






"""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"""
      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"""
      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()








"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__			=   logging.get_logger(__name__)

lowerCamelCase__			=   {
    "google/electra-small-generator": "https://huggingface.co/google/electra-small-generator/resolve/main/config.json",
    "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/config.json",
    "google/electra-large-generator": "https://huggingface.co/google/electra-large-generator/resolve/main/config.json",
    "google/electra-small-discriminator": (
        "https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"
    ),
    "google/electra-base-discriminator": (
        "https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"
    ),
    "google/electra-large-discriminator": (
        "https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"
    ),
}







class 					_UpperCamelCase		( __snake_case):
    __lowerCamelCase         =  "electra"






    def __init__(self						,      lowerCamelCase__=3_0_5_2_2						,      lowerCamelCase__=1_2_8						,      lowerCamelCase__=2_5_6						,      lowerCamelCase__=1_2						,      lowerCamelCase__=4						,      lowerCamelCase__=1_0_2_4						,      lowerCamelCase__="gelu"						,      lowerCamelCase__=0.1						,      lowerCamelCase__=0.1						,      lowerCamelCase__=5_1_2						,      lowerCamelCase__=2						,      lowerCamelCase__=0.0_2						,      lowerCamelCase__=1E-12						,      lowerCamelCase__="first"						,      lowerCamelCase__=True						,      lowerCamelCase__="gelu"						,      lowerCamelCase__=0.1						,      lowerCamelCase__=0						,      lowerCamelCase__="absolute"						,      lowerCamelCase__=True						,      lowerCamelCase__=None						,      **lowerCamelCase__						,      ):


      """simple docstring"""
      super().__init__(pad_token_id=lowerCamelCase__						,      **lowerCamelCase__   )

      A__											=							vocab_size
      A__											=							embedding_size
      A__											=							hidden_size
      A__											=							num_hidden_layers
      A__											=							num_attention_heads
      A__											=							intermediate_size
      A__											=							hidden_act
      A__											=							hidden_dropout_prob
      A__											=							attention_probs_dropout_prob
      A__											=							max_position_embeddings
      A__											=							type_vocab_size
      A__											=							initializer_range
      A__											=							layer_norm_eps

      A__											=							summary_type
      A__											=							summary_use_proj
      A__											=							summary_activation
      A__											=							summary_last_dropout
      A__											=							position_embedding_type
      A__											=							use_cache
      A__											=							classifier_dropout







class 					_UpperCamelCase		( __snake_case):






    @property
    def  A    (self   ):


      """simple docstring"""
      if self.task == "multiple-choice":
        A__											=							{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
        A__											=							{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
              ("""token_type_ids""", dynamic_axis),
          ]   )




