



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 transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification





def 		a__			(				_UpperCamelCase			: Optional[int]							):
   __lowerCamelCase													=    SwinvaConfig()
   __lowerCamelCase													=    swinva_name.split('''_'''							)

   __lowerCamelCase													=    name_split[1]
   if "to" in name_split[3]:
      __lowerCamelCase													=    int(name_split[3][-3:]							)
   else:
      __lowerCamelCase													=    int(name_split[3]							)
   if "to" in name_split[2]:
      __lowerCamelCase													=    int(name_split[2][-2:]							)
   else:
      __lowerCamelCase													=    int(name_split[2][6:]							)

   if model_size == "tiny":
      __lowerCamelCase													=    96
      __lowerCamelCase													=    (2, 2, 6, 2)
      __lowerCamelCase													=    (3, 6, 12, 24)
   elif model_size == "small":
      __lowerCamelCase													=    96
      __lowerCamelCase													=    (2, 2, 18, 2)
      __lowerCamelCase													=    (3, 6, 12, 24)
   elif model_size == "base":
      __lowerCamelCase													=    1_28
      __lowerCamelCase													=    (2, 2, 18, 2)
      __lowerCamelCase													=    (4, 8, 16, 32)
   else:
      __lowerCamelCase													=    1_92
      __lowerCamelCase													=    (2, 2, 18, 2)
      __lowerCamelCase													=    (6, 12, 24, 48)

   if "to" in swinva_name:
      __lowerCamelCase													=    (12, 12, 12, 6)

   if ("22k" in swinva_name) and ("to" not in swinva_name):
      __lowerCamelCase													=    2_18_41
      __lowerCamelCase													=    '''huggingface/label-files'''
      __lowerCamelCase													=    '''imagenet-22k-id2label.json'''
      __lowerCamelCase													=    json.load(open(hf_hub_download(_UpperCamelCase ,_UpperCamelCase ,repo_type='''dataset'''							) ,'''r'''							)							)
      __lowerCamelCase													=    {int(_UpperCamelCase							): v for k, v in idalabel.items()}
      __lowerCamelCase													=    idalabel
      __lowerCamelCase													=    {v: k for k, v in idalabel.items()}

   else:
      __lowerCamelCase													=    10_00
      __lowerCamelCase													=    '''huggingface/label-files'''
      __lowerCamelCase													=    '''imagenet-1k-id2label.json'''
      __lowerCamelCase													=    json.load(open(hf_hub_download(_UpperCamelCase ,_UpperCamelCase ,repo_type='''dataset'''							) ,'''r'''							)							)
      __lowerCamelCase													=    {int(_UpperCamelCase							): v for k, v in idalabel.items()}
      __lowerCamelCase													=    idalabel
      __lowerCamelCase													=    {v: k for k, v in idalabel.items()}

   __lowerCamelCase													=    img_size
   __lowerCamelCase													=    num_classes
   __lowerCamelCase													=    embed_dim
   __lowerCamelCase													=    depths
   __lowerCamelCase													=    num_heads
   __lowerCamelCase													=    window_size

   return config





def 		a__			(				_UpperCamelCase			: Tuple							):
   if "patch_embed.proj" in name:
      __lowerCamelCase													=    name.replace('''patch_embed.proj''' ,'''embeddings.patch_embeddings.projection'''							)
   if "patch_embed.norm" in name:
      __lowerCamelCase													=    name.replace('''patch_embed.norm''' ,'''embeddings.norm'''							)
   if "layers" in name:
      __lowerCamelCase													=    '''encoder.''' + name
   if "attn.proj" in name:
      __lowerCamelCase													=    name.replace('''attn.proj''' ,'''attention.output.dense'''							)
   if "attn" in name:
      __lowerCamelCase													=    name.replace('''attn''' ,'''attention.self'''							)
   if "norm1" in name:
      __lowerCamelCase													=    name.replace('''norm1''' ,'''layernorm_before'''							)
   if "norm2" in name:
      __lowerCamelCase													=    name.replace('''norm2''' ,'''layernorm_after'''							)
   if "mlp.fc1" in name:
      __lowerCamelCase													=    name.replace('''mlp.fc1''' ,'''intermediate.dense'''							)
   if "mlp.fc2" in name:
      __lowerCamelCase													=    name.replace('''mlp.fc2''' ,'''output.dense'''							)
   if "q_bias" in name:
      __lowerCamelCase													=    name.replace('''q_bias''' ,'''query.bias'''							)
   if "k_bias" in name:
      __lowerCamelCase													=    name.replace('''k_bias''' ,'''key.bias'''							)
   if "v_bias" in name:
      __lowerCamelCase													=    name.replace('''v_bias''' ,'''value.bias'''							)
   if "cpb_mlp" in name:
      __lowerCamelCase													=    name.replace('''cpb_mlp''' ,'''continuous_position_bias_mlp'''							)
   if name == "norm.weight":
      __lowerCamelCase													=    '''layernorm.weight'''
   if name == "norm.bias":
      __lowerCamelCase													=    '''layernorm.bias'''

   if "head" in name:
      __lowerCamelCase													=    name.replace('''head''' ,'''classifier'''							)
   else:
      __lowerCamelCase													=    '''swinv2.''' + name

   return name





def 		a__			(				_UpperCamelCase			: Optional[Any] ,_UpperCamelCase			: Dict							):
   for key in orig_state_dict.copy().keys():
      __lowerCamelCase													=    orig_state_dict.pop(_UpperCamelCase							)

      if "mask" in key:
         continue
      elif "qkv" in key:
         __lowerCamelCase													=    key.split('''.'''							)
         __lowerCamelCase													=    int(key_split[1]							)
         __lowerCamelCase													=    int(key_split[3]							)
         __lowerCamelCase													=    model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            __lowerCamelCase													=    val[:dim, :]
            __lowerCamelCase													=    val[dim : dim * 2, :]
            __lowerCamelCase													=    val[-dim:, :]
         else:
            __lowerCamelCase													=    val[:dim]
            __lowerCamelCase													=    val[
                dim : dim * 2
            ]
            __lowerCamelCase													=    val[-dim:]
      else:
         __lowerCamelCase													=    val

   return orig_state_dict





def 		a__			(				_UpperCamelCase			: Tuple ,_UpperCamelCase			: Optional[Any]							):
   __lowerCamelCase													=    timm.create_model(_UpperCamelCase ,pretrained=_UpperCamelCase							)
   timm_model.eval()

   __lowerCamelCase													=    get_swinva_config(_UpperCamelCase							)
   __lowerCamelCase													=    SwinvaForImageClassification(_UpperCamelCase							)
   model.eval()

   __lowerCamelCase													=    convert_state_dict(timm_model.state_dict() ,_UpperCamelCase							)
   model.load_state_dict(_UpperCamelCase							)

   __lowerCamelCase													=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''

   __lowerCamelCase													=    AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_''' ,'''-'''							)							)							)
   __lowerCamelCase													=    Image.open(requests.get(_UpperCamelCase ,stream=_UpperCamelCase							).raw							)
   __lowerCamelCase													=    image_processor(images=_UpperCamelCase ,return_tensors='''pt'''							)

   __lowerCamelCase													=    timm_model(inputs['''pixel_values''']							)
   __lowerCamelCase													=    model(**_UpperCamelCase							).logits

   assert torch.allclose(_UpperCamelCase ,_UpperCamelCase ,atol=1e-3							)

   print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""							)
   model.save_pretrained(_UpperCamelCase							)

   print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
   image_processor.save_pretrained(_UpperCamelCase							)

   model.push_to_hub(
       repo_path_or_name=Path(_UpperCamelCase ,_UpperCamelCase							) ,organization='''nandwalritik''' ,commit_message='''Add model''' ,)


if __name__ == "__main__":
  a_			=					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--swinv2_name""",
      default="""swinv2_tiny_patch4_window8_256""",
      type=str,
      help="""Name of the Swinv2 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."""
  )

  a_			=					parser.parse_args()
  convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)











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  __lowerCAmelCase     (	lowerCAmelCase__   ):




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    tempfile.mkdtemp()
    __lowerCamelCase													=    8

    # DPR tok
    __lowerCamelCase													=    [
        '''[UNK]''',
        '''[CLS]''',
        '''[SEP]''',
        '''[PAD]''',
        '''[MASK]''',
        '''want''',
        '''##want''',
        '''##ed''',
        '''wa''',
        '''un''',
        '''runn''',
        '''##ing''',
        ''',''',
        '''low''',
        '''lowest''',
    ]
    __lowerCamelCase													=    os.path.join(self.tmpdirname	,				'''dpr_tokenizer'''				)
    os.makedirs(__UpperCAmelCase	,				exist_ok=__UpperCAmelCase				)
    __lowerCamelCase													=    os.path.join(__UpperCAmelCase	,				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
    __lowerCamelCase													=    [
        '''l''',
        '''o''',
        '''w''',
        '''e''',
        '''r''',
        '''s''',
        '''t''',
        '''i''',
        '''d''',
        '''n''',
        '''\u0120''',
        '''\u0120l''',
        '''\u0120n''',
        '''\u0120lo''',
        '''\u0120low''',
        '''er''',
        '''\u0120lowest''',
        '''\u0120newer''',
        '''\u0120wider''',
        '''<unk>''',
    ]
    __lowerCamelCase													=    dict(zip(__UpperCAmelCase	,				range(len(__UpperCAmelCase				)				)				)				)
    __lowerCamelCase													=    ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
    __lowerCamelCase													=    {'''unk_token''': '''<unk>'''}

    __lowerCamelCase													=    os.path.join(self.tmpdirname	,				'''bart_tokenizer'''				)
    os.makedirs(__UpperCAmelCase	,				exist_ok=__UpperCAmelCase				)
    __lowerCamelCase													=    os.path.join(__UpperCAmelCase	,				BART_VOCAB_FILES_NAMES['''vocab_file''']				)
    __lowerCamelCase													=    os.path.join(__UpperCAmelCase	,				BART_VOCAB_FILES_NAMES['''merges_file''']				)
    with open(self.vocab_file	,				'''w'''	,				encoding='''utf-8'''				) as fp:
       fp.write(json.dumps(__UpperCAmelCase				) + '''\n'''				)
    with open(self.merges_file	,				'''w'''	,				encoding='''utf-8'''				) as fp:
       fp.write('''\n'''.join(__UpperCAmelCase				)				)




 def        lowerCamelCase      (						self				):



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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,				'''dpr_tokenizer'''				)				)




 def        lowerCamelCase      (						self				):



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




 def        lowerCamelCase      (						self				):



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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    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				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dummy_dataset()
    __lowerCamelCase													=    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:
       __lowerCamelCase													=    dataset
       __lowerCamelCase													=    RagRetriever(
           __UpperCAmelCase	,				question_encoder_tokenizer=self.get_dpr_tokenizer()	,				generator_tokenizer=self.get_bart_tokenizer()	,				)
    return retriever




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dummy_dataset()
    __lowerCamelCase													=    RagConfig(
        retrieval_vector_size=self.retrieval_vector_size	,				question_encoder=DPRConfig().to_dict()	,				generator=BartConfig().to_dict()	,				index_name='''custom'''	,				)
    if from_disk:
       __lowerCamelCase													=    os.path.join(self.tmpdirname	,				'''dataset'''				)
       __lowerCamelCase													=    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
       __lowerCamelCase													=    RagRetriever(
           __UpperCAmelCase	,				question_encoder_tokenizer=self.get_dpr_tokenizer()	,				generator_tokenizer=self.get_bart_tokenizer()	,				)
    else:
       __lowerCamelCase													=    RagRetriever(
           __UpperCAmelCase	,				question_encoder_tokenizer=self.get_dpr_tokenizer()	,				generator_tokenizer=self.get_bart_tokenizer()	,				index=CustomHFIndex(config.retrieval_vector_size	,				__UpperCAmelCase				)	,				)
    return retriever




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    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				)

    __lowerCamelCase													=    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'''				)				)

    __lowerCamelCase													=    os.path.join(self.tmpdirname	,				'''psgs_w100.tsv.pkl'''				)
    __lowerCamelCase													=    {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
    pickle.dump(__UpperCAmelCase	,				open(__UpperCAmelCase	,				'''wb'''				)				)

    __lowerCamelCase													=    RagConfig(
        retrieval_vector_size=self.retrieval_vector_size	,				question_encoder=DPRConfig().to_dict()	,				generator=BartConfig().to_dict()	,				index_name='''legacy'''	,				index_path=self.tmpdirname	,				)
    __lowerCamelCase													=    RagRetriever(
        __UpperCAmelCase	,				question_encoder_tokenizer=self.get_dpr_tokenizer()	,				generator_tokenizer=self.get_bart_tokenizer()				)
    return retriever




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_canonical_hf_index_retriever()
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    retriever.retrieve(__UpperCAmelCase	,				n_docs=__UpperCAmelCase				)
    self.assertEqual(retrieved_doc_embeds.shape	,				(2, n_docs, self.retrieval_vector_size)				)
    self.assertEqual(len(__UpperCAmelCase				)	,				2				)
    self.assertEqual(sorted(doc_dicts[0]				)	,				['''embeddings''', '''id''', '''text''', '''title''']				)
    self.assertEqual(len(doc_dicts[0]['''id''']				)	,				__UpperCAmelCase				)
    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				):



    '''simple docstring'''
    __lowerCamelCase													=    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:
          __lowerCamelCase													=    self.get_dummy_dataset()
          retriever.save_pretrained(__UpperCAmelCase				)
          __lowerCamelCase													=    RagRetriever.from_pretrained(__UpperCAmelCase				)
          self.assertIsInstance(__UpperCAmelCase	,				__UpperCAmelCase				)
          __lowerCamelCase													=    np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
          __lowerCamelCase													=    retriever.retrieve(__UpperCAmelCase	,				n_docs=1				)
          self.assertTrue(out is not None				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase				)
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    retriever.retrieve(__UpperCAmelCase	,				n_docs=__UpperCAmelCase				)
    self.assertEqual(retrieved_doc_embeds.shape	,				(2, n_docs, self.retrieval_vector_size)				)
    self.assertEqual(len(__UpperCAmelCase				)	,				2				)
    self.assertEqual(sorted(doc_dicts[0]				)	,				['''embeddings''', '''id''', '''text''', '''title''']				)
    self.assertEqual(len(doc_dicts[0]['''id''']				)	,				__UpperCAmelCase				)
    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				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase				)
    with tempfile.TemporaryDirectory() as tmp_dirname:
       retriever.save_pretrained(__UpperCAmelCase				)
       __lowerCamelCase													=    RagRetriever.from_pretrained(__UpperCAmelCase				)
       self.assertIsInstance(__UpperCAmelCase	,				__UpperCAmelCase				)
       __lowerCamelCase													=    np.array(
           [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
       __lowerCamelCase													=    retriever.retrieve(__UpperCAmelCase	,				n_docs=1				)
       self.assertTrue(out is not None				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase				)
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    retriever.retrieve(__UpperCAmelCase	,				n_docs=__UpperCAmelCase				)
    self.assertEqual(retrieved_doc_embeds.shape	,				(2, n_docs, self.retrieval_vector_size)				)
    self.assertEqual(len(__UpperCAmelCase				)	,				2				)
    self.assertEqual(sorted(doc_dicts[0]				)	,				['''embeddings''', '''id''', '''text''', '''title''']				)
    self.assertEqual(len(doc_dicts[0]['''id''']				)	,				__UpperCAmelCase				)
    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				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase				)
    with tempfile.TemporaryDirectory() as tmp_dirname:
       retriever.save_pretrained(__UpperCAmelCase				)
       __lowerCamelCase													=    RagRetriever.from_pretrained(__UpperCAmelCase				)
       self.assertIsInstance(__UpperCAmelCase	,				__UpperCAmelCase				)
       __lowerCamelCase													=    np.array(
           [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
       __lowerCamelCase													=    retriever.retrieve(__UpperCAmelCase	,				n_docs=1				)
       self.assertTrue(out is not None				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_legacy_index_retriever()
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    retriever.retrieve(__UpperCAmelCase	,				n_docs=__UpperCAmelCase				)
    self.assertEqual(retrieved_doc_embeds.shape	,				(2, n_docs, self.retrieval_vector_size)				)
    self.assertEqual(len(__UpperCAmelCase				)	,				2				)
    self.assertEqual(sorted(doc_dicts[0]				)	,				['''text''', '''title''']				)
    self.assertEqual(len(doc_dicts[0]['''text''']				)	,				__UpperCAmelCase				)
    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        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dummy_legacy_index_retriever()
    with tempfile.TemporaryDirectory() as tmp_dirname:
       retriever.save_pretrained(__UpperCAmelCase				)
       __lowerCamelCase													=    RagRetriever.from_pretrained(__UpperCAmelCase				)
       self.assertIsInstance(__UpperCAmelCase	,				__UpperCAmelCase				)
       __lowerCamelCase													=    np.array(
           [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
       __lowerCamelCase													=    retriever.retrieve(__UpperCAmelCase	,				n_docs=1				)
       self.assertTrue(out is not None				)




 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    import torch

    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_canonical_hf_index_retriever()
    __lowerCamelCase													=    [[5, 7], [10, 11]]
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase													=    retriever(__UpperCAmelCase	,				__UpperCAmelCase	,				prefix=retriever.config.generator.prefix	,				n_docs=__UpperCAmelCase				)
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    (
        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(__UpperCAmelCase	,				__UpperCAmelCase				)
    self.assertIsInstance(__UpperCAmelCase	,				__UpperCAmelCase				)
    self.assertIsInstance(__UpperCAmelCase	,				np.ndarray				)

    __lowerCamelCase													=    retriever(
        __UpperCAmelCase	,				__UpperCAmelCase	,				prefix=retriever.config.generator.prefix	,				n_docs=__UpperCAmelCase	,				return_tensors='''pt'''	,				)
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    (  # 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(__UpperCAmelCase	,				torch.Tensor				)
    self.assertIsInstance(__UpperCAmelCase	,				torch.Tensor				)
    self.assertIsInstance(__UpperCAmelCase	,				torch.Tensor				)




 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dpr_ctx_encoder_tokenizer()
    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase				)
    retriever.set_ctx_encoder_tokenizer(__UpperCAmelCase				)

    __lowerCamelCase													=    [[5, 7], [10, 11]]
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase													=    retriever(__UpperCAmelCase	,				__UpperCAmelCase	,				prefix=retriever.config.generator.prefix	,				n_docs=__UpperCAmelCase				)

    self.assertEqual(
        len(__UpperCAmelCase				)	,				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''')				)	,				__UpperCAmelCase				)  # check for doc token related keys in dictionary.






