




import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
  from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
  _lowerCAmelCase    : str		= None

if is_ray_available():
  import ray  # noqa: E402 # isort:skip
  from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
  _lowerCAmelCase    : Dict		= None
  _lowerCAmelCase    : Optional[Any]		= None
  _lowerCAmelCase    : Union[str, Any]		= None



def     lowerCAmelCase		(		_lowerCAmelCase				: str						):




 """simple docstring"""







 if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
  UpperCAmelCase__							=    unittest.skip("test requires Datasets, Faiss, psutil"						)(_lowerCAmelCase						)
 return test_case


@require_distributed_retrieval
class       _UpperCamelCase   (					lowerCAmelCase		):
      def 						UpperCAmelCase_    (      self :Any   )  ->      Optional[Any]:
       UpperCAmelCase__							=    tempfile.mkdtemp()
       UpperCAmelCase__							=    8

       # DPR tok
       UpperCAmelCase__							=    [
           "[UNK]",
           "[CLS]",
           "[SEP]",
           "[PAD]",
           "[MASK]",
           "want",
           "##want",
           "##ed",
           "wa",
           "un",
           "runn",
           "##ing",
           ",",
           "low",
           "lowest",
       ]
       UpperCAmelCase__							=    os.path.join(self.tmpdirname			,   "dpr_tokenizer"   )
       os.makedirs(lowerCamelCase			,   exist_ok=lowerCamelCase   )
       UpperCAmelCase__							=    os.path.join(lowerCamelCase			,   DPR_VOCAB_FILES_NAMES["vocab_file"]   )
       with open(self.vocab_file			,   "w"			,   encoding="utf-8"   ) as vocab_writer:
        vocab_writer.write("".join([x + "\n" for x in vocab_tokens]   )   )

       # BART tok
       UpperCAmelCase__							=    [
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
       ]
       UpperCAmelCase__							=    dict(zip(lowerCamelCase			,   range(len(lowerCamelCase   )   )   )   )
       UpperCAmelCase__							=    ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       UpperCAmelCase__							=    {"unk_token": "<unk>"}

       UpperCAmelCase__							=    os.path.join(self.tmpdirname			,   "bart_tokenizer"   )
       os.makedirs(lowerCamelCase			,   exist_ok=lowerCamelCase   )
       UpperCAmelCase__							=    os.path.join(lowerCamelCase			,   BART_VOCAB_FILES_NAMES["vocab_file"]   )
       UpperCAmelCase__							=    os.path.join(lowerCamelCase			,   BART_VOCAB_FILES_NAMES["merges_file"]   )
       with open(self.vocab_file			,   "w"			,   encoding="utf-8"   ) as fp:
        fp.write(json.dumps(lowerCamelCase   ) + "\n"   )
       with open(self.merges_file			,   "w"			,   encoding="utf-8"   ) as fp:
        fp.write("\n".join(lowerCamelCase   )   )
      def 						UpperCAmelCase_    (      self :Union[str, Any]   )  ->      DPRQuestionEncoderTokenizer:
       return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,   "dpr_tokenizer"   )   )
      def 						UpperCAmelCase_    (      self :Optional[int]   )  ->      BartTokenizer:
       return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,   "bart_tokenizer"   )   )
      def 						UpperCAmelCase_    (      self :List[Any]   )  ->      Optional[int]:
       shutil.rmtree(self.tmpdirname   )
      def 						UpperCAmelCase_    (      self :Union[str, Any]   )  ->      Union[str, Any]:
       UpperCAmelCase__							=    Dataset.from_dict(
           {
               "id": ["0", "1"],
               "text": ["foo", "bar"],
               "title": ["Foo", "Bar"],
               "embeddings": [np.ones(self.retrieval_vector_size   ), 2 * np.ones(self.retrieval_vector_size   )],
           }   )
       dataset.add_faiss_index("embeddings"			,   string_factory="Flat"			,   metric_type=faiss.METRIC_INNER_PRODUCT   )
       return dataset
      def 						UpperCAmelCase_    (      self :Optional[int]			,   lowerCamelCase :bool			,   lowerCamelCase :List[str]=1_2345   )  ->      RagPyTorchDistributedRetriever:
       UpperCAmelCase__							=    self.get_dummy_dataset()
       UpperCAmelCase__							=    RagConfig(
           retrieval_vector_size=self.retrieval_vector_size			,   question_encoder=DPRConfig().to_dict()			,   generator=BartConfig().to_dict()			,   )
       with patch("transformers.models.rag.retrieval_rag.load_dataset"   ) as mock_load_dataset:
        UpperCAmelCase__							=    dataset
        UpperCAmelCase__							=    RagPyTorchDistributedRetriever(
            lowerCamelCase			,   question_encoder_tokenizer=self.get_dpr_tokenizer()			,   generator_tokenizer=self.get_bart_tokenizer()			,   )
        if init_retrieval:
         retriever.init_retrieval(lowerCamelCase   )
       return retriever
      def 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :bool   )  ->      RagRayDistributedRetriever:
       # Have to run in local mode because sys.path modifications at top of
       # file are not propogated to remote workers.
       # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
       ray.init(local_mode=lowerCamelCase   )
       UpperCAmelCase__							=    RagConfig(
           retrieval_vector_size=self.retrieval_vector_size			,   question_encoder=DPRConfig().to_dict()			,   generator=BartConfig().to_dict()			,   )
       UpperCAmelCase__							=    ray.remote(lowerCamelCase   )
       UpperCAmelCase__							=    [remote_cls.remote() for _ in range(1   )]
       with patch("transformers.models.rag.retrieval_rag.load_dataset"   ) as mock_load_dataset:
        UpperCAmelCase__							=    self.get_dummy_dataset()
        UpperCAmelCase__							=    RagRayDistributedRetriever(
            lowerCamelCase			,   question_encoder_tokenizer=self.get_dpr_tokenizer()			,   generator_tokenizer=self.get_bart_tokenizer()			,   retrieval_workers=lowerCamelCase			,   )
        if init_retrieval:
         retriever.init_retrieval()
       return retriever
      def 						UpperCAmelCase_    (      self :Tuple			,   lowerCamelCase :bool			,   lowerCamelCase :bool			,   lowerCamelCase :int=1_2345   )  ->      Optional[Any]:
       UpperCAmelCase__							=    self.get_dummy_dataset()
       UpperCAmelCase__							=    RagConfig(
           retrieval_vector_size=self.retrieval_vector_size			,   question_encoder=DPRConfig().to_dict()			,   generator=BartConfig().to_dict()			,   index_name="custom"			,   )
       if from_disk:
        UpperCAmelCase__							=    os.path.join(self.tmpdirname			,   "dataset"   )
        UpperCAmelCase__							=    os.path.join(self.tmpdirname			,   "index.faiss"   )
        dataset.get_index("embeddings"   ).save(os.path.join(self.tmpdirname			,   "index.faiss"   )   )
        dataset.drop_index("embeddings"   )
        dataset.save_to_disk(os.path.join(self.tmpdirname			,   "dataset"   )   )
        del dataset
        UpperCAmelCase__							=    RagPyTorchDistributedRetriever(
            lowerCamelCase			,   question_encoder_tokenizer=self.get_dpr_tokenizer()			,   generator_tokenizer=self.get_bart_tokenizer()			,   )
       else:
        UpperCAmelCase__							=    RagPyTorchDistributedRetriever(
            lowerCamelCase			,   question_encoder_tokenizer=self.get_dpr_tokenizer()			,   generator_tokenizer=self.get_bart_tokenizer()			,   index=CustomHFIndex(config.retrieval_vector_size			,   lowerCamelCase   )			,   )
       if init_retrieval:
        retriever.init_retrieval(lowerCamelCase   )
       return retriever
      def 						UpperCAmelCase_    (      self :Any			,   lowerCamelCase :bool			,   lowerCamelCase :bool   )  ->      Union[str, Any]:
       # Have to run in local mode because sys.path modifications at top of
       # file are not propogated to remote workers.
       # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
       ray.init(local_mode=lowerCamelCase   )
       UpperCAmelCase__							=    self.get_dummy_dataset()
       UpperCAmelCase__							=    RagConfig(
           retrieval_vector_size=self.retrieval_vector_size			,   question_encoder=DPRConfig().to_dict()			,   generator=BartConfig().to_dict()			,   index_name="custom"			,   )
       UpperCAmelCase__							=    ray.remote(lowerCamelCase   )
       UpperCAmelCase__							=    [remote_cls.remote() for _ in range(1   )]
       if from_disk:
        UpperCAmelCase__							=    os.path.join(self.tmpdirname			,   "dataset"   )
        UpperCAmelCase__							=    os.path.join(self.tmpdirname			,   "index.faiss"   )
        dataset.get_index("embeddings"   ).save(os.path.join(self.tmpdirname			,   "index.faiss"   )   )
        dataset.drop_index("embeddings"   )
        dataset.save_to_disk(os.path.join(self.tmpdirname			,   "dataset"   )   )
        del dataset
        UpperCAmelCase__							=    RagRayDistributedRetriever(
            lowerCamelCase			,   question_encoder_tokenizer=self.get_dpr_tokenizer()			,   generator_tokenizer=self.get_bart_tokenizer()			,   retrieval_workers=lowerCamelCase			,   index=CustomHFIndex.load_from_disk(
                vector_size=config.retrieval_vector_size			,   dataset_path=config.passages_path			,   index_path=config.index_path			,   )			,   )
       else:
        UpperCAmelCase__							=    RagRayDistributedRetriever(
            lowerCamelCase			,   question_encoder_tokenizer=self.get_dpr_tokenizer()			,   generator_tokenizer=self.get_bart_tokenizer()			,   retrieval_workers=lowerCamelCase			,   index=CustomHFIndex(config.retrieval_vector_size			,   lowerCamelCase   )			,   )
       if init_retrieval:
        retriever.init_retrieval()
       return retriever
      def 						UpperCAmelCase_    (      self :Any			,   lowerCamelCase :RagRetriever			,   lowerCamelCase :np.array			,   lowerCamelCase :int   )  ->      None:
       UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__							=    retriever.retrieve(lowerCamelCase			,   n_docs=lowerCamelCase   )
       self.assertEqual(retrieved_doc_embeds.shape			,   (2, n_docs, self.retrieval_vector_size)   )
       self.assertEqual(len(lowerCamelCase   )			,   2   )
       self.assertEqual(sorted(doc_dicts[0]   )			,   ["embeddings", "id", "text", "title"]   )
       self.assertEqual(len(doc_dicts[0]["id"]   )			,   lowerCamelCase   )
       self.assertEqual(doc_dicts[0]["id"][0]			,   "1"   )  # max inner product is reached with second doc
       self.assertEqual(doc_dicts[1]["id"][0]			,   "0"   )  # max inner product is reached with first doc
       self.assertListEqual(doc_ids.tolist()			,   [[1], [0]]   )
      def 						UpperCAmelCase_    (      self :List[Any]   )  ->      Dict:
       UpperCAmelCase__							=    1
       UpperCAmelCase__							=    np.array(
           [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,   dtype=np.floataa   )

       self.distributed_retriever_check(
           self.get_dummy_pytorch_distributed_retriever(init_retrieval=lowerCamelCase   )			,   lowerCamelCase			,   lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :Optional[int]   )  ->      Optional[int]:
       UpperCAmelCase__							=    1
       UpperCAmelCase__							=    np.array(
           [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,   dtype=np.floataa   )

       self.distributed_retriever_check(
           self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCamelCase			,   from_disk=lowerCamelCase   )			,   lowerCamelCase			,   lowerCamelCase			,   )
      def 						UpperCAmelCase_    (      self :List[str]   )  ->      List[Any]:
       UpperCAmelCase__							=    1
       UpperCAmelCase__							=    np.array(
           [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,   dtype=np.floataa   )

       self.distributed_retriever_check(
           self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCamelCase			,   from_disk=lowerCamelCase   )			,   lowerCamelCase			,   lowerCamelCase			,   )
      @require_ray
      def 						UpperCAmelCase_    (      self :List[Any]   )  ->      int:
       UpperCAmelCase__							=    1
       UpperCAmelCase__							=    np.array(
           [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,   dtype=np.floataa   )

       self.distributed_retriever_check(
           self.get_dummy_ray_distributed_retriever(init_retrieval=lowerCamelCase   )			,   lowerCamelCase			,   lowerCamelCase   )
       ray.shutdown()
      @require_ray
      def 						UpperCAmelCase_    (      self :List[str]   )  ->      Tuple:
       UpperCAmelCase__							=    1
       UpperCAmelCase__							=    np.array(
           [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,   dtype=np.floataa   )
       with self.assertRaises(lowerCamelCase   ):
        self.distributed_retriever_check(
            self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase			,   from_disk=lowerCamelCase   )			,   lowerCamelCase			,   lowerCamelCase			,   )
       ray.shutdown()







      @require_ray
      def 						UpperCAmelCase_    (      self :Union[str, Any]   )  ->      List[Any]:
       UpperCAmelCase__							=    1
       UpperCAmelCase__							=    np.array(
           [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,   dtype=np.floataa   )

       self.distributed_retriever_check(
           self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase			,   from_disk=lowerCamelCase   )			,   lowerCamelCase			,   lowerCamelCase   )
       ray.shutdown()











import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm



def     lowerCAmelCase		(		_lowerCAmelCase				: int    ,				_lowerCAmelCase				: bool = False						):




 """simple docstring"""







 if device_specific:
  seed += AcceleratorState().process_index
 random.seed(_lowerCAmelCase						)
 np.random.seed(_lowerCAmelCase						)
 torch.manual_seed(_lowerCAmelCase						)
 if is_xpu_available():
  torch.xpu.manual_seed_all(_lowerCAmelCase						)
 elif is_npu_available():
  torch.npu.manual_seed_all(_lowerCAmelCase						)
 else:
  torch.cuda.manual_seed_all(_lowerCAmelCase						)
 # ^^ safe to call this function even if cuda is not available
 if is_tpu_available():
  xm.set_rng_state(_lowerCAmelCase						)



def     lowerCAmelCase		(		_lowerCAmelCase				: Optional[RNGType] = None    ,				_lowerCAmelCase				: Optional[torch.Generator] = None						):




 """simple docstring"""







 if rng_type == RNGType.TORCH:
  UpperCAmelCase__							=    torch.get_rng_state()
 elif rng_type == RNGType.CUDA:
  UpperCAmelCase__							=    torch.cuda.get_rng_state()
 elif rng_type == RNGType.XLA:
  assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
  UpperCAmelCase__							=    torch.tensor(xm.get_rng_state()						)
 elif rng_type == RNGType.NPU:
  assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
  UpperCAmelCase__							=    torch.npu.get_rng_state()
 elif rng_type == RNGType.XPU:
  assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
  UpperCAmelCase__							=    torch.xpu.get_rng_state()
 elif rng_type == RNGType.GENERATOR:
  assert generator is not None, "Need a generator to synchronize its seed."
  UpperCAmelCase__							=    generator.get_state()

 # Broadcast the rng state from device 0 to other devices
 UpperCAmelCase__							=    AcceleratorState()
 if state.distributed_type == DistributedType.TPU:
  UpperCAmelCase__							=    rng_state.to(xm.xla_device()						)
  xm.collective_broadcast([rng_state]						)
  xm.mark_step()
  UpperCAmelCase__							=    rng_state.cpu()
 elif (
     state.distributed_type in CUDA_DISTRIBUTED_TYPES
     or state.distributed_type == DistributedType.MULTI_NPU
     or state.distributed_type == DistributedType.MULTI_XPU
 ):
  UpperCAmelCase__							=    rng_state.to(state.device						)
  torch.distributed.broadcast(_lowerCAmelCase    ,				0						)
  UpperCAmelCase__							=    rng_state.cpu()
 elif state.distributed_type == DistributedType.MULTI_CPU:
  torch.distributed.broadcast(_lowerCAmelCase    ,				0						)

 # Set the broadcast rng state
 if rng_type == RNGType.TORCH:
  torch.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.CUDA:
  torch.cuda.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.NPU:
  torch.npu.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.XPU:
  torch.xpu.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.XLA:
  xm.set_rng_state(rng_state.item()						)
 elif rng_type == RNGType.GENERATOR:
  generator.set_state(_lowerCAmelCase						)



def     lowerCAmelCase		(		_lowerCAmelCase				: List[Union[str, RNGType]]    ,				_lowerCAmelCase				: Optional[torch.Generator] = None						):




 """simple docstring"""







 for rng_type in rng_types:
  synchronize_rng_state(RNGType(_lowerCAmelCase						)    ,				generator=_lowerCAmelCase						)





