



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

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A:     Tuple										=	logging.get_logger(__name__)

def    _snake_case		(		UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	Tuple=False		):
 UpperCAmelCase			:      Dict			=			[]

 # fmt: off
 # stem:
 rename_keys.append(("""cls_token""", """vit.embeddings.cls_token""")		)
 rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings""")		)

 rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight""")		)
 rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias""")		)

 # backbone
 rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight""")		)
 rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight""")		)
 rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias""")		)

 for stage_idx in range(len(config.backbone_config.depths		)		):
  for layer_idx in range(config.backbone_config.depths[stage_idx]		):
   rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")		)
   rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")		)
   rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")		)
   rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")		)
   rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")		)
   rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")		)
   rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")		)
   rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")		)
   rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")		)

  rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")		)
  rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")		)
  rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")		)

 # transformer encoder
 for i in range(config.num_hidden_layers		):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((F"blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight")		)
  rename_keys.append((F"blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias")		)
  rename_keys.append((F"blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight")		)
  rename_keys.append((F"blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias")		)
  rename_keys.append((F"blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight")		)
  rename_keys.append((F"blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias")		)
  rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight")		)
  rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias")		)
  rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight")		)
  rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias")		)

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ("""norm.weight""", """layernorm.weight"""),
          ("""norm.bias""", """layernorm.bias"""),
          ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
          ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
      ]		)

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  UpperCAmelCase			:      Union[str, Any]			=			[(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""		) else pair for pair in rename_keys]
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ("""norm.weight""", """vit.layernorm.weight"""),
          ("""norm.bias""", """vit.layernorm.bias"""),
          ("""head.weight""", """classifier.weight"""),
          ("""head.bias""", """classifier.bias"""),
      ]		)
 # fmt: on

 return rename_keys

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

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

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

def    _snake_case		(		):
 UpperCAmelCase			:      Union[str, Any]			=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCAmelCase			:      Union[str, Any]			=			Image.open(requests.get(UpperCamelCase    ,	stream=UpperCamelCase		).raw		)
 return im

@torch.no_grad()
def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	Optional[Any]    ,	UpperCamelCase		:	Any=False		):
 UpperCAmelCase			:      str			=			BitConfig(
     global_padding="""same"""    ,	layer_type="""bottleneck"""    ,	depths=(3, 4, 9)    ,	out_features=["""stage3"""]    ,	embedding_dynamic_padding=UpperCamelCase    ,	)
 UpperCAmelCase			:      List[Any]			=			ViTHybridConfig(backbone_config=UpperCamelCase    ,	image_size=384    ,	num_labels=1000		)
 UpperCAmelCase			:      str			=			False

 # load original model from timm
 UpperCAmelCase			:      str			=			timm.create_model(UpperCamelCase    ,	pretrained=UpperCamelCase		)
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 UpperCAmelCase			:      Union[str, Any]			=			timm_model.state_dict()
 if base_model:
  remove_classification_head_(UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			create_rename_keys(UpperCamelCase    ,	UpperCamelCase		)
 for src, dest in rename_keys:
  rename_key(UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase		)
 read_in_q_k_v(UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase		)

 UpperCAmelCase			:      Optional[int]			=			"""huggingface/label-files"""
 UpperCAmelCase			:      Any			=			"""imagenet-1k-id2label.json"""
 UpperCAmelCase			:      List[Any]			=			json.load(open(hf_hub_download(UpperCamelCase    ,	UpperCamelCase    ,	repo_type="""dataset"""		)    ,	"""r"""		)		)
 UpperCAmelCase			:      Dict			=			{int(UpperCamelCase		): v for k, v in idalabel.items()}
 UpperCAmelCase			:      Union[str, Any]			=			idalabel
 UpperCAmelCase			:      Tuple			=			{v: k for k, v in idalabel.items()}

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  UpperCAmelCase			:      List[Any]			=			ViTHybridModel(UpperCamelCase		).eval()
 else:
  UpperCAmelCase			:      Optional[int]			=			ViTHybridForImageClassification(UpperCamelCase		).eval()
 model.load_state_dict(UpperCamelCase		)

 # create image processor
 UpperCAmelCase			:      int			=			create_transform(**resolve_data_config({}    ,	model=UpperCamelCase		)		)
 UpperCAmelCase			:      Union[str, Any]			=			transform.transforms

 UpperCAmelCase			:      Union[str, Any]			=			{
     """bilinear""": PILImageResampling.BILINEAR,
     """bicubic""": PILImageResampling.BICUBIC,
     """nearest""": PILImageResampling.NEAREST,
 }

 UpperCAmelCase			:      List[Any]			=			ViTHybridImageProcessor(
     do_resize=UpperCamelCase    ,	size={"""shortest_edge""": timm_transforms[0].size}    ,	resample=pillow_resamplings[timm_transforms[0].interpolation.value]    ,	do_center_crop=UpperCamelCase    ,	crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}    ,	do_normalize=UpperCamelCase    ,	image_mean=timm_transforms[-1].mean.tolist()    ,	image_std=timm_transforms[-1].std.tolist()    ,	)

 UpperCAmelCase			:      Optional[int]			=			prepare_img()
 UpperCAmelCase			:      Optional[int]			=			transform(UpperCamelCase		).unsqueeze(0		)
 UpperCAmelCase			:      str			=			processor(UpperCamelCase    ,	return_tensors="""pt"""		).pixel_values

 # verify pixel values
 assert torch.allclose(UpperCamelCase    ,	UpperCamelCase		)

 # verify logits
 with torch.no_grad():
  UpperCAmelCase			:      Union[str, Any]			=			model(UpperCamelCase		)
  UpperCAmelCase			:      Optional[Any]			=			outputs.logits

 print("""Predicted class:"""    ,	logits.argmax(-1		).item()		)
 if base_model:
  UpperCAmelCase			:      Dict			=			timm_model.forward_features(UpperCamelCase		)
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(UpperCamelCase    ,	outputs.pooler_output    ,	atol=1e-3		)
 else:
  UpperCAmelCase			:      Union[str, Any]			=			timm_model(UpperCamelCase		)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(UpperCamelCase    ,	outputs.logits    ,	atol=1e-3		)
 print("""Looks ok!"""		)

 if pytorch_dump_folder_path is not None:
  Path(UpperCamelCase		).mkdir(exist_ok=UpperCamelCase		)
  print(F"Saving model {vit_name} to {pytorch_dump_folder_path}"		)
  model.save_pretrained(UpperCamelCase		)
  print(F"Saving processor to {pytorch_dump_folder_path}"		)
  processor.save_pretrained(UpperCamelCase		)

 if push_to_hub:
  print(F"Pushing model and processor to the hub {vit_name}"		)
  model.push_to_hub(F"ybelkada/{vit_name}"		)
  processor.push_to_hub(F"ybelkada/{vit_name}"		)


if __name__ == "__main__":
      A:     Optional[Any]										=	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--vit_name",
          default="vit_base_r50_s16_384",
          type=str,
          help="Name of the hybrid ViT timm model you'd like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
      )
      parser.add_argument(
          "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
      )

      A:     Optional[int]										=	parser.parse_args()
      convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""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.
