






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__			=   {
    "configuration_gptsan_japanese": ["GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTSanJapaneseConfig"],
    "tokenization_gptsan_japanese": ["GPTSanJapaneseTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCamelCase__			=   [
          "GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST",
          "GPTSanJapaneseForConditionalGeneration",
          "GPTSanJapaneseModel",
          "GPTSanJapanesePreTrainedModel",
      ]
      lowerCamelCase__			=   [
          "GPTSanJapaneseTokenizer",
      ]


if TYPE_CHECKING:
      from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
      from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gptsan_japanese import (
                GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTSanJapaneseForConditionalGeneration,
                GPTSanJapaneseModel,
                GPTSanJapanesePreTrainedModel,
            )
            from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
      import sys

      lowerCamelCase__			=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












"""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:
      lowerCamelCase__			=   None

if is_ray_available():
      import ray  # noqa: E402 # isort:skip
      from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
      lowerCamelCase__			=   None
      lowerCamelCase__			=   None
      lowerCamelCase__			=   None






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Any    ):
  if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
    A__											=							unittest.skip("""test requires Datasets, Faiss, psutil"""    )(UpperCamelCase    )
  return test_case







@require_distributed_retrieval
class 					_UpperCamelCase		( __snake_case):






    def  A    (self   ):


      """simple docstring"""
      A__											=							tempfile.mkdtemp()
      A__											=							8

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

      A__											=							os.path.join(self.tmpdirname						,      """bart_tokenizer"""   )
      os.makedirs(lowerCamelCase__						,      exist_ok=lowerCamelCase__   )
      A__											=							os.path.join(lowerCamelCase__						,      BART_VOCAB_FILES_NAMES["""vocab_file"""]   )
      A__											=							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__											=							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_2_3_4_5   ):


      """simple docstring"""
      A__											=							self.get_dummy_dataset()
      A__											=							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__											=							dataset
        A__											=							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"""
      # 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__   )
      A__											=							RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,      question_encoder=DPRConfig().to_dict()						,      generator=BartConfig().to_dict()						,      )
      A__											=							ray.remote(lowerCamelCase__   )
      A__											=							[remote_cls.remote() for _ in range(1   )]
      with patch("""transformers.models.rag.retrieval_rag.load_dataset"""   ) as mock_load_dataset:
        A__											=							self.get_dummy_dataset()
        A__											=							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_2_3_4_5   ):


      """simple docstring"""
      A__											=							self.get_dummy_dataset()
      A__											=							RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,      question_encoder=DPRConfig().to_dict()						,      generator=BartConfig().to_dict()						,      index_name="""custom"""						,      )
      if from_disk:
        A__											=							os.path.join(self.tmpdirname						,      """dataset"""   )
        A__											=							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__											=							RagPyTorchDistributedRetriever(
            lowerCamelCase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      )
      else:
        A__											=							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"""
      # 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__   )
      A__											=							self.get_dummy_dataset()
      A__											=							RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,      question_encoder=DPRConfig().to_dict()						,      generator=BartConfig().to_dict()						,      index_name="""custom"""						,      )
      A__											=							ray.remote(lowerCamelCase__   )
      A__											=							[remote_cls.remote() for _ in range(1   )]
      if from_disk:
        A__											=							os.path.join(self.tmpdirname						,      """dataset"""   )
        A__											=							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__											=							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__											=							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__											=							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__											=							1
      A__											=							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__											=							1
      A__											=							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__											=							1
      A__											=							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__											=							1
      A__											=							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__											=							1
      A__											=							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__											=							1
      A__											=							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()




