

'''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


_lowerCAmelCase         =						pytest.mark.integration






@require_faiss
class   _SCREAMING_SNAKE_CASE   (   __a     ):



 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(a__      ) for x in np.arange(30      ).tolist()]}      )
      return dset



 def 			snake_case__  (   self		:   List[str]      ):
      import faiss

      __magic_name__						       =    self._create_dummy_dataset()
      __magic_name__						       =    dset.map(
          lambda a__			,				a__      : {"vecs": i * np.ones(5			,				dtype=np.floataa      )}			,				with_indices=a__			,				keep_in_memory=a__      )
      __magic_name__						       =    dset.add_faiss_index('''vecs'''			,				batch_size=100			,				metric_type=faiss.METRIC_INNER_PRODUCT      )
      __magic_name__       ,  __magic_name__						       =    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 			snake_case__  (   self		:   str      ):
      import faiss

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



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      import faiss

      __magic_name__						       =    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      )

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



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    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 			snake_case__  (   self		:   List[str]      ):
      from elasticsearch import Elasticsearch

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

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






@require_faiss
class   _SCREAMING_SNAKE_CASE   (   __a     ):



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      import faiss

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

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



 def 			snake_case__  (   self		:   Optional[Any]      ):
      import faiss

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



 def 			snake_case__  (   self		:   List[str]      ):
      import faiss

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



 def 			snake_case__  (   self		:   str      ):
      import faiss

      __magic_name__						       =    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      )
           __magic_name__						       =    FaissIndex.load(tmp_file.name      )
      os.unlink(tmp_file.name      )

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




@require_faiss
def 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     import faiss

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

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

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






@require_elasticsearch
class   _SCREAMING_SNAKE_CASE   (   __a     ):



 def 			snake_case__  (   self		:   Any      ):
      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:
           __magic_name__						       =    Elasticsearch()
           __magic_name__						       =    {'''acknowledged''': True}
           __magic_name__						       =    ElasticSearchIndex(es_client=a__      )
           mocked_bulk.return_value([(True, None)] * 3      )
           index.add_documents(['''foo''', '''bar''', '''foobar''']      )

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

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

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

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









'''simple docstring'''




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer




def 						UpperCamelCase	(			a		,						a		,						a		,						a=1024			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    [], []

     __magic_name__						       =    list(zip(a		,						a			)			)
     __magic_name__       ,  __magic_name__						       =    sorted_examples[0]

     def is_too_big(a			):
          return tok(a		,						return_tensors='''pt'''			).input_ids.shape[1] > max_tokens

     for src, tgt in tqdm(sorted_examples[1:]			):
          __magic_name__						       =    new_src + ''' ''' + src
          __magic_name__						       =    new_tgt + ''' ''' + tgt
          if is_too_big(a			) or is_too_big(a			):  # cant fit, finalize example
               finished_src.append(a			)
               finished_tgt.append(a			)
               __magic_name__       ,  __magic_name__						       =    src, tgt
          else:  # can fit, keep adding
               __magic_name__       ,  __magic_name__						       =    cand_src, cand_tgt

    # cleanup
     if new_src:
          assert new_tgt
          finished_src.append(a			)
          finished_tgt.append(a			)
     return finished_src, finished_tgt




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     Any:






     '''simple docstring'''
     __magic_name__						       =    Path(a			)
     save_path.mkdir(exist_ok=a			)
     for split in ["train"]:
          __magic_name__       ,  __magic_name__						       =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__       ,  __magic_name__						       =    pack_examples(a		,						a		,						a		,						a			)
          print(F'''packed {split} split from {len(a			)} examples -> {len(a			)}.'''			)
          Path(save_path / F'''{split}.source'''			).open('''w'''			).write('''\n'''.join(a			)			)
          Path(save_path / F'''{split}.target'''			).open('''w'''			).write('''\n'''.join(a			)			)
     for split in ["val", "test"]:
          __magic_name__       ,  __magic_name__						       =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
          shutil.copyfile(a		,						save_path / F'''{split}.source'''			)
          shutil.copyfile(a		,						save_path / F'''{split}.target'''			)




def 						UpperCamelCase	(			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    argparse.ArgumentParser()
     parser.add_argument('''--tok_name'''		,						type=a		,						help='''like facebook/bart-large-cnn,t5-base, etc.'''			)
     parser.add_argument('''--max_seq_len'''		,						type=a		,						default=128			)
     parser.add_argument('''--data_dir'''		,						type=a			)
     parser.add_argument('''--save_path'''		,						type=a			)
     __magic_name__						       =    parser.parse_args()
     __magic_name__						       =    AutoTokenizer.from_pretrained(args.tok_name			)
     return pack_data_dir(a		,						Path(args.data_dir			)		,						args.max_seq_len		,						args.save_path			)


if __name__ == "__main__":
      packer_cli()






