




'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase_	 =		logging.get_logger(__name__)

lowerCamelCase_	 =		{
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Dict ,  __A       :				int ,  __A       :				Tuple ,  __A       :				Dict ,  __A       :				int						)				->				str:
   for attribute in key.split("."						):
      _SCREAMING_SNAKE_CASE =   getattr(__A ,  __A						)

   if weight_type is not None:
      _SCREAMING_SNAKE_CASE =   getattr(__A ,  __A						).shape
   else:
      _SCREAMING_SNAKE_CASE =   hf_pointer.shape

   assert hf_shape == value.shape, (
       f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
       f""" {value.shape} for {full_name}"""
   )

   if weight_type == "weight":
      _SCREAMING_SNAKE_CASE =   value
   elif weight_type == "weight_g":
      _SCREAMING_SNAKE_CASE =   value
   elif weight_type == "weight_v":
      _SCREAMING_SNAKE_CASE =   value
   elif weight_type == "bias":
      _SCREAMING_SNAKE_CASE =   value
   else:
      _SCREAMING_SNAKE_CASE =   value

   logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""						)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int ,  __A       :				str ,  __A       :				List[str]						)				->				Tuple:
   _SCREAMING_SNAKE_CASE =   []
   _SCREAMING_SNAKE_CASE =   fairseq_model.state_dict()

   _SCREAMING_SNAKE_CASE =   hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      _SCREAMING_SNAKE_CASE =   False
      if "conv_layers" in name:
         load_conv_layer(
             __A ,  __A ,  __A ,  __A ,  hf_model.config.feat_extract_norm == "group" ,  )
         _SCREAMING_SNAKE_CASE =   True
      else:
         for key, mapped_key in MAPPING.items():
            _SCREAMING_SNAKE_CASE =   "sew_d." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

            if key in name or key.split("w2v_model."						)[-1] == name.split("."						)[0]:
               _SCREAMING_SNAKE_CASE =   True
               if "*" in mapped_key:
                  _SCREAMING_SNAKE_CASE =   name.split(__A						)[0].split("."						)[-2]
                  if not layer_index.isnumeric():
                     continue
                  _SCREAMING_SNAKE_CASE =   mapped_key.replace("*" ,  __A						)
               if "weight_g" in name:
                  _SCREAMING_SNAKE_CASE =   "weight_g"
               elif "weight_v" in name:
                  _SCREAMING_SNAKE_CASE =   "weight_v"
               elif "weight" in name:
                  _SCREAMING_SNAKE_CASE =   "weight"
               elif "bias" in name:
                  _SCREAMING_SNAKE_CASE =   "bias"
               else:
                  _SCREAMING_SNAKE_CASE =   None
               set_recursively(__A ,  __A ,  __A ,  __A ,  __A						)
            continue
      if not is_used:
         unused_weights.append(__A						)

   logger.warning(f"""Unused weights: {unused_weights}"""						)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int ,  __A       :				Tuple ,  __A       :				Optional[int] ,  __A       :				Any ,  __A       :				Union[str, Any]						)				->				List[str]:
   _SCREAMING_SNAKE_CASE =   full_name.split("conv_layers."						)[-1]
   _SCREAMING_SNAKE_CASE =   name.split("."						)
   _SCREAMING_SNAKE_CASE =   int(items[0]						)
   _SCREAMING_SNAKE_CASE =   int(items[1]						)

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
         )
         _SCREAMING_SNAKE_CASE =   value
         logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
         )
         _SCREAMING_SNAKE_CASE =   value
         logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
             " found."
         )
         _SCREAMING_SNAKE_CASE =   value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
         )
         _SCREAMING_SNAKE_CASE =   value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
   else:
      unused_weights.append(__A						)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Tuple ,  __A       :				Dict						)				->				int:
   _SCREAMING_SNAKE_CASE =   SEWDConfig()
   if is_finetuned:
      _SCREAMING_SNAKE_CASE =   model.wav_encoder.wav_model.cfg
   else:
      _SCREAMING_SNAKE_CASE =   model.cfg

   _SCREAMING_SNAKE_CASE =   fs_config.conv_bias
   _SCREAMING_SNAKE_CASE =   eval(fs_config.conv_feature_layers						)
   _SCREAMING_SNAKE_CASE =   [x[0] for x in conv_layers]
   _SCREAMING_SNAKE_CASE =   [x[1] for x in conv_layers]
   _SCREAMING_SNAKE_CASE =   [x[2] for x in conv_layers]
   _SCREAMING_SNAKE_CASE =   "gelu"
   _SCREAMING_SNAKE_CASE =   "layer" if fs_config.extractor_mode == "layer_norm" else "group"
   _SCREAMING_SNAKE_CASE =   0.0
   _SCREAMING_SNAKE_CASE =   fs_config.activation_fn.name
   _SCREAMING_SNAKE_CASE =   fs_config.encoder_embed_dim
   _SCREAMING_SNAKE_CASE =   0.0_2
   _SCREAMING_SNAKE_CASE =   fs_config.encoder_ffn_embed_dim
   _SCREAMING_SNAKE_CASE =   1e-5
   _SCREAMING_SNAKE_CASE =   fs_config.encoder_layerdrop
   _SCREAMING_SNAKE_CASE =   fs_config.encoder_attention_heads
   _SCREAMING_SNAKE_CASE =   fs_config.conv_pos_groups
   _SCREAMING_SNAKE_CASE =   fs_config.conv_pos
   _SCREAMING_SNAKE_CASE =   len(__A						)
   _SCREAMING_SNAKE_CASE =   fs_config.encoder_layers
   _SCREAMING_SNAKE_CASE =   fs_config.squeeze_factor
   # DeBERTa-specific parameters:
   _SCREAMING_SNAKE_CASE =   fs_config.max_position_embeddings
   _SCREAMING_SNAKE_CASE =   fs_config.position_buckets
   _SCREAMING_SNAKE_CASE =   fs_config.share_att_key
   _SCREAMING_SNAKE_CASE =   fs_config.relative_attention
   _SCREAMING_SNAKE_CASE =   fs_config.position_biased_input
   _SCREAMING_SNAKE_CASE =   tuple(fs_config.pos_att_type.split("|"						)						)
   _SCREAMING_SNAKE_CASE =   fs_config.norm_rel_ebd

   # take care of any params that are overridden by the Wav2VecCtc model
   if is_finetuned:
      _SCREAMING_SNAKE_CASE =   model.cfg
      _SCREAMING_SNAKE_CASE =   fs_config.final_dropout
      _SCREAMING_SNAKE_CASE =   fs_config.layerdrop
   _SCREAMING_SNAKE_CASE =   fs_config.activation_dropout
   _SCREAMING_SNAKE_CASE =   fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
   _SCREAMING_SNAKE_CASE =   fs_config.attention_dropout
   _SCREAMING_SNAKE_CASE =   fs_config.dropout_input
   _SCREAMING_SNAKE_CASE =   fs_config.dropout
   _SCREAMING_SNAKE_CASE =   fs_config.mask_channel_length
   _SCREAMING_SNAKE_CASE =   fs_config.mask_channel_prob
   _SCREAMING_SNAKE_CASE =   fs_config.mask_length
   _SCREAMING_SNAKE_CASE =   fs_config.mask_prob

   _SCREAMING_SNAKE_CASE =   "Wav2Vec2FeatureExtractor"
   _SCREAMING_SNAKE_CASE =   "Wav2Vec2CTCTokenizer"

   return config
@torch.no_grad()
def 				SCREAMING_SNAKE_CASE_				(							__A       :				str ,  __A       :				str ,  __A       :				Dict=None ,  __A       :				Dict=None ,  __A       :				str=True						)				->				Any:

   if is_finetuned:
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path] ,  arg_overrides={"data": "/".join(dict_path.split("/"						)[:-1]						)}						)
   else:
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

   if config_path is not None:
      _SCREAMING_SNAKE_CASE =   SEWDConfig.from_pretrained(__A						)
   else:
      _SCREAMING_SNAKE_CASE =   convert_config(model[0] ,  __A						)
   _SCREAMING_SNAKE_CASE =   model[0].eval()

   _SCREAMING_SNAKE_CASE =   True if config.feat_extract_norm == "layer" else False
   _SCREAMING_SNAKE_CASE =   WavaVecaFeatureExtractor(
       feature_size=1 ,  sampling_rate=1_60_00 ,  padding_value=0 ,  do_normalize=__A ,  return_attention_mask=__A ,  )

   if is_finetuned:
      if dict_path:
         _SCREAMING_SNAKE_CASE =   Dictionary.load(__A						)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         _SCREAMING_SNAKE_CASE =   target_dict.pad_index
         _SCREAMING_SNAKE_CASE =   target_dict.bos_index
         _SCREAMING_SNAKE_CASE =   target_dict.pad_index
         _SCREAMING_SNAKE_CASE =   target_dict.bos_index
         _SCREAMING_SNAKE_CASE =   target_dict.eos_index
         _SCREAMING_SNAKE_CASE =   len(target_dict.symbols						)
         _SCREAMING_SNAKE_CASE =   os.path.join(__A ,  "vocab.json"						)
         if not os.path.isdir(__A						):
            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__A						)						)
            return
         os.makedirs(__A ,  exist_ok=__A						)
         with open(__A ,  "w" ,  encoding="utf-8"						) as vocab_handle:
            json.dump(target_dict.indices ,  __A						)
         _SCREAMING_SNAKE_CASE =   WavaVecaCTCTokenizer(
             __A ,  unk_token=target_dict.unk_word ,  pad_token=target_dict.pad_word ,  bos_token=target_dict.bos_word ,  eos_token=target_dict.eos_word ,  word_delimiter_token="|" ,  do_lower_case=__A ,  )
         _SCREAMING_SNAKE_CASE =   WavaVecaProcessor(feature_extractor=__A ,  tokenizer=__A						)
         processor.save_pretrained(__A						)

      _SCREAMING_SNAKE_CASE =   SEWDForCTC(__A						)
   else:
      _SCREAMING_SNAKE_CASE =   SEWDModel(__A						)
      feature_extractor.save_pretrained(__A						)

   recursively_load_weights(__A ,  __A ,  __A						)

   hf_model.save_pretrained(__A						)


if __name__ == "__main__":
 lowerCamelCase_	 =		argparse.ArgumentParser()
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
 parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
 parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
 parser.add_argument(
     '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
 )
 lowerCamelCase_	 =		parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )







'''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_				(							__A       :				Any						)				->				Union[str, Any]:
   if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
      _SCREAMING_SNAKE_CASE =   unittest.skip("test requires Datasets, Faiss, psutil"						)(__A						)
   return test_case







@require_distributed_retrieval
class 	lowercase_					(				A     ):


   """simple docstring"""

   def 							lowerCAmelCase_    (					self      :			List[str]					):

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

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

      _SCREAMING_SNAKE_CASE =   os.path.join(self.tmpdirname						,   "bart_tokenizer"					)
      os.makedirs(__lowerCamelCase						,   exist_ok=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   os.path.join(__lowerCamelCase						,   BART_VOCAB_FILES_NAMES["vocab_file"]					)
      _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			List[Any]					):

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



   def 							lowerCAmelCase_    (					self      :			Any					):

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



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      shutil.rmtree(self.tmpdirname					)



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			Optional[Any]=1_2_3_4_5					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.get_dummy_dataset()
      _SCREAMING_SNAKE_CASE =   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:
         _SCREAMING_SNAKE_CASE =   dataset
         _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Union[str, Any]						,   __lowerCamelCase      :			bool					):

      """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					)
      _SCREAMING_SNAKE_CASE =   RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,   question_encoder=DPRConfig().to_dict()						,   generator=BartConfig().to_dict()						,   )
      _SCREAMING_SNAKE_CASE =   ray.remote(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   [remote_cls.remote() for _ in range(1					)]
      with patch("transformers.models.rag.retrieval_rag.load_dataset"					) as mock_load_dataset:
         _SCREAMING_SNAKE_CASE =   self.get_dummy_dataset()
         _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Dict						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			List[str]=1_2_3_4_5					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.get_dummy_dataset()
      _SCREAMING_SNAKE_CASE =   RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,   question_encoder=DPRConfig().to_dict()						,   generator=BartConfig().to_dict()						,   index_name="custom"						,   )
      if from_disk:
         _SCREAMING_SNAKE_CASE =   os.path.join(self.tmpdirname						,   "dataset"					)
         _SCREAMING_SNAKE_CASE =   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
         _SCREAMING_SNAKE_CASE =   RagPyTorchDistributedRetriever(
             __lowerCamelCase						,   question_encoder_tokenizer=self.get_dpr_tokenizer()						,   generator_tokenizer=self.get_bart_tokenizer()						,   )
      else:
         _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			bool					):

      """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					)
      _SCREAMING_SNAKE_CASE =   self.get_dummy_dataset()
      _SCREAMING_SNAKE_CASE =   RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,   question_encoder=DPRConfig().to_dict()						,   generator=BartConfig().to_dict()						,   index_name="custom"						,   )
      _SCREAMING_SNAKE_CASE =   ray.remote(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   [remote_cls.remote() for _ in range(1					)]
      if from_disk:
         _SCREAMING_SNAKE_CASE =   os.path.join(self.tmpdirname						,   "dataset"					)
         _SCREAMING_SNAKE_CASE =   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
         _SCREAMING_SNAKE_CASE =   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:
         _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			RagRetriever						,   __lowerCamelCase      :			np.array						,   __lowerCamelCase      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   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()

