



"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__a      =				logging.get_logger(__name__)

__a      =				{'vocab_file': 'sentencepiece.bpe.model'}

__a      =				{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

__a      =				{
    'camembert-base': 5_12,
}

__a      =				'▁'
class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			int						=     VOCAB_FILES_NAMES
					_A		:			Optional[Any]						=     PRETRAINED_VOCAB_FILES_MAP
					_A		:			Optional[int]						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_A		:			int						=     ['''input_ids''', '''attention_mask''']






					def __init__(       self:							Dict						,     snake_case:							Optional[Any]						,     snake_case:							int="<s>"						,     snake_case:							Tuple="</s>"						,     snake_case:							str="</s>"						,     snake_case:							Any="<s>"						,     snake_case:							List[Any]="<unk>"						,     snake_case:							List[str]="<pad>"						,     snake_case:							Dict="<mask>"						,     snake_case:							Tuple=["<s>NOTUSED", "</s>NOTUSED"]						,     snake_case:							Tuple = None						,     **snake_case:							str						,     )   ->	int:
											# Mask token behave like a normal word, i.e. include the space before it
											snake_case_ :Dict						=			AddedToken(A__						,     lstrip=A__						,     rstrip=A__						) if isinstance(A__						,     A__						) else mask_token

											snake_case_ :Any						=			{} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    bos_token=A__						,     eos_token=A__						,     unk_token=A__						,     sep_token=A__						,     cls_token=A__						,     pad_token=A__						,     mask_token=A__						,     additional_special_tokens=A__						,     sp_model_kwargs=self.sp_model_kwargs						,     **A__						,     )
											snake_case_ :int						=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
											self.sp_model.Load(str(A__						)						)
											snake_case_ :str						=			vocab_file
											# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
											# sentencepiece vocabulary (this is the case for <s> and </s>
											snake_case_ :Tuple						=			{"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
											snake_case_ :Optional[Any]						=			len(self.fairseq_tokens_to_ids						)
											snake_case_ :Tuple						=			len(self.sp_model						) + len(self.fairseq_tokens_to_ids						)
											snake_case_ :List[Any]						=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}






					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							int						,     snake_case:							Optional[Any] = None						)   ->	List[str]:

											if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											snake_case_ :Any						=			[self.cls_token_id]
											snake_case_ :Union[str, Any]						=			[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							int						,     snake_case:							Optional[int] = None						,     snake_case:							Optional[int] = False						)   ->	List[Any]:
											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=A__						,     token_ids_a=A__						,     already_has_special_tokens=A__						)

											if token_ids_a is None:
																	return [1] + ([0] * len(A__						)) + [1]
											return [1] + ([0] * len(A__						)) + [1, 1] + ([0] * len(A__						)) + [1]






					def    lowerCAmelCase_				(       self:							str						,     snake_case:							str						,     snake_case:							Optional[int] = None						)   ->	Any:
											snake_case_ :str						=			[self.sep_token_id]
											snake_case_ :str						=			[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 + sep + token_ids_a + sep						) * [0]






					@property
					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Optional[int]:
											return len(self.fairseq_tokens_to_ids						) + len(self.sp_model						)






					def    lowerCAmelCase_				(       self:							Any						)   ->	int:
											snake_case_ :Optional[int]						=			{self.convert_ids_to_tokens(A__						): i for i in range(self.vocab_size						)}
											vocab.update(self.added_tokens_encoder						)
											return vocab






					def    lowerCAmelCase_				(       self:							Any						,     snake_case:							Dict						)   ->	Optional[Any]:
											return self.sp_model.encode(A__						,     out_type=A__						)






					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							int						)   ->	Tuple:
											if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
											elif self.sp_model.PieceToId(A__						) == 0:
																	# Convert sentence piece unk token to fairseq unk token index
																	return self.unk_token_id
											return self.fairseq_offset + self.sp_model.PieceToId(A__						)






					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							Dict						)   ->	List[str]:
											if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(index - self.fairseq_offset						)






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							Optional[Any]						)   ->	int:
											snake_case_ :Dict						=			[]
											snake_case_ :Optional[Any]						=			""""""
											snake_case_ :Optional[int]						=			False
											for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																							if not prev_is_special:
																													out_string += " "
																							out_string += self.sp_model.decode(A__						) + token
																							snake_case_ :int						=			True
																							snake_case_ :str						=			[]
																	else:
																							current_sub_tokens.append(A__						)
																							snake_case_ :Dict						=			False
											out_string += self.sp_model.decode(A__						)
											return out_string.strip()






					def __getstate__(       self:							Optional[Any]						)   ->	int:
											snake_case_ :List[str]						=			self.__dict__.copy()
											snake_case_ :List[Any]						=			None
											return state






					def __setstate__(       self:							Tuple						,     snake_case:							Optional[int]						)   ->	List[Any]:
											snake_case_ :Union[str, Any]						=			d

											# for backward compatibility
											if not hasattr(self						,     """sp_model_kwargs"""						):
																	snake_case_ :Optional[int]						=			{}

											snake_case_ :List[Any]						=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
											self.sp_model.Load(self.vocab_file						)






					def    lowerCAmelCase_				(       self:							str						,     snake_case:							Optional[Any]						,     snake_case:							Optional[Any] = None						)   ->	str:
											if not os.path.isdir(A__						):
																	logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
																	return
											snake_case_ :Any						=			os.path.join(
											    A__						,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

											if os.path.abspath(self.vocab_file						) != os.path.abspath(A__						) and os.path.isfile(self.vocab_file						):
																	copyfile(self.vocab_file						,     A__						)
											elif not os.path.isfile(self.vocab_file						):
																	with open(A__						,     """wb"""						) as fi:
																							snake_case_ :Optional[int]						=			self.sp_model.serialized_model_proto()
																							fi.write(A__						)

											return (out_vocab_file,)



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_       : Any         =			pytest.mark.integration



@require_faiss
class     _a						(__magic_name__				):


					'''simple docstring'''






					def   __A			( self			):
						A__							:    Tuple        =		Dataset.from_dict({"""filename""": ["""my_name-train""" + """_""" + str(A__			) for x in np.arange(30			).tolist()]}			)
						return dset






					def   __A			( self			):
						import faiss

						A__							:    Dataset        =		self._create_dummy_dataset()
						A__							:    Union[str, Any]        =		dset.map(
						    lambda A__    ,       A__			: {"vecs": i * np.ones(5    ,       dtype=np.floataa			)}    ,       with_indices=A__    ,       keep_in_memory=A__			)
						A__							:    int        =		dset.add_faiss_index("""vecs"""    ,       batch_size=100    ,       metric_type=faiss.METRIC_INNER_PRODUCT			)
						A__   ,   A__							:    Optional[Any]        =		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   __A			( self			):
						import faiss

						A__							:    Dataset        =		self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)			) * np.arange(30			).reshape(-1    ,       1			)    ,       index_name="""vecs"""    ,       batch_size=100    ,       metric_type=faiss.METRIC_INNER_PRODUCT    ,       )
						A__   ,   A__							:    str        =		dset.get_nearest_examples("""vecs"""    ,       np.ones(5    ,       dtype=np.floataa			)			)
						self.assertEqual(examples["""filename"""][0]    ,       """my_name-train_29"""			)






					def   __A			( self			):
						import faiss

						A__							:    Dataset        =		self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)			) * np.arange(30			).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=A__			) as tmp_file:
							dset.save_faiss_index("""vecs"""    ,       tmp_file.name			)
							dset.load_faiss_index("""vecs2"""    ,       tmp_file.name			)
						os.unlink(tmp_file.name			)

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






					def   __A			( self			):
						A__							:    Dataset        =		self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)			) * np.arange(30			).reshape(-1    ,       1			)    ,       index_name="""vecs"""			)
						dset.drop_index("""vecs"""			)
						self.assertRaises(A__    ,       partial(dset.get_nearest_examples    ,       """vecs2"""    ,       np.ones(5    ,       dtype=np.floataa			)			)			)






					def   __A			( self			):
						from elasticsearch import Elasticsearch

						A__							:    Dataset        =		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:
							A__							:    List[Any]        =		{"""acknowledged""": True}
							mocked_bulk.return_value([(True, None)] * 30			)
							A__							:    List[Any]        =		{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 29}]}}
							A__							:    Any        =		Elasticsearch()

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



@require_faiss
class     _a						(__magic_name__				):


					'''simple docstring'''






					def   __A			( self			):
						import faiss

						A__							:    Optional[int]        =		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    ,       10			)

						# single query
						A__							:    Any        =		np.zeros(5    ,       dtype=np.floataa			)
						A__							:    str        =		1
						A__   ,   A__							:    Optional[Any]        =		index.search(A__			)
						self.assertRaises(A__    ,       index.search    ,       query.reshape(-1    ,       1			)			)
						self.assertGreater(scores[0]    ,       0			)
						self.assertEqual(indices[0]    ,       1			)

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






					def   __A			( self			):
						import faiss

						A__							:    Dict        =		FaissIndex(string_factory="""Flat"""			)
						index.add_vectors(np.eye(5    ,       dtype=np.floataa			)			)
						self.assertIsInstance(index.faiss_index    ,       faiss.IndexFlat			)
						A__							:    Dict        =		FaissIndex(string_factory="""LSH"""			)
						index.add_vectors(np.eye(5    ,       dtype=np.floataa			)			)
						self.assertIsInstance(index.faiss_index    ,       faiss.IndexLSH			)
						with self.assertRaises(A__			):
							A__							:    List[Any]        =		FaissIndex(string_factory="""Flat"""    ,       custom_index=faiss.IndexFlat(5			)			)






					def   __A			( self			):
						import faiss

						A__							:    List[Any]        =		faiss.IndexFlat(5			)
						A__							:    Union[str, Any]        =		FaissIndex(custom_index=A__			)
						index.add_vectors(np.eye(5    ,       dtype=np.floataa			)			)
						self.assertIsInstance(index.faiss_index    ,       faiss.IndexFlat			)






					def   __A			( self			):
						import faiss

						A__							:    Any        =		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=A__			) as tmp_file:
							index.save(tmp_file.name			)
							A__							:    int        =		FaissIndex.load(tmp_file.name			)
						os.unlink(tmp_file.name			)

						A__							:    Optional[Any]        =		np.zeros(5    ,       dtype=np.floataa			)
						A__							:    Optional[int]        =		1
						A__   ,   A__							:    List[Any]        =		index.search(A__			)
						self.assertGreater(scores[0]    ,       0			)
						self.assertEqual(indices[0]    ,       1			)



@require_faiss
def 						UpperCamelCase					(lowercase_: Dict					)     ->      Optional[Any]:
	import faiss

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

	A__							:    Optional[Any]        =		"""index.faiss"""
	A__							:    Any        =		f"""mock://{index_name}"""
	index.save(lowercase_    ,      storage_options=mockfs.storage_options					)
	A__							:    str        =		FaissIndex.load(lowercase_    ,      storage_options=mockfs.storage_options					)

	A__							:    int        =		np.zeros(5    ,      dtype=np.floataa					)
	A__							:    Union[str, Any]        =		1
	A__   ,   A__							:    Any        =		index.search(lowercase_					)
	assert scores[0] > 0
	assert indices[0] == 1



@require_elasticsearch
class     _a						(__magic_name__				):


					'''simple docstring'''






					def   __A			( self			):
						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:
							A__							:    List[str]        =		Elasticsearch()
							A__							:    List[Any]        =		{"""acknowledged""": True}
							A__							:    int        =		ElasticSearchIndex(es_client=A__			)
							mocked_bulk.return_value([(True, None)] * 3			)
							index.add_documents(["""foo""", """bar""", """foobar"""]			)

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

							# single query with timeout
							A__							:    List[str]        =		"""foo"""
							A__							:    str        =		{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
							A__   ,   A__							:    Dict        =		index.search(A__    ,       request_timeout=30			)
							self.assertEqual(scores[0]    ,       1			)
							self.assertEqual(indices[0]    ,       0			)

							# batched queries
							A__							:    Union[str, Any]        =		["""foo""", """bar""", """foobar"""]
							A__							:    Tuple        =		{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
							A__   ,   A__							:    Dict        =		index.search_batch(A__			)
							A__							:    Tuple        =		[scores[0] for scores in total_scores]
							A__							:    Optional[int]        =		[indices[0] for indices in total_indices]
							self.assertGreater(np.min(A__			)    ,       0			)
							self.assertListEqual([1, 1, 1]    ,       A__			)

							# batched queries with timeout
							A__							:    int        =		["""foo""", """bar""", """foobar"""]
							A__							:    List[Any]        =		{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
							A__   ,   A__							:    Tuple        =		index.search_batch(A__    ,       request_timeout=30			)
							A__							:    List[Any]        =		[scores[0] for scores in total_scores]
							A__							:    Optional[int]        =		[indices[0] for indices in total_indices]
							self.assertGreater(np.min(A__			)    ,       0			)
							self.assertListEqual([1, 1, 1]    ,       A__			)



