




'''simple docstring'''





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:
  a		:	List[Any]       =      None

if is_ray_available():
  import ray  # noqa: E402 # isort:skip
  from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
  a		:	Optional[int]       =      None
  a		:	Tuple       =      None
  a		:	Tuple       =      None





def UpperCAmelCase    (       UpperCAmelCase_						):
    if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
        _a				:							Tuple							       =      unittest.skip("""test requires Datasets, Faiss, psutil"""						)(UpperCAmelCase_						)
    return test_case





@require_distributed_retrieval
class      UpperCamelCase       (	snake_case__       ):



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Union[str, Any]							       =      tempfile.mkdtemp()
      _a				:							Optional[Any]							       =      8

      # DPR tok
      _a				:							int							       =      [
          """[UNK]""",
          """[CLS]""",
          """[SEP]""",
          """[PAD]""",
          """[MASK]""",
          """want""",
          """##want""",
          """##ed""",
          """wa""",
          """un""",
          """runn""",
          """##ing""",
          """,""",
          """low""",
          """lowest""",
      ]
      _a				:							Tuple							       =      os.path.join(self.tmpdirname		,     """dpr_tokenizer""" )
      os.makedirs(__lowerCAmelCase		,     exist_ok=__lowerCAmelCase )
      _a				:							Any							       =      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
      _a				:							Union[str, Any]							       =      [
          """l""",
          """o""",
          """w""",
          """e""",
          """r""",
          """s""",
          """t""",
          """i""",
          """d""",
          """n""",
          """\u0120""",
          """\u0120l""",
          """\u0120n""",
          """\u0120lo""",
          """\u0120low""",
          """er""",
          """\u0120lowest""",
          """\u0120newer""",
          """\u0120wider""",
          """<unk>""",
      ]
      _a				:							Tuple							       =      dict(zip(__lowerCAmelCase		,     range(len(__lowerCAmelCase ) ) ) )
      _a				:							Any							       =      ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
      _a				:							Tuple							       =      {"""unk_token""": """<unk>"""}

      _a				:							Optional[Any]							       =      os.path.join(self.tmpdirname		,     """bart_tokenizer""" )
      os.makedirs(__lowerCAmelCase		,     exist_ok=__lowerCAmelCase )
      _a				:							List[Any]							       =      os.path.join(__lowerCAmelCase		,     BART_VOCAB_FILES_NAMES["""vocab_file"""] )
      _a				:							Union[str, Any]							       =      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     a_							(			self ):







      '''simple docstring'''


      return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,     """dpr_tokenizer""" ) )



  def     a_							(			self ):







      '''simple docstring'''


      return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		,     """bart_tokenizer""" ) )



  def     a_							(			self ):







      '''simple docstring'''


      shutil.rmtree(self.tmpdirname )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Tuple							       =      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     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=1_2345 ):







      '''simple docstring'''


      _a				:							str							       =      self.get_dummy_dataset()
      _a				:							Any							       =      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:
          _a				:							Optional[Any]							       =      dataset
          _a				:							Tuple							       =      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     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      ray.init(local_mode=__lowerCAmelCase )
      _a				:							Union[str, Any]							       =      RagConfig(
          retrieval_vector_size=self.retrieval_vector_size		,     question_encoder=DPRConfig().to_dict()		,     generator=BartConfig().to_dict()		,     )
      _a				:							List[str]							       =      ray.remote(__lowerCAmelCase )
      _a				:							Any							       =      [remote_cls.remote() for _ in range(1 )]
      with patch("""transformers.models.rag.retrieval_rag.load_dataset""" ) as mock_load_dataset:
          _a				:							str							       =      self.get_dummy_dataset()
          _a				:							int							       =      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     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase=1_2345 ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      self.get_dummy_dataset()
      _a				:							List[Any]							       =      RagConfig(
          retrieval_vector_size=self.retrieval_vector_size		,     question_encoder=DPRConfig().to_dict()		,     generator=BartConfig().to_dict()		,     index_name="""custom"""		,     )
      if from_disk:
          _a				:							Any							       =      os.path.join(self.tmpdirname		,     """dataset""" )
          _a				:							str							       =      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
          _a				:							List[Any]							       =      RagPyTorchDistributedRetriever(
              __lowerCAmelCase		,     question_encoder_tokenizer=self.get_dpr_tokenizer()		,     generator_tokenizer=self.get_bart_tokenizer()		,     )
      else:
          _a				:							Any							       =      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     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      ray.init(local_mode=__lowerCAmelCase )
      _a				:							Any							       =      self.get_dummy_dataset()
      _a				:							str							       =      RagConfig(
          retrieval_vector_size=self.retrieval_vector_size		,     question_encoder=DPRConfig().to_dict()		,     generator=BartConfig().to_dict()		,     index_name="""custom"""		,     )
      _a				:							int							       =      ray.remote(__lowerCAmelCase )
      _a				:							Optional[Any]							       =      [remote_cls.remote() for _ in range(1 )]
      if from_disk:
          _a				:							List[Any]							       =      os.path.join(self.tmpdirname		,     """dataset""" )
          _a				:							Optional[int]							       =      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
          _a				:							Optional[int]							       =      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:
          _a				:							Tuple							       =      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     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a       , _a       , _a				:							Union[str, Any]							       =      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     a_							(			self ):







      '''simple docstring'''


      _a				:							Dict							       =      1
      _a				:							Any							       =      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     a_							(			self ):







      '''simple docstring'''


      _a				:							Dict							       =      1
      _a				:							int							       =      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     a_							(			self ):







      '''simple docstring'''


      _a				:							Tuple							       =      1
      _a				:							int							       =      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     a_							(			self ):







      '''simple docstring'''


      _a				:							str							       =      1
      _a				:							Union[str, Any]							       =      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     a_							(			self ):







      '''simple docstring'''


      _a				:							Dict							       =      1
      _a				:							Optional[int]							       =      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     a_							(			self ):







      '''simple docstring'''


      _a				:							Tuple							       =      1
      _a				:							Optional[Any]							       =      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()








'''simple docstring'''





import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_reformer import ReformerTokenizer
else:
  a		:	Optional[int]       =      None


a		:	Optional[Any]       =      logging.get_logger(__name__)


a		:	Union[str, Any]       =      '''▁'''

a		:	Any       =      {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:	List[Any]       =      {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    },
    '''tokenizer_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'''
        )
    },
}

a		:	Any       =      {
    '''google/reformer-crime-and-punishment''': 5_2_4_2_8_8,
}




class      UpperCamelCase       (	snake_case__       ):
  a     :str          =					VOCAB_FILES_NAMES
  a     :List[Any]          =					PRETRAINED_VOCAB_FILES_MAP
  a     :Optional[int]          =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a     :Any          =					['input_ids', 'attention_mask']
  a     :int          =					ReformerTokenizer



  def __init__(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase="</s>"		,     __lowerCAmelCase="<unk>"		,     __lowerCAmelCase=[]		,     **__lowerCAmelCase		,     ):







      '''simple docstring'''


      super().__init__(
          __lowerCAmelCase		,     tokenizer_file=__lowerCAmelCase		,     eos_token=__lowerCAmelCase		,     unk_token=__lowerCAmelCase		,     additional_special_tokens=__lowerCAmelCase		,     **__lowerCAmelCase		,     )

      _a				:							List[str]							       =      vocab_file
      _a				:							Optional[int]							       =      False if not self.vocab_file else True



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase = None ):







      '''simple docstring'''


      if not self.can_save_slow_tokenizer:
          raise ValueError(
              """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
              """tokenizer.""" )

      if not os.path.isdir(__lowerCAmelCase ):
          logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
          return
      _a				:							List[str]							       =      os.path.join(
          __lowerCAmelCase		,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

      if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCAmelCase ):
          copyfile(self.vocab_file		,     __lowerCAmelCase )

      return (out_vocab_file,)


