



from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
  import PIL

# soft dependency
if is_pytesseract_available():
  import pytesseract

a_			=					logging.get_logger(__name__)





def 		a__			(				_UpperCamelCase			: Tuple ,_UpperCamelCase			: Dict ,_UpperCamelCase			: Optional[Any]							):
   return [
       int(10_00 * (box[0] / width)							),
       int(10_00 * (box[1] / height)							),
       int(10_00 * (box[2] / width)							),
       int(10_00 * (box[3] / height)							),
   ]





def 		a__			(				_UpperCamelCase			: np.ndarray ,_UpperCamelCase			: Optional[str] ,_UpperCamelCase			: Optional[str] = None							):
   __lowerCamelCase													=    tesseract_config if tesseract_config is not None else ''''''

   # apply OCR
   __lowerCamelCase													=    to_pil_image(_UpperCamelCase							)
   __lowerCamelCase							,__lowerCamelCase													=    pil_image.size
   __lowerCamelCase													=    pytesseract.image_to_data(_UpperCamelCase ,lang=_UpperCamelCase ,output_type='''dict''' ,config=_UpperCamelCase							)
   __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

   # filter empty words and corresponding coordinates
   __lowerCamelCase													=    [idx for idx, word in enumerate(_UpperCamelCase							) if not word.strip()]
   __lowerCamelCase													=    [word for idx, word in enumerate(_UpperCamelCase							) if idx not in irrelevant_indices]
   __lowerCamelCase													=    [coord for idx, coord in enumerate(_UpperCamelCase							) if idx not in irrelevant_indices]
   __lowerCamelCase													=    [coord for idx, coord in enumerate(_UpperCamelCase							) if idx not in irrelevant_indices]
   __lowerCamelCase													=    [coord for idx, coord in enumerate(_UpperCamelCase							) if idx not in irrelevant_indices]
   __lowerCamelCase													=    [coord for idx, coord in enumerate(_UpperCamelCase							) if idx not in irrelevant_indices]

   # turn coordinates into (left, top, left+width, top+height) format
   __lowerCamelCase													=    []
   for x, y, w, h in zip(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase							):
      __lowerCamelCase													=    [x, y, x + w, y + h]
      actual_boxes.append(_UpperCamelCase							)

   # finally, normalize the bounding boxes
   __lowerCamelCase													=    []
   for box in actual_boxes:
      normalized_boxes.append(normalize_box(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase							)							)

   assert len(_UpperCamelCase							) == len(_UpperCamelCase							), "Not as many words as there are bounding boxes"

   return words, normalized_boxes





class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  ["""pixel_values"""]




 def __init__(						self	,				__UpperCAmelCase = True	,				__UpperCAmelCase = None	,				__UpperCAmelCase = PILImageResampling.BILINEAR	,				__UpperCAmelCase = True	,				__UpperCAmelCase = None	,				__UpperCAmelCase = ""	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    super().__init__(**__UpperCAmelCase				)
    __lowerCamelCase													=    size if size is not None else {'''height''': 224, '''width''': 224}
    __lowerCamelCase													=    get_size_dict(__UpperCAmelCase				)

    __lowerCamelCase													=    do_resize
    __lowerCamelCase													=    size
    __lowerCamelCase													=    resample
    __lowerCamelCase													=    apply_ocr
    __lowerCamelCase													=    ocr_lang
    __lowerCamelCase													=    tesseract_config




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase = PILImageResampling.BILINEAR	,				__UpperCAmelCase = None	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    get_size_dict(__UpperCAmelCase				)
    if "height" not in size or "width" not in size:
       raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""				)
    __lowerCamelCase													=    (size['''height'''], size['''width'''])
    return resize(__UpperCAmelCase	,				size=__UpperCAmelCase	,				resample=__UpperCAmelCase	,				data_format=__UpperCAmelCase	,				**__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = ChannelDimension.FIRST	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    do_resize if do_resize is not None else self.do_resize
    __lowerCamelCase													=    size if size is not None else self.size
    __lowerCamelCase													=    get_size_dict(__UpperCAmelCase				)
    __lowerCamelCase													=    resample if resample is not None else self.resample
    __lowerCamelCase													=    apply_ocr if apply_ocr is not None else self.apply_ocr
    __lowerCamelCase													=    ocr_lang if ocr_lang is not None else self.ocr_lang
    __lowerCamelCase													=    tesseract_config if tesseract_config is not None else self.tesseract_config

    __lowerCamelCase													=    make_list_of_images(__UpperCAmelCase				)

    if not valid_images(__UpperCAmelCase				):
       raise ValueError(
           '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
           '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)

    if do_resize and size is None:
       raise ValueError('''Size must be specified if do_resize is True.'''				)

    # All transformations expect numpy arrays.
    __lowerCamelCase													=    [to_numpy_array(__UpperCAmelCase				) for image in images]

    if apply_ocr:
       requires_backends(self	,				'''pytesseract'''				)
       __lowerCamelCase													=    []
       __lowerCamelCase													=    []
       for image in images:
          __lowerCamelCase							,__lowerCamelCase													=    apply_tesseract(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)
          words_batch.append(__UpperCAmelCase				)
          boxes_batch.append(__UpperCAmelCase				)

    if do_resize:
       __lowerCamelCase													=    [self.resize(image=__UpperCAmelCase	,				size=__UpperCAmelCase	,				resample=__UpperCAmelCase				) for image in images]

    # flip color channels from RGB to BGR (as Detectron2 requires this)
    __lowerCamelCase													=    [flip_channel_order(__UpperCAmelCase				) for image in images]
    __lowerCamelCase													=    [to_channel_dimension_format(__UpperCAmelCase	,				__UpperCAmelCase				) for image in images]

    __lowerCamelCase													=    BatchFeature(data={'''pixel_values''': images}	,				tensor_type=__UpperCAmelCase				)

    if apply_ocr:
       __lowerCamelCase													=    words_batch
       __lowerCamelCase													=    boxes_batch
    return data











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.






