


'''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[Any]						= pytest.mark.integration





@require_faiss
class      snake_case    (    lowercase			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  Dataset.from_dict({"filename": ["my_name-train" + "_" + str(UpperCamelCase     ) for x in np.arange(30     ).tolist()]}     )
   return dset






  def   snake_case		(    self     ):







   """simple docstring"""
   import faiss

   lowerCamelCase_      =  self._create_dummy_dataset()
   lowerCamelCase_      =  dset.map(
       lambda UpperCamelCase			,  UpperCamelCase     : {"vecs": i * np.ones(5			,  dtype=np.floataa     )}			,  with_indices=UpperCamelCase			,  keep_in_memory=UpperCamelCase     )
   lowerCamelCase_      =  dset.add_faiss_index("vecs"			,  batch_size=100			,  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   snake_case		(    self     ):







   """simple docstring"""
   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=100			,  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   snake_case		(    self     ):







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






  def   snake_case		(    self     ):







   """simple docstring"""
   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(UpperCamelCase			,  partial(dset.get_nearest_examples			,  "vecs2"			,  np.ones(5			,  dtype=np.floataa     )     )     )







  def   snake_case		(    self     ):







   """simple docstring"""
   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=UpperCamelCase     )
    lowerCamelCase_ ,lowerCamelCase_      =  dset.get_nearest_examples("filename"			,  "my_name-train_29"     )
    self.assertEqual(examples["filename"][0]			,  "my_name-train_29"     )





@require_faiss
class      snake_case    (    lowercase			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   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(UpperCamelCase     )
   self.assertRaises(UpperCamelCase			,  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(UpperCamelCase     )
   self.assertRaises(UpperCamelCase			,  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(UpperCamelCase     )			,  0     )
   self.assertListEqual([4, 3, 2, 1, 0]			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   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(UpperCamelCase     ):
    lowerCamelCase_      =  FaissIndex(string_factory="Flat"			,  custom_index=faiss.IndexFlat(5     )     )






  def   snake_case		(    self     ):







   """simple docstring"""
   import faiss

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







  def   snake_case		(    self     ):







   """simple docstring"""
   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=UpperCamelCase     ) 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(UpperCamelCase     )
   self.assertGreater(scores[0]			,  0     )
   self.assertEqual(indices[0]			,  1     )





@require_faiss
def __snake_case					(      UpperCAmelCase_						:						List[Any]		):
 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(UpperCAmelCase_					,				storage_options=mockfs.storage_options		)
 lowerCamelCase_      =  FaissIndex.load(UpperCAmelCase_					,				storage_options=mockfs.storage_options		)

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







@require_elasticsearch
class      snake_case    (    lowercase			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   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=UpperCamelCase     )
    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(UpperCamelCase     )
    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(UpperCamelCase			,  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(UpperCamelCase     )
    lowerCamelCase_      =  [scores[0] for scores in total_scores]
    lowerCamelCase_      =  [indices[0] for indices in total_indices]
    self.assertGreater(np.min(UpperCamelCase     )			,  0     )
    self.assertListEqual([1, 1, 1]			,  UpperCamelCase     )

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






import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


a__				=		logging.get_logger(__name__)

@dataclass
class 				UpperCAmelCase_		:
							"""simple docstring"""







							UpperCAmelCase__       :						str	    =    field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()   )}   )
							UpperCAmelCase__       :						str	    =    field(
							    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}   )
							UpperCAmelCase__       :						int	    =    field(
							    default=128				,       metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }				,       )
							UpperCAmelCase__       :						bool	    =    field(
							    default=__lowercase				,       metadata={"help": "Overwrite the cached training and evaluation sets"}   )


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    List[Any]         =		self.task_name.lower()



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						List[str]	    =    "train"
							UpperCAmelCase__       :						List[str]	    =    "dev"
							UpperCAmelCase__       :						Union[str, Any]	    =    "test"



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						GlueDataTrainingArguments
							UpperCAmelCase__       :						str
							UpperCAmelCase__       :						List[InputFeatures]


							def __init__(			self       ,							_a       ,							_a       ,							_a = None       ,							_a = Split.train       ,							_a = None       ,							)    ->    str:
												warnings.warn(
												    '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
												    '''library. You can have a look at this example script for pointers: '''
												    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''       ,							_a       ,							)
												_a	:    List[str]         =		args
												_a	:    List[str]         =		glue_processors[args.task_name]()
												_a	:    List[Any]         =		glue_output_modes[args.task_name]
												if isinstance(_a       ,							_a       ):
																	try:
																						_a	:    Any         =		Split[mode]
																	except KeyError:
																						raise KeyError('''mode is not a valid split name'''       )
        # Load data features from cache or dataset file
												_a	:    Union[str, Any]         =		os.path.join(
												    cache_dir if cache_dir is not None else args.data_dir       ,							F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""       ,							)
												_a	:    str         =		self.processor.get_labels()
												if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
												    "RobertaTokenizer",
												    "RobertaTokenizerFast",
												    "XLMRobertaTokenizer",
												    "BartTokenizer",
												    "BartTokenizerFast",
												):
																	# HACK(label indices are swapped in RoBERTa pretrained model)
																	_a      ,					_a	:    Optional[int]         =		label_list[2], label_list[1]
												_a	:    Dict         =		label_list

												# Make sure only the first process in distributed training processes the dataset,
												# and the others will use the cache.
												_a	:    Optional[int]         =		cached_features_file + '''.lock'''
												with FileLock(_a       ):
																	if os.path.exists(_a       ) and not args.overwrite_cache:
																						_a	:    List[Any]         =		time.time()
																						_a	:    str         =		torch.load(_a       )
																						logger.info(
																						    F"""Loading features from cached file {cached_features_file} [took %.3f s]"""       ,							time.time() - start       )
																	else:
																						logger.info(F"""Creating features from dataset file at {args.data_dir}"""       )

																						if mode == Split.dev:
																											_a	:    str         =		self.processor.get_dev_examples(args.data_dir       )
																						elif mode == Split.test:
																											_a	:    Any         =		self.processor.get_test_examples(args.data_dir       )
																						else:
																											_a	:    List[str]         =		self.processor.get_train_examples(args.data_dir       )
																						if limit_length is not None:
																											_a	:    Dict         =		examples[:limit_length]
																						_a	:    Union[str, Any]         =		glue_convert_examples_to_features(
																						    _a       ,							_a       ,							max_length=args.max_seq_length       ,							label_list=_a       ,							output_mode=self.output_mode       ,							)
																						_a	:    Optional[int]         =		time.time()
																						torch.save(self.features       ,							_a       )
																						# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																						logger.info(
																						    F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""       )


							def __len__(			self       )    ->    Optional[int]:
												return len(self.features       )


							def __getitem__(			self       ,							_a       )    ->    InputFeatures:
												return self.features[i]




							def 				__lowercase				(			self       )    ->    Tuple:
												return self.label_list



