



"""simple docstring"""






import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING






def        snake_case_  (      A_					:		Optional[Any]						):


       '''simple docstring'''





       return 1.0 / (1.0 + np.exp(-_outputs						))






def        snake_case_  (      A_					:		Union[str, Any]						):


       '''simple docstring'''





       _lowerCamelCase						:				Tuple    			= np.max(_outputs,				axis=-1,				keepdims=A_						)
       _lowerCamelCase						:				str    			= np.exp(_outputs - maxes						)
       return shifted_exp / shifted_exp.sum(axis=-1,				keepdims=A_						)






class        __snake_case       (	_lowercase):
  snake_case__				:   Tuple  			=						"sigmoid"
  snake_case__				:   str  			=						"softmax"
  snake_case__				:   Tuple  			=						"none"






@add_end_docstrings(
    _lowercase  ,     R"\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    "  ,     )
class        __snake_case       (	_lowercase):
  snake_case__				:   Dict  			=						False
  snake_case__				:   List[str]  			=						ClassificationFunction.NONE




  def __init__( self     :						List[str]						,					**__lowerCAmelCase     :						Optional[Any]		):






         """simple docstring"""






         super().__init__(**__lowerCAmelCase		)

         self.check_model_type(
             TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
             if self.framework == '''tf'''
             else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]						,					__lowerCAmelCase     :						Dict=None						,					__lowerCAmelCase     :						Union[str, Any]=None						,					__lowerCAmelCase     :						List[str]=""						,					**__lowerCAmelCase     :						Optional[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Any    			= tokenizer_kwargs

         _lowerCamelCase						:				List[Any]    			= {}
         if hasattr(self.model.config						,					'''return_all_scores'''		) and return_all_scores is None:
                _lowerCamelCase						:				str    			= self.model.config.return_all_scores

         if isinstance(__lowerCAmelCase						,					__lowerCAmelCase		) or top_k is None:
                _lowerCamelCase						:				Tuple    			= top_k
                _lowerCamelCase						:				List[str]    			= False
         elif return_all_scores is not None:
                warnings.warn(
                    '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
                    ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'''						,					__lowerCAmelCase						,					)
                if return_all_scores:
                       _lowerCamelCase						:				Dict    			= None
                else:
                       _lowerCamelCase						:				Any    			= 1

         if isinstance(__lowerCAmelCase						,					__lowerCAmelCase		):
                _lowerCamelCase						:				str    			= ClassificationFunction[function_to_apply.upper()]

         if function_to_apply is not None:
                _lowerCamelCase						:				int    			= function_to_apply
         return preprocess_params, {}, postprocess_params




  def __call__( self     :						Tuple						,					*__lowerCAmelCase     :						int						,					**__lowerCAmelCase     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				Union[str, Any]    			= super().__call__(*__lowerCAmelCase						,					**__lowerCAmelCase		)
         # TODO try and retrieve it in a nicer way from _sanitize_parameters.
         _lowerCamelCase						:				Dict    			= '''top_k''' not in kwargs
         if isinstance(args[0]						,					__lowerCAmelCase		) and _legacy:
                # This pipeline is odd, and return a list when single item is run
                return [result]
         else:
                return result




  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple						,					__lowerCAmelCase     :						Optional[int]						,					**__lowerCAmelCase     :						Optional[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[int]    			= self.framework
         if isinstance(__lowerCAmelCase						,					__lowerCAmelCase		):
                return self.tokenizer(**__lowerCAmelCase						,					return_tensors=__lowerCAmelCase						,					**__lowerCAmelCase		)
         elif isinstance(__lowerCAmelCase						,					__lowerCAmelCase		) and len(__lowerCAmelCase		) == 1 and isinstance(inputs[0]						,					__lowerCAmelCase		) and len(inputs[0]		) == 2:
                # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
                return self.tokenizer(
                    text=inputs[0][0]						,					text_pair=inputs[0][1]						,					return_tensors=__lowerCAmelCase						,					**__lowerCAmelCase		)
         elif isinstance(__lowerCAmelCase						,					__lowerCAmelCase		):
                # This is likely an invalid usage of the pipeline attempting to pass text pairs.
                raise ValueError(
                    '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
                    ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'''		)
         return self.tokenizer(__lowerCAmelCase						,					return_tensors=__lowerCAmelCase						,					**__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]						,					__lowerCAmelCase     :						Union[str, Any]		):






         """simple docstring"""






         return self.model(**__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						int						,					__lowerCAmelCase     :						Union[str, Any]						,					__lowerCAmelCase     :						Optional[Any]=None						,					__lowerCAmelCase     :						int=1						,					__lowerCAmelCase     :						Dict=True		):






         """simple docstring"""






         if function_to_apply is None:
                if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                       _lowerCamelCase						:				Optional[int]    			= ClassificationFunction.SIGMOID
                elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                       _lowerCamelCase						:				Optional[int]    			= ClassificationFunction.SOFTMAX
                elif hasattr(self.model.config						,					'''function_to_apply'''		) and function_to_apply is None:
                       _lowerCamelCase						:				List[str]    			= self.model.config.function_to_apply
                else:
                       _lowerCamelCase						:				List[Any]    			= ClassificationFunction.NONE

         _lowerCamelCase						:				Union[str, Any]    			= model_outputs['''logits'''][0]
         _lowerCamelCase						:				Dict    			= outputs.numpy()

         if function_to_apply == ClassificationFunction.SIGMOID:
                _lowerCamelCase						:				Dict    			= sigmoid(__lowerCAmelCase		)
         elif function_to_apply == ClassificationFunction.SOFTMAX:
                _lowerCamelCase						:				Tuple    			= softmax(__lowerCAmelCase		)
         elif function_to_apply == ClassificationFunction.NONE:
                _lowerCamelCase						:				str    			= outputs
         else:
                raise ValueError(f'''Unrecognized `function_to_apply` argument: {function_to_apply}'''		)

         if top_k == 1 and _legacy:
                return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

         _lowerCamelCase						:				Tuple    			= [
             {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(__lowerCAmelCase		)
         ]
         if not _legacy:
                dict_scores.sort(key=lambda __lowerCAmelCase		: x["score"]						,					reverse=__lowerCAmelCase		)
                if top_k is not None:
                       _lowerCamelCase						:				Tuple    			= dict_scores[:top_k]
         return dict_scores









'''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      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  tempfile.mkdtemp()
        __UpperCamelCase       =  8

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

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






 def        _lowerCamelCase					(       self       :							Tuple	):
        return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,	'dpr_tokenizer'	)	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,	'dpr_tokenizer'	)	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname							,	'bart_tokenizer'	)	)






 def        _lowerCamelCase					(       self       :							str	):
        shutil.rmtree(self.tmpdirname	)






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  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       :							Tuple	):
        __UpperCamelCase       =  self.get_dummy_dataset()
        __UpperCamelCase       =  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       =  dataset
               __UpperCamelCase       =  RagRetriever(
                   __A							,	question_encoder_tokenizer=self.get_dpr_tokenizer()							,	generator_tokenizer=self.get_bart_tokenizer()							,	)
        return retriever






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							bool	):
        __UpperCamelCase       =  self.get_dummy_dataset()
        __UpperCamelCase       =  RagConfig(
            retrieval_vector_size=self.retrieval_vector_size							,	question_encoder=DPRConfig().to_dict()							,	generator=BartConfig().to_dict()							,	index_name='custom'							,	)
        if from_disk:
               __UpperCamelCase       =  os.path.join(self.tmpdirname							,	'dataset'	)
               __UpperCamelCase       =  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       =  RagRetriever(
                   __A							,	question_encoder_tokenizer=self.get_dpr_tokenizer()							,	generator_tokenizer=self.get_bart_tokenizer()							,	)
        else:
               __UpperCamelCase       =  RagRetriever(
                   __A							,	question_encoder_tokenizer=self.get_dpr_tokenizer()							,	generator_tokenizer=self.get_bart_tokenizer()							,	index=CustomHFIndex(config.retrieval_vector_size							,	__A	)							,	)
        return retriever






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

        __UpperCamelCase       =  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       =  RagRetriever(
            __A							,	question_encoder_tokenizer=self.get_dpr_tokenizer()							,	generator_tokenizer=self.get_bart_tokenizer()	)
        return retriever






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  1
        __UpperCamelCase       =  self.get_dummy_canonical_hf_index_retriever()
        __UpperCamelCase       =  np.array(
            [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  retriever.retrieve(__A							,	n_docs=__A	)
        self.assertEqual(retrieved_doc_embeds.shape							,	(2, n_docs, self.retrieval_vector_size)	)
        self.assertEqual(len(__A	)							,	2	)
        self.assertEqual(sorted(doc_dicts[0]	)							,	['embeddings', 'id', 'text', 'title']	)
        self.assertEqual(len(doc_dicts[0]['id']	)							,	__A	)
        self.assertEqual(doc_dicts[0]['id'][0]							,	'1'	)  # max inner product is reached with second doc
        self.assertEqual(doc_dicts[1]['id'][0]							,	'0'	)  # max inner product is reached with first doc
        self.assertListEqual(doc_ids.tolist()							,	[[1], [0]]	)






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       =  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       =  self.get_dummy_dataset()
                      retriever.save_pretrained(__A	)
                      __UpperCamelCase       =  RagRetriever.from_pretrained(__A	)
                      self.assertIsInstance(__A							,	__A	)
                      __UpperCamelCase       =  np.array(
                          [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
                      __UpperCamelCase       =  retriever.retrieve(__A							,	n_docs=1	)
                      self.assertTrue(out is not None	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  1
        __UpperCamelCase       =  self.get_dummy_custom_hf_index_retriever(from_disk=__A	)
        __UpperCamelCase       =  np.array(
            [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  retriever.retrieve(__A							,	n_docs=__A	)
        self.assertEqual(retrieved_doc_embeds.shape							,	(2, n_docs, self.retrieval_vector_size)	)
        self.assertEqual(len(__A	)							,	2	)
        self.assertEqual(sorted(doc_dicts[0]	)							,	['embeddings', 'id', 'text', 'title']	)
        self.assertEqual(len(doc_dicts[0]['id']	)							,	__A	)
        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       :							str	):
        __UpperCamelCase       =  self.get_dummy_custom_hf_index_retriever(from_disk=__A	)
        with tempfile.TemporaryDirectory() as tmp_dirname:
               retriever.save_pretrained(__A	)
               __UpperCamelCase       =  RagRetriever.from_pretrained(__A	)
               self.assertIsInstance(__A							,	__A	)
               __UpperCamelCase       =  np.array(
                   [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
               __UpperCamelCase       =  retriever.retrieve(__A							,	n_docs=1	)
               self.assertTrue(out is not None	)






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       =  1
        __UpperCamelCase       =  self.get_dummy_custom_hf_index_retriever(from_disk=__A	)
        __UpperCamelCase       =  np.array(
            [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  retriever.retrieve(__A							,	n_docs=__A	)
        self.assertEqual(retrieved_doc_embeds.shape							,	(2, n_docs, self.retrieval_vector_size)	)
        self.assertEqual(len(__A	)							,	2	)
        self.assertEqual(sorted(doc_dicts[0]	)							,	['embeddings', 'id', 'text', 'title']	)
        self.assertEqual(len(doc_dicts[0]['id']	)							,	__A	)
        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       :							Any	):
        __UpperCamelCase       =  self.get_dummy_custom_hf_index_retriever(from_disk=__A	)
        with tempfile.TemporaryDirectory() as tmp_dirname:
               retriever.save_pretrained(__A	)
               __UpperCamelCase       =  RagRetriever.from_pretrained(__A	)
               self.assertIsInstance(__A							,	__A	)
               __UpperCamelCase       =  np.array(
                   [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
               __UpperCamelCase       =  retriever.retrieve(__A							,	n_docs=1	)
               self.assertTrue(out is not None	)






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  1
        __UpperCamelCase       =  self.get_dummy_legacy_index_retriever()
        __UpperCamelCase       =  np.array(
            [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  retriever.retrieve(__A							,	n_docs=__A	)
        self.assertEqual(retrieved_doc_embeds.shape							,	(2, n_docs, self.retrieval_vector_size)	)
        self.assertEqual(len(__A	)							,	2	)
        self.assertEqual(sorted(doc_dicts[0]	)							,	['text', 'title']	)
        self.assertEqual(len(doc_dicts[0]['text']	)							,	__A	)
        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       :							str	):
        __UpperCamelCase       =  self.get_dummy_legacy_index_retriever()
        with tempfile.TemporaryDirectory() as tmp_dirname:
               retriever.save_pretrained(__A	)
               __UpperCamelCase       =  RagRetriever.from_pretrained(__A	)
               self.assertIsInstance(__A							,	__A	)
               __UpperCamelCase       =  np.array(
                   [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
               __UpperCamelCase       =  retriever.retrieve(__A							,	n_docs=1	)
               self.assertTrue(out is not None	)






 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        import torch

        __UpperCamelCase       =  1
        __UpperCamelCase       =  self.get_dummy_canonical_hf_index_retriever()
        __UpperCamelCase       =  [[5, 7], [1_0, 1_1]]
        __UpperCamelCase       =  np.array(
            [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
        __UpperCamelCase       =  retriever(__A							,	__A							,	prefix=retriever.config.generator.prefix							,	n_docs=__A	)
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  (
            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(__A							,	__A	)
        self.assertIsInstance(__A							,	__A	)
        self.assertIsInstance(__A							,	np.ndarray	)

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






 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  self.get_dpr_ctx_encoder_tokenizer()
        __UpperCamelCase       =  1
        __UpperCamelCase       =  self.get_dummy_custom_hf_index_retriever(from_disk=__A	)
        retriever.set_ctx_encoder_tokenizer(__A	)

        __UpperCamelCase       =  [[5, 7], [1_0, 1_1]]
        __UpperCamelCase       =  np.array(
            [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
        __UpperCamelCase       =  retriever(__A							,	__A							,	prefix=retriever.config.generator.prefix							,	n_docs=__A	)

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