith("""e"""))    ,  _UpperCamelCase)
          return ifname




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				int)				->Tuple[np.ndarray, List[dict]]:


          """simple docstring"""


          if not dist.is_initialized():
              _lowerCamelCase						,							_lowerCamelCase     :		Union[str, Any] 						=						self._main_retrieve(_UpperCamelCase    ,  _UpperCamelCase)
              return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_UpperCamelCase)

          # distributed training
          _lowerCamelCase     :		int 						=						dist.get_world_size(group=self.process_group)

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

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

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





import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase			:							Dict     =logging.get_logger(__name__)

lowerCAmelCase			:							Dict     ={"vocab_file": "vocab.json"}

lowerCAmelCase			:							List[str]     ={
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

lowerCAmelCase			:							int     ={"mgp-str": 27}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



      def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int="[GO]"    ,  _UpperCamelCase     :				Any="[GO]"    ,  _UpperCamelCase     :				Optional[Any]="[s]"    ,  _UpperCamelCase     :				List[str]="[GO]"    ,  **_UpperCamelCase     :				Dict)				->Union[str, Any]:


          """simple docstring"""


          super().__init__(
              unk_token=_UpperCamelCase    ,  bos_token=_UpperCamelCase    ,  eos_token=_UpperCamelCase    ,  pad_token=_UpperCamelCase    ,  **_UpperCamelCase    ,  )

          with open(_UpperCamelCase    ,  encoding="""utf-8""") as vocab_handle:
              _lowerCamelCase     :		Optional[Any] 						=						json.load(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						{v: k for k, v in self.vocab.items()}



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Any:


          """simple docstring"""


          return len(self.vocab)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[Any]:


          """simple docstring"""


          return dict(self.vocab    ,  **self.added_tokens_encoder)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						[]
          for s in text:
              char_tokens.extend(_UpperCamelCase)
          return char_tokens



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				int)				->Optional[int]:


          """simple docstring"""


          return self.vocab.get(_UpperCamelCase    ,  self.vocab.get(self.unk_token))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[Any])				->Dict:


          """simple docstring"""


          return self.decoder.get(_UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[str] = None)				->Tuple[str]:


          """simple docstring"""


          if not os.path.isdir(_UpperCamelCase):
              logger.error("""Vocabulary path ({}) should be a directory""".format(_UpperCamelCase))
              return
          _lowerCamelCase     :		Tuple 						=						os.path.join(
              _UpperCamelCase    ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

          with open(_UpperCamelCase    ,  """w"""    ,  encoding="""utf-8""") as f:
              f.write(json.dumps(self.vocab    ,  indent=2    ,  sort_keys=_UpperCamelCase    ,  ensure_ascii=_UpperCamelCase) + """\n""")

          return (vocab_file,)




