


'''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


__UpperCAmelCase       =							pytest.mark.integration




@require_faiss
class 		a__    (							a__  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														lowerCAmelCase__	       =     Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(lowerCamelCase_       ) for x in np.arange(30       ).tolist()]}       )
														return dset







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

														lowerCAmelCase__	       =     self._create_dummy_dataset()
														lowerCAmelCase__	       =     dset.map(
														    lambda lowerCamelCase_ ,   lowerCamelCase_       : {"vecs": i * np.ones(5 ,   dtype=np.floataa       )} ,   with_indices=lowerCamelCase_ ,   keep_in_memory=lowerCamelCase_       )
														lowerCAmelCase__	       =     dset.add_faiss_index('''vecs''' ,   batch_size=1_00 ,   metric_type=faiss.METRIC_INNER_PRODUCT       )
														lowerCAmelCase__       , lowerCAmelCase__	       =     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]:
														import faiss

														lowerCAmelCase__	       =     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=1_00 ,   metric_type=faiss.METRIC_INNER_PRODUCT ,   )
														lowerCAmelCase__       , lowerCAmelCase__	       =     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       )	->							int:
														import faiss

														lowerCAmelCase__	       =     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=lowerCamelCase_       ) 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__	       =     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       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     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(lowerCamelCase_ ,   partial(dset.get_nearest_examples ,   '''vecs2''' ,   np.ones(5 ,   dtype=np.floataa       )       )       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														from elasticsearch import Elasticsearch

														lowerCAmelCase__	       =     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__	       =     {'''acknowledged''': True}
																					mocked_bulk.return_value([(True, None)] * 30       )
																					lowerCAmelCase__	       =     {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
																					lowerCAmelCase__	       =     Elasticsearch()

																					dset.add_elasticsearch_index('''filename''' ,   es_client=lowerCamelCase_       )
																					lowerCAmelCase__       , lowerCAmelCase__	       =     dset.get_nearest_examples('''filename''' ,   '''my_name-train_29'''       )
																					self.assertEqual(examples['''filename'''][0] ,   '''my_name-train_29'''       )




@require_faiss
class 		a__    (							a__  ):





							'''simple docstring'''






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

														lowerCAmelCase__	       =     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__	       =     np.zeros(5 ,   dtype=np.floataa       )
														lowerCAmelCase__	       =     1
														lowerCAmelCase__       , lowerCAmelCase__	       =     index.search(lowerCamelCase_       )
														self.assertRaises(lowerCamelCase_ ,   index.search ,   query.reshape(-1 ,   1       )       )
														self.assertGreater(scores[0] ,   0       )
														self.assertEqual(indices[0] ,   1       )

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														import faiss

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														import faiss

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														import faiss

														lowerCAmelCase__	       =     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=lowerCamelCase_       ) as tmp_file:
																					index.save(tmp_file.name       )
																					lowerCAmelCase__	       =     FaissIndex.load(tmp_file.name       )
														os.unlink(tmp_file.name       )

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


@require_faiss
def  _snake_case       (					A	)						->   Tuple:
							import faiss

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

							lowerCAmelCase__	       =     '''index.faiss'''
							lowerCAmelCase__	       =     F"""mock://{index_name}"""
							index.save(A			,   storage_options=mockfs.storage_options	)
							lowerCAmelCase__	       =     FaissIndex.load(A			,   storage_options=mockfs.storage_options	)

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






@require_elasticsearch
class 		a__    (							a__  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[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__	       =     Elasticsearch()
																					lowerCAmelCase__	       =     {'''acknowledged''': True}
																					lowerCAmelCase__	       =     ElasticSearchIndex(es_client=lowerCamelCase_       )
																					mocked_bulk.return_value([(True, None)] * 3       )
																					index.add_documents(['''foo''', '''bar''', '''foobar''']       )

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

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

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

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



'''simple docstring'''






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def  _snake_case       (					A			,   A			,   A			,   A=5	)						->   List[str]:
							# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
							assert masked_input.count('''<mask>'''	) == 1
							lowerCAmelCase__	       =     torch.tensor(tokenizer.encode(A			,   add_special_tokens=A	)	).unsqueeze(0	)  # Batch size 1
							lowerCAmelCase__	       =     model(A	)[0]  # The last hidden-state is the first element of the output tuple
							lowerCAmelCase__	       =     (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
							lowerCAmelCase__	       =     logits[0, masked_index, :]
							lowerCAmelCase__	       =     logits.softmax(dim=0	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     prob.topk(k=A			,   dim=0	)
							lowerCAmelCase__	       =     ''' '''.join(
							    [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(A	)	)]	)
							lowerCAmelCase__	       =     tokenizer.mask_token
							lowerCAmelCase__	       =     []
							for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''	)	):
														lowerCAmelCase__	       =     predicted_token_bpe.replace('''\u2581'''			,   ''' '''	)
														if " {0}".format(A	) in masked_input:
																					topk_filled_outputs.append(
																					    (
																					        masked_input.replace(''' {0}'''.format(A	)			,   A	),
																					        values[index].item(),
																					        predicted_token,
																					    )	)
														else:
																					topk_filled_outputs.append(
																					    (
																					        masked_input.replace(A			,   A	),
																					        values[index].item(),
																					        predicted_token,
																					    )	)
							return topk_filled_outputs


__UpperCAmelCase       =							CamembertTokenizer.from_pretrained('''camembert-base''')
__UpperCAmelCase       =							CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

__UpperCAmelCase       =							'''Le camembert est <mask> :)'''






print(fill_mask(masked_input, model, tokenizer, topk=3))