

"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_rembert import RemBertTokenizer
else:
						A_				:      Optional[int]           =			None

A_				:      List[Any]           =			logging.get_logger(__name__)
A_				:      Tuple           =			{"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

A_				:      Any           =			{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

A_				:      Union[str, Any]           =			{
    "google/rembert": 256,
}

A_				:      List[Any]           =			"▁"





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		List[str]       		=				VOCAB_FILES_NAMES
			lowerCamelCase__ :		int       		=				PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__ :		List[str]       		=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__ :		Union[str, Any]       		=				RemBertTokenizer

			def __init__(       self					:    int     ,					__UpperCAmelCase					:    Any=None     ,					__UpperCAmelCase					:    Union[str, Any]=None     ,					__UpperCAmelCase					:    List[str]=True     ,					__UpperCAmelCase					:    int=True     ,					__UpperCAmelCase					:    int=False     ,					__UpperCAmelCase					:    Optional[int]="[CLS]"     ,					__UpperCAmelCase					:    str="[SEP]"     ,					__UpperCAmelCase					:    int="<unk>"     ,					__UpperCAmelCase					:    Optional[int]="[SEP]"     ,					__UpperCAmelCase					:    Any="<pad>"     ,					__UpperCAmelCase					:    Optional[int]="[CLS]"     ,					__UpperCAmelCase					:    Optional[Any]="[MASK]"     ,					**__UpperCAmelCase					:    Union[str, Any]     ,					) ->     Tuple:
						# Mask token behave like a normal word, i.e. include the space before it
						SCREAMING_SNAKE_CASE__										=   AddedToken(__UpperCAmelCase     ,					lstrip=__UpperCAmelCase     ,					rstrip=__UpperCAmelCase							) if isinstance(__UpperCAmelCase     ,					__UpperCAmelCase							) else mask_token

						super().__init__(
						    __UpperCAmelCase     ,					tokenizer_file=__UpperCAmelCase     ,					do_lower_case=__UpperCAmelCase     ,					remove_space=__UpperCAmelCase     ,					keep_accents=__UpperCAmelCase     ,					bos_token=__UpperCAmelCase     ,					eos_token=__UpperCAmelCase     ,					unk_token=__UpperCAmelCase     ,					sep_token=__UpperCAmelCase     ,					pad_token=__UpperCAmelCase     ,					cls_token=__UpperCAmelCase     ,					mask_token=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)

						SCREAMING_SNAKE_CASE__										=   do_lower_case
						SCREAMING_SNAKE_CASE__										=   remove_space
						SCREAMING_SNAKE_CASE__										=   keep_accents
						SCREAMING_SNAKE_CASE__										=   vocab_file
						SCREAMING_SNAKE_CASE__										=   False if not self.vocab_file else True

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]     ,					__UpperCAmelCase					:    List[int]     ,					__UpperCAmelCase					:    Optional[List[int]] = None							) ->     List[int]:
						SCREAMING_SNAKE_CASE__										=   [self.sep_token_id]
						SCREAMING_SNAKE_CASE__										=   [self.cls_token_id]
						if token_ids_a is None:
									return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    List[int]     ,					__UpperCAmelCase					:    Optional[List[int]] = None     ,					__UpperCAmelCase					:    bool = False							) ->     List[int]:

						if already_has_special_tokens:
									if token_ids_a is not None:
												raise ValueError(
												    """You should not supply a second sequence if the provided sequence of """
												    """ids is already formatted with special tokens for the model."""							)
									return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

						if token_ids_a is not None:
									return [1] + ([0] * len(__UpperCAmelCase							)) + [1] + ([0] * len(__UpperCAmelCase							)) + [1]
						return [1] + ([0] * len(__UpperCAmelCase							)) + [1]

			def        SCREAMING_SNAKE_CASE					(       self					:    Any     ,					__UpperCAmelCase					:    List[int]     ,					__UpperCAmelCase					:    Optional[List[int]] = None							) ->     List[int]:
						SCREAMING_SNAKE_CASE__										=   [self.sep_token_id]
						SCREAMING_SNAKE_CASE__										=   [self.cls_token_id]

						if token_ids_a is None:
									return len(cls + token_ids_a + sep							) * [0]
						return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]





			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Optional[str] = None							) ->     Tuple[str]:
						if not os.path.isdir(__UpperCAmelCase							):
									logger.error("""Vocabulary path ({}) should be a directory""".format(__UpperCAmelCase							)							)
									return
						SCREAMING_SNAKE_CASE__										=   os.path.join(
						    __UpperCAmelCase     ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

						if os.path.abspath(self.vocab_file							) != os.path.abspath(__UpperCAmelCase							):
									copyfile(self.vocab_file     ,					__UpperCAmelCase							)

						return (out_vocab_file,)





"""simple docstring"""




import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


A_				:      List[str]           =			pytest.mark.integration





@require_faiss
class  lowerCamelCase    (A__      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   Dataset.from_dict({"""filename""": ["""my_name-train""" + """_""" + str(__UpperCAmelCase							) for x in np.arange(3_0							).tolist()]}							)
						return dset

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Tuple:
						import faiss

						SCREAMING_SNAKE_CASE__										=   self._create_dummy_dataset()
						SCREAMING_SNAKE_CASE__										=   dset.map(
						    lambda __UpperCAmelCase     ,					__UpperCAmelCase							: {"vecs": i * np.ones(5     ,					dtype=np.floataa							)}     ,					with_indices=__UpperCAmelCase     ,					keep_in_memory=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   dset.add_faiss_index("""vecs"""     ,					batch_size=1_0_0     ,					metric_type=faiss.METRIC_INNER_PRODUCT							)
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   dset.get_nearest_examples("""vecs"""     ,					np.ones(5     ,					dtype=np.floataa							)							)
						self.assertEqual(examples["""filename"""][0]     ,					"""my_name-train_29"""							)
						dset.drop_index("""vecs"""							)

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

						SCREAMING_SNAKE_CASE__										=   self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((3_0, 5)							) * np.arange(3_0							).reshape(-1     ,					1							)     ,					index_name="""vecs"""     ,					batch_size=1_0_0     ,					metric_type=faiss.METRIC_INNER_PRODUCT     ,					)
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   dset.get_nearest_examples("""vecs"""     ,					np.ones(5     ,					dtype=np.floataa							)							)
						self.assertEqual(examples["""filename"""][0]     ,					"""my_name-train_29"""							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Any:
						import faiss

						SCREAMING_SNAKE_CASE__										=   self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((3_0, 5)							) * np.arange(3_0							).reshape(-1     ,					1							)     ,					index_name="""vecs"""     ,					metric_type=faiss.METRIC_INNER_PRODUCT     ,					)

						# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
						# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
						# see https://bugs.python.org/issue14243 and
						# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
						with tempfile.NamedTemporaryFile(delete=__UpperCAmelCase							) as tmp_file:
									dset.save_faiss_index("""vecs"""     ,					tmp_file.name							)
									dset.load_faiss_index("""vecs2"""     ,					tmp_file.name							)
						os.unlink(tmp_file.name							)

						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   dset.get_nearest_examples("""vecs2"""     ,					np.ones(5     ,					dtype=np.floataa							)							)
						self.assertEqual(examples["""filename"""][0]     ,					"""my_name-train_29"""							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((3_0, 5)							) * np.arange(3_0							).reshape(-1     ,					1							)     ,					index_name="""vecs"""							)
						dset.drop_index("""vecs"""							)
						self.assertRaises(__UpperCAmelCase     ,					partial(dset.get_nearest_examples     ,					"""vecs2"""     ,					np.ones(5     ,					dtype=np.floataa							)							)							)





			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     str:
						from elasticsearch import Elasticsearch

						SCREAMING_SNAKE_CASE__										=   self._create_dummy_dataset()
						with patch("""elasticsearch.Elasticsearch.search"""							) as mocked_search, patch(
						    """elasticsearch.client.IndicesClient.create"""							) as mocked_index_create, patch("""elasticsearch.helpers.streaming_bulk"""							) as mocked_bulk:
									SCREAMING_SNAKE_CASE__										=   {"""acknowledged""": True}
									mocked_bulk.return_value([(True, None)] * 3_0							)
									SCREAMING_SNAKE_CASE__										=   {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 2_9}]}}
									SCREAMING_SNAKE_CASE__										=   Elasticsearch()

									dset.add_elasticsearch_index("""filename"""     ,					es_client=__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   dset.get_nearest_examples("""filename"""     ,					"""my_name-train_29"""							)
									self.assertEqual(examples["""filename"""][0]     ,					"""my_name-train_29"""							)





@require_faiss
class  lowerCamelCase    (A__      ):

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

						SCREAMING_SNAKE_CASE__										=   FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT							)

						# add vectors
						index.add_vectors(np.eye(5     ,					dtype=np.floataa							)							)
						self.assertIsNotNone(index.faiss_index							)
						self.assertEqual(index.faiss_index.ntotal     ,					5							)
						index.add_vectors(np.zeros((5, 5)     ,					dtype=np.floataa							)							)
						self.assertEqual(index.faiss_index.ntotal     ,					1_0							)

						# single query
						SCREAMING_SNAKE_CASE__										=   np.zeros(5     ,					dtype=np.floataa							)
						SCREAMING_SNAKE_CASE__										=   1
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   index.search(__UpperCAmelCase							)
						self.assertRaises(__UpperCAmelCase     ,					index.search     ,					query.reshape(-1     ,					1							)							)
						self.assertGreater(scores[0]     ,					0							)
						self.assertEqual(indices[0]     ,					1							)

						# batched queries
						SCREAMING_SNAKE_CASE__										=   np.eye(5     ,					dtype=np.floataa							)[::-1]
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   index.search_batch(__UpperCAmelCase							)
						self.assertRaises(__UpperCAmelCase     ,					index.search_batch     ,					queries[0]							)
						SCREAMING_SNAKE_CASE__										=   [scores[0] for scores in total_scores]
						SCREAMING_SNAKE_CASE__										=   [indices[0] for indices in total_indices]
						self.assertGreater(np.min(__UpperCAmelCase							)     ,					0							)
						self.assertListEqual([4, 3, 2, 1, 0]     ,					__UpperCAmelCase							)

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

						SCREAMING_SNAKE_CASE__										=   FaissIndex(string_factory="""Flat"""							)
						index.add_vectors(np.eye(5     ,					dtype=np.floataa							)							)
						self.assertIsInstance(index.faiss_index     ,					faiss.IndexFlat							)
						SCREAMING_SNAKE_CASE__										=   FaissIndex(string_factory="""LSH"""							)
						index.add_vectors(np.eye(5     ,					dtype=np.floataa							)							)
						self.assertIsInstance(index.faiss_index     ,					faiss.IndexLSH							)
						with self.assertRaises(__UpperCAmelCase							):
									SCREAMING_SNAKE_CASE__										=   FaissIndex(string_factory="""Flat"""     ,					custom_index=faiss.IndexFlat(5							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Any:
						import faiss

						SCREAMING_SNAKE_CASE__										=   faiss.IndexFlat(5							)
						SCREAMING_SNAKE_CASE__										=   FaissIndex(custom_index=__UpperCAmelCase							)
						index.add_vectors(np.eye(5     ,					dtype=np.floataa							)							)
						self.assertIsInstance(index.faiss_index     ,					faiss.IndexFlat							)





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

						SCREAMING_SNAKE_CASE__										=   FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT							)
						index.add_vectors(np.eye(5     ,					dtype=np.floataa							)							)

						# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
						# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
						# see https://bugs.python.org/issue14243 and
						# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
						with tempfile.NamedTemporaryFile(delete=__UpperCAmelCase							) as tmp_file:
									index.save(tmp_file.name							)
									SCREAMING_SNAKE_CASE__										=   FaissIndex.load(tmp_file.name							)
						os.unlink(tmp_file.name							)

						SCREAMING_SNAKE_CASE__										=   np.zeros(5     ,					dtype=np.floataa							)
						SCREAMING_SNAKE_CASE__										=   1
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   index.search(__UpperCAmelCase							)
						self.assertGreater(scores[0]     ,					0							)
						self.assertEqual(indices[0]     ,					1							)

@require_faiss
def 						A							(  snake_case__     ):




			'''simple docstring'''


			import faiss

			SCREAMING_SNAKE_CASE__										=   FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT     )
			index.add_vectors(np.eye(5		,					dtype=np.floataa     )     )

			SCREAMING_SNAKE_CASE__										=   """index.faiss"""
			SCREAMING_SNAKE_CASE__										=   f"""mock://{index_name}"""
			index.save(snake_case__		,					storage_options=mockfs.storage_options     )
			SCREAMING_SNAKE_CASE__										=   FaissIndex.load(snake_case__		,					storage_options=mockfs.storage_options     )

			SCREAMING_SNAKE_CASE__										=   np.zeros(5		,					dtype=np.floataa     )
			SCREAMING_SNAKE_CASE__										=   1
			SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   index.search(snake_case__     )
			assert scores[0] > 0
			assert indices[0] == 1





@require_elasticsearch
class  lowerCamelCase    (A__      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Dict:
						from elasticsearch import Elasticsearch

						with patch("""elasticsearch.Elasticsearch.search"""							) as mocked_search, patch(
						    """elasticsearch.client.IndicesClient.create"""							) as mocked_index_create, patch("""elasticsearch.helpers.streaming_bulk"""							) as mocked_bulk:
									SCREAMING_SNAKE_CASE__										=   Elasticsearch()
									SCREAMING_SNAKE_CASE__										=   {"""acknowledged""": True}
									SCREAMING_SNAKE_CASE__										=   ElasticSearchIndex(es_client=__UpperCAmelCase							)
									mocked_bulk.return_value([(True, None)] * 3							)
									index.add_documents(["""foo""", """bar""", """foobar"""]							)

									# single query
									SCREAMING_SNAKE_CASE__										=   """foo"""
									SCREAMING_SNAKE_CASE__										=   {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   index.search(__UpperCAmelCase							)
									self.assertEqual(scores[0]     ,					1							)
									self.assertEqual(indices[0]     ,					0							)

									# single query with timeout
									SCREAMING_SNAKE_CASE__										=   """foo"""
									SCREAMING_SNAKE_CASE__										=   {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   index.search(__UpperCAmelCase     ,					request_timeout=3_0							)
									self.assertEqual(scores[0]     ,					1							)
									self.assertEqual(indices[0]     ,					0							)

									# batched queries
									SCREAMING_SNAKE_CASE__										=   ["""foo""", """bar""", """foobar"""]
									SCREAMING_SNAKE_CASE__										=   {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   index.search_batch(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   [scores[0] for scores in total_scores]
									SCREAMING_SNAKE_CASE__										=   [indices[0] for indices in total_indices]
									self.assertGreater(np.min(__UpperCAmelCase							)     ,					0							)
									self.assertListEqual([1, 1, 1]     ,					__UpperCAmelCase							)

									# batched queries with timeout
									SCREAMING_SNAKE_CASE__										=   ["""foo""", """bar""", """foobar"""]
									SCREAMING_SNAKE_CASE__										=   {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   index.search_batch(__UpperCAmelCase     ,					request_timeout=3_0							)
									SCREAMING_SNAKE_CASE__										=   [scores[0] for scores in total_scores]
									SCREAMING_SNAKE_CASE__										=   [indices[0] for indices in total_indices]
									self.assertGreater(np.min(__UpperCAmelCase							)     ,					0							)
									self.assertListEqual([1, 1, 1]     ,					__UpperCAmelCase							)


