
"""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_1_2,
}

_A						=			'▁'






class 	_lowercase       (    A__		):
 lowercase_				=     VOCAB_FILES_NAMES
 lowercase_				=     PRETRAINED_VOCAB_FILES_MAP
 lowercase_				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_				=     ['input_ids', 'attention_mask']




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

   lowerCamelCase					:      str          =   {} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       bos_token=__snake_case		,			eos_token=__snake_case		,			unk_token=__snake_case		,			sep_token=__snake_case		,			cls_token=__snake_case		,			pad_token=__snake_case		,			mask_token=__snake_case		,			additional_special_tokens=__snake_case		,			sp_model_kwargs=self.sp_model_kwargs		,			**__snake_case		,			)
   lowerCamelCase					:      int          =   spm.SentencePieceProcessor(**self.sp_model_kwargs )
   self.sp_model.Load(str(__snake_case ) )
   lowerCamelCase					:      Dict          =   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>
   lowerCamelCase					:      Dict          =   {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
   lowerCamelCase					:      Dict          =   len(self.fairseq_tokens_to_ids )
   lowerCamelCase					:      Optional[Any]          =   len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
   lowerCamelCase					:      Dict          =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							List[int]:

   if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   lowerCamelCase					:      Any          =   [self.cls_token_id]
   lowerCamelCase					:      Optional[int]          =   [self.sep_token_id]
   return cls + token_ids_a + sep + sep + token_ids_a + sep




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = False )		->							List[int]:
   if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=__snake_case		,			token_ids_a=__snake_case		,			already_has_special_tokens=__snake_case )

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




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							List[int]:
   lowerCamelCase					:      Union[str, Any]          =   [self.sep_token_id]
   lowerCamelCase					:      Tuple          =   [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       _UpperCamelCase   (			self )		->							List[str]:
   return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )




 def       _UpperCamelCase   (			self )		->							Dict:
   lowerCamelCase					:      List[str]          =   {self.convert_ids_to_tokens(__snake_case ): i for i in range(self.vocab_size )}
   vocab.update(self.added_tokens_encoder )
   return vocab




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							List[str]:
   return self.sp_model.encode(__snake_case		,			out_type=__snake_case )




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							List[str]:
   if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
   elif self.sp_model.PieceToId(__snake_case ) == 0:
     # Convert sentence piece unk token to fairseq unk token index
     return self.unk_token_id
   return self.fairseq_offset + self.sp_model.PieceToId(__snake_case )




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Optional[int]:
   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       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							str:
   lowerCamelCase					:      List[Any]          =   []
   lowerCamelCase					:      Union[str, Any]          =   ''
   lowerCamelCase					:      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(__snake_case ) + token
       lowerCamelCase					:      Dict          =   True
       lowerCamelCase					:      str          =   []
     else:
       current_sub_tokens.append(__snake_case )
       lowerCamelCase					:      Any          =   False
   out_string += self.sp_model.decode(__snake_case )
   return out_string.strip()




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




 def __setstate__(			self		,			UpperCAmelCase_ )		->							List[Any]:
   lowerCamelCase					:      Dict          =   d

   # for backward compatibility
   if not hasattr(self		,			'sp_model_kwargs' ):
     lowerCamelCase					:      str          =   {}

   lowerCamelCase					:      Any          =   spm.SentencePieceProcessor(**self.sp_model_kwargs )
   self.sp_model.Load(self.vocab_file )




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							Tuple[str]:
   if not os.path.isdir(__snake_case ):
     logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
     return
   lowerCamelCase					:      int          =   os.path.join(
       __snake_case		,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

   if os.path.abspath(self.vocab_file ) != os.path.abspath(__snake_case ) and os.path.isfile(self.vocab_file ):
     copyfile(self.vocab_file		,			__snake_case )
   elif not os.path.isfile(self.vocab_file ):
     with open(__snake_case		,			'wb' ) as fi:
       lowerCamelCase					:      Dict          =   self.sp_model.serialized_model_proto()
       fi.write(__snake_case )

   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						=			pytest.mark.integration






@require_faiss
class 	_lowercase       (    __UpperCAmelCase		):




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Any          =   Dataset.from_dict({'filename': ['my_name-train' + '_' + str(UpperCAmelCase_ ) for x in np.arange(30 ).tolist()]} )
			return dset




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

			lowerCamelCase					:      Dataset          =   self._create_dummy_dataset()
			lowerCamelCase					:      Optional[int]          =   dset.map(
			    lambda UpperCAmelCase_		,			UpperCAmelCase_ : {"vecs": i * np.ones(5		,			dtype=np.floataa )}		,			with_indices=UpperCAmelCase_		,			keep_in_memory=UpperCAmelCase_ )
			lowerCamelCase					:      Dict          =   dset.add_faiss_index('vecs'		,			batch_size=100		,			metric_type=faiss.METRIC_INNER_PRODUCT )
			lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   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       _UpperCamelCase   (			self )		->							Tuple:
			import faiss

			lowerCamelCase					:      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		,			)
			lowerCamelCase     ,   lowerCamelCase					:      str          =   dset.get_nearest_examples('vecs'		,			np.ones(5		,			dtype=np.floataa ) )
			self.assertEqual(examples['filename'][0]		,			'my_name-train_29' )




	def       _UpperCamelCase   (			self )		->							int:
			import faiss

			lowerCamelCase					:      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=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 )

			lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   dset.get_nearest_examples('vecs2'		,			np.ones(5		,			dtype=np.floataa ) )
			self.assertEqual(examples['filename'][0]		,			'my_name-train_29' )




	def       _UpperCamelCase   (			self )		->							Any:
			lowerCamelCase					:      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(UpperCAmelCase_		,			partial(dset.get_nearest_examples		,			'vecs2'		,			np.ones(5		,			dtype=np.floataa ) ) )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			from elasticsearch import Elasticsearch

			lowerCamelCase					:      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:
					lowerCamelCase					:      Tuple          =   {'acknowledged': True}
					mocked_bulk.return_value([(True, None)] * 30 )
					lowerCamelCase					:      int          =   {'hits': {'hits': [{'_score': 1, '_id': 29}]}}
					lowerCamelCase					:      Optional[Any]          =   Elasticsearch()

					dset.add_elasticsearch_index('filename'		,			es_client=UpperCAmelCase_ )
					lowerCamelCase     ,   lowerCamelCase					:      str          =   dset.get_nearest_examples('filename'		,			'my_name-train_29' )
					self.assertEqual(examples['filename'][0]		,			'my_name-train_29' )






@require_faiss
class 	_lowercase       (    __UpperCAmelCase		):




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

			lowerCamelCase					:      str          =   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
			lowerCamelCase					:      Optional[int]          =   np.zeros(5		,			dtype=np.floataa )
			lowerCamelCase					:      List[str]          =   1
			lowerCamelCase     ,   lowerCamelCase					:      int          =   index.search(UpperCAmelCase_ )
			self.assertRaises(UpperCAmelCase_		,			index.search		,			query.reshape(-1		,			1 ) )
			self.assertGreater(scores[0]		,			0 )
			self.assertEqual(indices[0]		,			1 )

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




	def       _UpperCamelCase   (			self )		->							Dict:
			import faiss

			lowerCamelCase					:      List[Any]          =   FaissIndex(string_factory='Flat' )
			index.add_vectors(np.eye(5		,			dtype=np.floataa ) )
			self.assertIsInstance(index.faiss_index		,			faiss.IndexFlat )
			lowerCamelCase					:      int          =   FaissIndex(string_factory='LSH' )
			index.add_vectors(np.eye(5		,			dtype=np.floataa ) )
			self.assertIsInstance(index.faiss_index		,			faiss.IndexLSH )
			with self.assertRaises(UpperCAmelCase_ ):
					lowerCamelCase					:      str          =   FaissIndex(string_factory='Flat'		,			custom_index=faiss.IndexFlat(5 ) )




	def       _UpperCamelCase   (			self )		->							Any:
			import faiss

			lowerCamelCase					:      Any          =   faiss.IndexFlat(5 )
			lowerCamelCase					:      Any          =   FaissIndex(custom_index=UpperCAmelCase_ )
			index.add_vectors(np.eye(5		,			dtype=np.floataa ) )
			self.assertIsInstance(index.faiss_index		,			faiss.IndexFlat )




	def       _UpperCamelCase   (			self )		->							Any:
			import faiss

			lowerCamelCase					:      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=UpperCAmelCase_ ) as tmp_file:
					index.save(tmp_file.name )
					lowerCamelCase					:      List[str]          =   FaissIndex.load(tmp_file.name )
			os.unlink(tmp_file.name )

			lowerCamelCase					:      Dict          =   np.zeros(5		,			dtype=np.floataa )
			lowerCamelCase					:      Optional[Any]          =   1
			lowerCamelCase     ,   lowerCamelCase					:      str          =   index.search(UpperCAmelCase_ )
			self.assertGreater(scores[0]		,			0 )
			self.assertEqual(indices[0]		,			1 )


@require_faiss
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		import faiss

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

		lowerCamelCase					:      Union[str, Any]          =   'index.faiss'
		lowerCamelCase					:      List[Any]          =   F"""mock://{index_name}"""
		index.save(a_,						storage_options=mockfs.storage_options      )
		lowerCamelCase					:      Optional[int]          =   FaissIndex.load(a_,						storage_options=mockfs.storage_options      )

		lowerCamelCase					:      str          =   np.zeros(5,						dtype=np.floataa      )
		lowerCamelCase					:      str          =   1
		lowerCamelCase     ,   lowerCamelCase					:      int          =   index.search(a_      )
		assert scores[0] > 0
		assert indices[0] == 1






@require_elasticsearch
class 	_lowercase       (    __UpperCAmelCase		):




	def       _UpperCamelCase   (			self )		->							int:
			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:
					lowerCamelCase					:      Union[str, Any]          =   Elasticsearch()
					lowerCamelCase					:      Optional[Any]          =   {'acknowledged': True}
					lowerCamelCase					:      str          =   ElasticSearchIndex(es_client=UpperCAmelCase_ )
					mocked_bulk.return_value([(True, None)] * 3 )
					index.add_documents(['foo', 'bar', 'foobar'] )

					# single query
					lowerCamelCase					:      Tuple          =   'foo'
					lowerCamelCase					:      List[str]          =   {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
					lowerCamelCase     ,   lowerCamelCase					:      Any          =   index.search(UpperCAmelCase_ )
					self.assertEqual(scores[0]		,			1 )
					self.assertEqual(indices[0]		,			0 )

					# single query with timeout
					lowerCamelCase					:      Dict          =   'foo'
					lowerCamelCase					:      Optional[Any]          =   {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
					lowerCamelCase     ,   lowerCamelCase					:      Optional[Any]          =   index.search(UpperCAmelCase_		,			request_timeout=30 )
					self.assertEqual(scores[0]		,			1 )
					self.assertEqual(indices[0]		,			0 )

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

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






