



"""simple docstring"""
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BertTokenizer,
    ViltConfig,
    ViltForImageAndTextRetrieval,
    ViltForImagesAndTextClassification,
    ViltForMaskedLM,
    ViltForQuestionAnswering,
    ViltImageProcessor,
    ViltProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A:     Union[str, Any]								=	logging.get_logger(__name__)

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	Optional[Any]=False    ,	UpperCamelCase		:	Optional[Any]=False    ,	UpperCamelCase		:	str=False		):
 UpperCAmelCase			:      List[str]			=			[]
 for i in range(config.num_hidden_layers		):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((F"transformer.blocks.{i}.norm1.weight", F"vilt.encoder.layer.{i}.layernorm_before.weight")		)
  rename_keys.append((F"transformer.blocks.{i}.norm1.bias", F"vilt.encoder.layer.{i}.layernorm_before.bias")		)
  rename_keys.append(
      (F"transformer.blocks.{i}.attn.proj.weight", F"vilt.encoder.layer.{i}.attention.output.dense.weight")		)
  rename_keys.append(
      (F"transformer.blocks.{i}.attn.proj.bias", F"vilt.encoder.layer.{i}.attention.output.dense.bias")		)
  rename_keys.append((F"transformer.blocks.{i}.norm2.weight", F"vilt.encoder.layer.{i}.layernorm_after.weight")		)
  rename_keys.append((F"transformer.blocks.{i}.norm2.bias", F"vilt.encoder.layer.{i}.layernorm_after.bias")		)
  rename_keys.append(
      (F"transformer.blocks.{i}.mlp.fc1.weight", F"vilt.encoder.layer.{i}.intermediate.dense.weight")		)
  rename_keys.append((F"transformer.blocks.{i}.mlp.fc1.bias", F"vilt.encoder.layer.{i}.intermediate.dense.bias")		)
  rename_keys.append((F"transformer.blocks.{i}.mlp.fc2.weight", F"vilt.encoder.layer.{i}.output.dense.weight")		)
  rename_keys.append((F"transformer.blocks.{i}.mlp.fc2.bias", F"vilt.encoder.layer.{i}.output.dense.bias")		)

 # embeddings
 rename_keys.extend(
     [
         # text embeddings
         ("""text_embeddings.word_embeddings.weight""", """vilt.embeddings.text_embeddings.word_embeddings.weight"""),
         (
             """text_embeddings.position_embeddings.weight""",
             """vilt.embeddings.text_embeddings.position_embeddings.weight""",
         ),
         ("""text_embeddings.position_ids""", """vilt.embeddings.text_embeddings.position_ids"""),
         (
             """text_embeddings.token_type_embeddings.weight""",
             """vilt.embeddings.text_embeddings.token_type_embeddings.weight""",
         ),
         ("""text_embeddings.LayerNorm.weight""", """vilt.embeddings.text_embeddings.LayerNorm.weight"""),
         ("""text_embeddings.LayerNorm.bias""", """vilt.embeddings.text_embeddings.LayerNorm.bias"""),
         # patch embeddings
         ("""transformer.cls_token""", """vilt.embeddings.cls_token"""),
         ("""transformer.patch_embed.proj.weight""", """vilt.embeddings.patch_embeddings.projection.weight"""),
         ("""transformer.patch_embed.proj.bias""", """vilt.embeddings.patch_embeddings.projection.bias"""),
         ("""transformer.pos_embed""", """vilt.embeddings.position_embeddings"""),
         # token type embeddings
         ("""token_type_embeddings.weight""", """vilt.embeddings.token_type_embeddings.weight"""),
     ]		)

 # final layernorm + pooler
 rename_keys.extend(
     [
         ("""transformer.norm.weight""", """vilt.layernorm.weight"""),
         ("""transformer.norm.bias""", """vilt.layernorm.bias"""),
         ("""pooler.dense.weight""", """vilt.pooler.dense.weight"""),
         ("""pooler.dense.bias""", """vilt.pooler.dense.bias"""),
     ]		)

 # classifier head(s)
 if vqa_model:
  # classification head
  rename_keys.extend(
      [
          ("""vqa_classifier.0.weight""", """classifier.0.weight"""),
          ("""vqa_classifier.0.bias""", """classifier.0.bias"""),
          ("""vqa_classifier.1.weight""", """classifier.1.weight"""),
          ("""vqa_classifier.1.bias""", """classifier.1.bias"""),
          ("""vqa_classifier.3.weight""", """classifier.3.weight"""),
          ("""vqa_classifier.3.bias""", """classifier.3.bias"""),
      ]		)
 elif nlvr_model:
  # classification head
  rename_keys.extend(
      [
          ("""nlvr2_classifier.0.weight""", """classifier.0.weight"""),
          ("""nlvr2_classifier.0.bias""", """classifier.0.bias"""),
          ("""nlvr2_classifier.1.weight""", """classifier.1.weight"""),
          ("""nlvr2_classifier.1.bias""", """classifier.1.bias"""),
          ("""nlvr2_classifier.3.weight""", """classifier.3.weight"""),
          ("""nlvr2_classifier.3.bias""", """classifier.3.bias"""),
      ]		)
 else:
  pass

 return rename_keys

def    _snake_case		(		UpperCamelCase		:	Optional[int]    ,	UpperCamelCase		:	Tuple		):
 for i in range(config.num_hidden_layers		):
  UpperCAmelCase			:      List[Any]			=			"""vilt."""
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  UpperCAmelCase			:      List[str]			=			state_dict.pop(F"transformer.blocks.{i}.attn.qkv.weight"		)
  UpperCAmelCase			:      Any			=			state_dict.pop(F"transformer.blocks.{i}.attn.qkv.bias"		)
  # next, add query, keys and values (in that order) to the state dict
  UpperCAmelCase			:      List[str]			=			in_proj_weight[
      : config.hidden_size, :
  ]
  UpperCAmelCase			:      int			=			in_proj_bias[: config.hidden_size]
  UpperCAmelCase			:      List[Any]			=			in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  UpperCAmelCase			:      Optional[int]			=			in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  UpperCAmelCase			:      Any			=			in_proj_weight[
      -config.hidden_size :, :
  ]
  UpperCAmelCase			:      Any			=			in_proj_bias[-config.hidden_size :]

def    _snake_case		(		UpperCamelCase		:	List[str]		):
 UpperCAmelCase			:      Dict			=			["""head.weight""", """head.bias"""]
 for k in ignore_keys:
  state_dict.pop(UpperCamelCase    ,	UpperCamelCase		)

def    _snake_case		(		UpperCamelCase		:	Optional[Any]    ,	UpperCamelCase		:	str    ,	UpperCamelCase		:	List[str]		):
 UpperCAmelCase			:      Any			=			dct.pop(UpperCamelCase		)
 UpperCAmelCase			:      Dict			=			val

@torch.no_grad()
def    _snake_case		(		UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	Union[str, Any]		):
 UpperCAmelCase			:      Any			=			ViltConfig(image_size=384    ,	patch_size=32    ,	tie_word_embeddings=UpperCamelCase		)
 UpperCAmelCase			:      Optional[Any]			=			False
 UpperCAmelCase			:      List[str]			=			False
 UpperCAmelCase			:      List[Any]			=			False
 UpperCAmelCase			:      Tuple			=			False
 if "vqa" in checkpoint_url:
  UpperCAmelCase			:      int			=			True
  UpperCAmelCase			:      List[str]			=			3129
  UpperCAmelCase			:      Union[str, Any]			=			"""huggingface/label-files"""
  UpperCAmelCase			:      Optional[Any]			=			"""vqa2-id2label.json"""
  UpperCAmelCase			:      List[Any]			=			json.load(open(hf_hub_download(UpperCamelCase    ,	UpperCamelCase    ,	repo_type="""dataset"""		)    ,	"""r"""		)		)
  UpperCAmelCase			:      int			=			{int(UpperCamelCase		): v for k, v in idalabel.items()}
  UpperCAmelCase			:      Any			=			idalabel
  UpperCAmelCase			:      List[str]			=			{v: k for k, v in idalabel.items()}
  UpperCAmelCase			:      Any			=			ViltForQuestionAnswering(UpperCamelCase		)
 elif "nlvr" in checkpoint_url:
  UpperCAmelCase			:      Any			=			True
  UpperCAmelCase			:      Optional[int]			=			2
  UpperCAmelCase			:      Tuple			=			{0: """False""", 1: """True"""}
  UpperCAmelCase			:      int			=			{v: k for k, v in config.idalabel.items()}
  UpperCAmelCase			:      Union[str, Any]			=			3
  UpperCAmelCase			:      Tuple			=			ViltForImagesAndTextClassification(UpperCamelCase		)
 elif "irtr" in checkpoint_url:
  UpperCAmelCase			:      Union[str, Any]			=			True
  UpperCAmelCase			:      int			=			ViltForImageAndTextRetrieval(UpperCamelCase		)
 elif "mlm_itm" in checkpoint_url:
  UpperCAmelCase			:      Tuple			=			True
  UpperCAmelCase			:      int			=			ViltForMaskedLM(UpperCamelCase		)
 else:
  raise ValueError("""Unknown model type"""		)

 # load state_dict of original model, remove and rename some keys
 UpperCAmelCase			:      Dict			=			torch.hub.load_state_dict_from_url(UpperCamelCase    ,	map_location="""cpu"""		)["""state_dict"""]
 UpperCAmelCase			:      Optional[Any]			=			create_rename_keys(UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase		)
 for src, dest in rename_keys:
  rename_key(UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase		)
 read_in_q_k_v(UpperCamelCase    ,	UpperCamelCase		)
 if mlm_model or irtr_model:
  UpperCAmelCase			:      Any			=			["""itm_score.fc.weight""", """itm_score.fc.bias"""]
  for k in ignore_keys:
   state_dict.pop(UpperCamelCase    ,	UpperCamelCase		)

    # load state dict into HuggingFace model
 model.eval()
 if mlm_model:
  UpperCAmelCase			:      List[Any]			=			model.load_state_dict(UpperCamelCase    ,	strict=UpperCamelCase		)
  assert missing_keys == ["mlm_score.decoder.bias"]
 else:
  model.load_state_dict(UpperCamelCase		)

 # Define processor
 UpperCAmelCase			:      Optional[int]			=			ViltImageProcessor(size=384		)
 UpperCAmelCase			:      str			=			BertTokenizer.from_pretrained("""bert-base-uncased"""		)
 UpperCAmelCase			:      int			=			ViltProcessor(UpperCamelCase    ,	UpperCamelCase		)

 # Forward pass on example inputs (image + text)
 if nlvr_model:
  UpperCAmelCase			:      int			=			Image.open(requests.get("""https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg"""    ,	stream=UpperCamelCase		).raw		)
  UpperCAmelCase			:      Union[str, Any]			=			Image.open(requests.get("""https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg"""    ,	stream=UpperCamelCase		).raw		)
  UpperCAmelCase			:      Optional[int]			=			(
      """The left image contains twice the number of dogs as the right image, and at least two dogs in total are"""
      """ standing."""
  )
  UpperCAmelCase			:      Optional[int]			=			processor(UpperCamelCase    ,	UpperCamelCase    ,	return_tensors="""pt"""		)
  UpperCAmelCase			:      List[str]			=			processor(UpperCamelCase    ,	UpperCamelCase    ,	return_tensors="""pt"""		)
  UpperCAmelCase			:      str			=			model(
      input_ids=encoding_a.input_ids    ,	pixel_values=encoding_a.pixel_values    ,	pixel_values_a=encoding_a.pixel_values    ,	)
 else:
  UpperCAmelCase			:      List[Any]			=			Image.open(requests.get("""http://images.cocodataset.org/val2017/000000039769.jpg"""    ,	stream=UpperCamelCase		).raw		)
  if mlm_model:
   UpperCAmelCase			:      str			=			"""a bunch of [MASK] laying on a [MASK]."""
  else:
   UpperCAmelCase			:      List[str]			=			"""How many cats are there?"""
  UpperCAmelCase			:      Union[str, Any]			=			processor(UpperCamelCase    ,	UpperCamelCase    ,	return_tensors="""pt"""		)
  UpperCAmelCase			:      Any			=			model(**UpperCamelCase		)

 # Verify outputs
 if mlm_model:
  UpperCAmelCase			:      str			=			torch.Size([1, 11, 30522]		)
  UpperCAmelCase			:      Dict			=			torch.tensor([-12.5061, -12.5123, -12.5174]		)
  assert outputs.logits.shape == expected_shape
  assert torch.allclose(outputs.logits[0, 0, :3]    ,	UpperCamelCase    ,	atol=1e-4		)

  # verify masked token prediction equals "cats"
  UpperCAmelCase			:      Optional[int]			=			outputs.logits[0, 4, :].argmax(-1		).item()
  assert tokenizer.decode([predicted_id]		) == "cats"
 elif vqa_model:
  UpperCAmelCase			:      Dict			=			torch.Size([1, 3129]		)
  UpperCAmelCase			:      Union[str, Any]			=			torch.tensor([-15.9495, -18.1472, -10.3041]		)
  assert torch.allclose(outputs.logits[0, :3]    ,	UpperCamelCase    ,	atol=1e-4		)
  assert outputs.logits.shape == expected_shape
  assert torch.allclose(outputs.logits[0, 0, :3]    ,	UpperCamelCase    ,	atol=1e-4		)

  # verify vqa prediction equals "2"
  UpperCAmelCase			:      int			=			outputs.logits.argmax(-1		).item()
  assert model.config.idalabel[predicted_idx] == "2"
 elif nlvr_model:
  UpperCAmelCase			:      List[str]			=			torch.Size([1, 2]		)
  UpperCAmelCase			:      str			=			torch.tensor([-2.8721, 2.1291]		)
  assert torch.allclose(outputs.logits[0, :3]    ,	UpperCamelCase    ,	atol=1e-4		)
  assert outputs.logits.shape == expected_shape

 Path(UpperCamelCase		).mkdir(exist_ok=UpperCamelCase		)
 print(F"Saving model and processor to {pytorch_dump_folder_path}"		)
 model.save_pretrained(UpperCamelCase		)
 processor.save_pretrained(UpperCamelCase		)


if __name__ == "__main__":
      A:     str								=	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--checkpoint_url",
          default="https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt",
          type=str,
          help="URL of the checkpoint you'd like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
      )

      A:     List[Any]								=	parser.parse_args()
      convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





"""simple docstring"""
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
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_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
      import faiss



@require_faiss
class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			tempfile.mkdtemp()
    UpperCAmelCase			:      str			=			8

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

    UpperCAmelCase			:      int			=			os.path.join(self.tmpdirname   ,  """bart_tokenizer"""		)
    os.makedirs(_SCREAMING_SNAKE_CASE   ,  exist_ok=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Union[str, Any]			=			os.path.join(_SCREAMING_SNAKE_CASE   ,  BART_VOCAB_FILES_NAMES["""vocab_file"""]		)
    UpperCAmelCase			:      Union[str, Any]			=			os.path.join(_SCREAMING_SNAKE_CASE   ,  BART_VOCAB_FILES_NAMES["""merges_file"""]		)
    with open(self.vocab_file   ,  """w"""   ,  encoding="""utf-8"""		) as fp:
     fp.write(json.dumps(_SCREAMING_SNAKE_CASE		) + """\n"""		)
    with open(self.merges_file   ,  """w"""   ,  encoding="""utf-8"""		) as fp:
     fp.write("""\n""".join(_SCREAMING_SNAKE_CASE		)		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   DPRQuestionEncoderTokenizer:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   DPRContextEncoderTokenizer:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   BartTokenizer:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    shutil.rmtree(self.tmpdirname		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			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 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			self.get_dummy_dataset()
    UpperCAmelCase			:      List[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:
     UpperCAmelCase			:      Tuple			=			dataset
     UpperCAmelCase			:      List[str]			=			RagRetriever(
         _SCREAMING_SNAKE_CASE   ,  question_encoder_tokenizer=self.get_dpr_tokenizer()   ,  generator_tokenizer=self.get_bart_tokenizer()   ,  )
    return retriever

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			self.get_dummy_dataset()
    UpperCAmelCase			:      Any			=			RagConfig(
        retrieval_vector_size=self.retrieval_vector_size   ,  question_encoder=DPRConfig().to_dict()   ,  generator=BartConfig().to_dict()   ,  index_name="""custom"""   ,  )
    if from_disk:
     UpperCAmelCase			:      str			=			os.path.join(self.tmpdirname   ,  """dataset"""		)
     UpperCAmelCase			:      Union[str, Any]			=			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			:      List[Any]			=			RagRetriever(
         _SCREAMING_SNAKE_CASE   ,  question_encoder_tokenizer=self.get_dpr_tokenizer()   ,  generator_tokenizer=self.get_bart_tokenizer()   ,  )
    else:
     UpperCAmelCase			:      Optional[Any]			=			RagRetriever(
         _SCREAMING_SNAKE_CASE   ,  question_encoder_tokenizer=self.get_dpr_tokenizer()   ,  generator_tokenizer=self.get_bart_tokenizer()   ,  index=CustomHFIndex(config.retrieval_vector_size   ,  _SCREAMING_SNAKE_CASE		)   ,  )
    return retriever

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			Dataset.from_dict(
        {
            """id""": ["""0""", """1"""],
            """text""": ["""foo""", """bar"""],
            """title""": ["""Foo""", """Bar"""],
            """embeddings""": [np.ones(self.retrieval_vector_size + 1		), 2 * np.ones(self.retrieval_vector_size + 1		)],
        }		)
    dataset.add_faiss_index("""embeddings"""   ,  string_factory="""Flat"""   ,  metric_type=faiss.METRIC_INNER_PRODUCT		)

    UpperCAmelCase			:      Optional[int]			=			os.path.join(self.tmpdirname   ,  """hf_bert_base.hnswSQ8_correct_phi_128.c_index"""		)
    dataset.save_faiss_index("""embeddings"""   ,  index_file_name + """.index.dpr"""		)
    pickle.dump(dataset["""id"""]   ,  open(index_file_name + """.index_meta.dpr"""   ,  """wb"""		)		)

    UpperCAmelCase			:      Dict			=			os.path.join(self.tmpdirname   ,  """psgs_w100.tsv.pkl"""		)
    UpperCAmelCase			:      Any			=			{sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
    pickle.dump(_SCREAMING_SNAKE_CASE   ,  open(_SCREAMING_SNAKE_CASE   ,  """wb"""		)		)

    UpperCAmelCase			:      Dict			=			RagConfig(
        retrieval_vector_size=self.retrieval_vector_size   ,  question_encoder=DPRConfig().to_dict()   ,  generator=BartConfig().to_dict()   ,  index_name="""legacy"""   ,  index_path=self.tmpdirname   ,  )
    UpperCAmelCase			:      str			=			RagRetriever(
        _SCREAMING_SNAKE_CASE   ,  question_encoder_tokenizer=self.get_dpr_tokenizer()   ,  generator_tokenizer=self.get_bart_tokenizer()		)
    return retriever

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			1
    UpperCAmelCase			:      List[Any]			=			self.get_dummy_canonical_hf_index_retriever()
    UpperCAmelCase			:      List[str]			=			np.array(
        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      List[Any]			=			retriever.retrieve(_SCREAMING_SNAKE_CASE   ,  n_docs=_SCREAMING_SNAKE_CASE		)
    self.assertEqual(retrieved_doc_embeds.shape   ,  (2, n_docs, self.retrieval_vector_size)		)
    self.assertEqual(len(_SCREAMING_SNAKE_CASE		)   ,  2		)
    self.assertEqual(sorted(doc_dicts[0]		)   ,  ["""embeddings""", """id""", """text""", """title"""]		)
    self.assertEqual(len(doc_dicts[0]["""id"""]		)   ,  _SCREAMING_SNAKE_CASE		)
    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 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			self.get_dummy_canonical_hf_index_retriever()
    with tempfile.TemporaryDirectory() as tmp_dirname:
     with patch("""transformers.models.rag.retrieval_rag.load_dataset"""		) as mock_load_dataset:
      UpperCAmelCase			:      Optional[int]			=			self.get_dummy_dataset()
      retriever.save_pretrained(_SCREAMING_SNAKE_CASE		)
      UpperCAmelCase			:      Tuple			=			RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE		)
      self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
      UpperCAmelCase			:      List[Any]			=			np.array(
          [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
      UpperCAmelCase			:      Optional[Any]			=			retriever.retrieve(_SCREAMING_SNAKE_CASE   ,  n_docs=1		)
      self.assertTrue(out is not None		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			1
    UpperCAmelCase			:      Dict			=			self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			np.array(
        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      Union[str, Any]			=			retriever.retrieve(_SCREAMING_SNAKE_CASE   ,  n_docs=_SCREAMING_SNAKE_CASE		)
    self.assertEqual(retrieved_doc_embeds.shape   ,  (2, n_docs, self.retrieval_vector_size)		)
    self.assertEqual(len(_SCREAMING_SNAKE_CASE		)   ,  2		)
    self.assertEqual(sorted(doc_dicts[0]		)   ,  ["""embeddings""", """id""", """text""", """title"""]		)
    self.assertEqual(len(doc_dicts[0]["""id"""]		)   ,  _SCREAMING_SNAKE_CASE		)
    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 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE		)
    with tempfile.TemporaryDirectory() as tmp_dirname:
     retriever.save_pretrained(_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      Union[str, Any]			=			RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      Optional[int]			=			np.array(
         [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
     UpperCAmelCase			:      List[str]			=			retriever.retrieve(_SCREAMING_SNAKE_CASE   ,  n_docs=1		)
     self.assertTrue(out is not None		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			1
    UpperCAmelCase			:      Optional[Any]			=			self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			np.array(
        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      str			=			retriever.retrieve(_SCREAMING_SNAKE_CASE   ,  n_docs=_SCREAMING_SNAKE_CASE		)
    self.assertEqual(retrieved_doc_embeds.shape   ,  (2, n_docs, self.retrieval_vector_size)		)
    self.assertEqual(len(_SCREAMING_SNAKE_CASE		)   ,  2		)
    self.assertEqual(sorted(doc_dicts[0]		)   ,  ["""embeddings""", """id""", """text""", """title"""]		)
    self.assertEqual(len(doc_dicts[0]["""id"""]		)   ,  _SCREAMING_SNAKE_CASE		)
    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 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE		)
    with tempfile.TemporaryDirectory() as tmp_dirname:
     retriever.save_pretrained(_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      List[Any]			=			RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      List[str]			=			np.array(
         [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
     UpperCAmelCase			:      int			=			retriever.retrieve(_SCREAMING_SNAKE_CASE   ,  n_docs=1		)
     self.assertTrue(out is not None		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			1
    UpperCAmelCase			:      List[Any]			=			self.get_dummy_legacy_index_retriever()
    UpperCAmelCase			:      Any			=			np.array(
        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      Dict			=			retriever.retrieve(_SCREAMING_SNAKE_CASE   ,  n_docs=_SCREAMING_SNAKE_CASE		)
    self.assertEqual(retrieved_doc_embeds.shape   ,  (2, n_docs, self.retrieval_vector_size)		)
    self.assertEqual(len(_SCREAMING_SNAKE_CASE		)   ,  2		)
    self.assertEqual(sorted(doc_dicts[0]		)   ,  ["""text""", """title"""]		)
    self.assertEqual(len(doc_dicts[0]["""text"""]		)   ,  _SCREAMING_SNAKE_CASE		)
    self.assertEqual(doc_dicts[0]["""text"""][0]   ,  """bar"""		)  # max inner product is reached with second doc
    self.assertEqual(doc_dicts[1]["""text"""][0]   ,  """foo"""		)  # max inner product is reached with first doc
    self.assertListEqual(doc_ids.tolist()   ,  [[1], [0]]		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			self.get_dummy_legacy_index_retriever()
    with tempfile.TemporaryDirectory() as tmp_dirname:
     retriever.save_pretrained(_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      Any			=			RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      Optional[Any]			=			np.array(
         [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
     UpperCAmelCase			:      Tuple			=			retriever.retrieve(_SCREAMING_SNAKE_CASE   ,  n_docs=1		)
     self.assertTrue(out is not None		)

   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    import torch

    UpperCAmelCase			:      Dict			=			1
    UpperCAmelCase			:      List[Any]			=			self.get_dummy_canonical_hf_index_retriever()
    UpperCAmelCase			:      List[str]			=			[[5, 7], [10, 11]]
    UpperCAmelCase			:      List[Any]			=			np.array(
        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
    UpperCAmelCase			:      Union[str, Any]			=			retriever(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  prefix=retriever.config.generator.prefix   ,  n_docs=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      Any			=			(
        out["""context_input_ids"""],
        out["""context_attention_mask"""],
        out["""retrieved_doc_embeds"""],
    )
    self.assertEqual(retrieved_doc_embeds.shape   ,  (2, n_docs, self.retrieval_vector_size)		)
    self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  np.ndarray		)

    UpperCAmelCase			:      Any			=			retriever(
        _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  prefix=retriever.config.generator.prefix   ,  n_docs=_SCREAMING_SNAKE_CASE   ,  return_tensors="""pt"""   ,  )
    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      Dict			=			(  # noqa: F841
        out["""context_input_ids"""],
        out["""context_attention_mask"""],
        out["""retrieved_doc_embeds"""],
        out["""doc_ids"""],
    )
    self.assertEqual(retrieved_doc_embeds.shape   ,  (2, n_docs, self.retrieval_vector_size)		)
    self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  torch.Tensor		)
    self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  torch.Tensor		)
    self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  torch.Tensor		)




   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.get_dpr_ctx_encoder_tokenizer()
    UpperCAmelCase			:      Any			=			1
    UpperCAmelCase			:      List[str]			=			self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE		)
    retriever.set_ctx_encoder_tokenizer(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Dict			=			[[5, 7], [10, 11]]
    UpperCAmelCase			:      Optional[Any]			=			np.array(
        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
    UpperCAmelCase			:      Tuple			=			retriever(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  prefix=retriever.config.generator.prefix   ,  n_docs=_SCREAMING_SNAKE_CASE		)

    self.assertEqual(
        len(_SCREAMING_SNAKE_CASE		)   ,  6		)  # check whether the retriever output consist of 6 attributes including tokenized docs
    self.assertEqual(
        all(k in out for k in ("""tokenized_doc_ids""", """tokenized_doc_attention_mask""")		)   ,  _SCREAMING_SNAKE_CASE		)  # check for doc token related keys in dictionary.
