
"""simple docstring"""





import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


_a								=						trt.Logger(trt.Logger.WARNING)
_a								=						absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

_a								=						logging.getLogger(__name__)

_a								=						argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    """--onnx_model_path""",
    default=None,
    type=str,
    required=True,
    help="""Path to ONNX model: """,
)

parser.add_argument(
    """--output_dir""",
    default=None,
    type=str,
    required=True,
    help="""The output directory where the model checkpoints and predictions will be written.""",
)

# Other parameters

parser.add_argument(
    """--tokenizer_name""",
    default="""""",
    type=str,
    required=True,
    help="""Pretrained tokenizer name or path if not the same as model_name""",
)

parser.add_argument(
    """--version_2_with_negative""",
    action="""store_true""",
    help="""If true, the SQuAD examples contain some that do not have an answer.""",
)
parser.add_argument(
    """--null_score_diff_threshold""",
    type=float,
    default=0.0,
    help="""If null_score - best_non_null is greater than the threshold predict null.""",
)

parser.add_argument(
    """--max_seq_length""",
    default=384,
    type=int,
    help=(
        """The maximum total input sequence length after WordPiece tokenization. Sequences """
        """longer than this will be truncated, and sequences shorter than this will be padded."""
    ),
)
parser.add_argument(
    """--doc_stride""",
    default=128,
    type=int,
    help="""When splitting up a long document into chunks, how much stride to take between chunks.""",
)

parser.add_argument("""--per_device_eval_batch_size""", default=8, type=int, help="""Batch size per GPU/CPU for evaluation.""")

parser.add_argument(
    """--n_best_size""",
    default=20,
    type=int,
    help="""The total number of n-best predictions to generate in the nbest_predictions.json output file.""",
)
parser.add_argument(
    """--max_answer_length""",
    default=30,
    type=int,
    help=(
        """The maximum length of an answer that can be generated. This is needed because the start """
        """and end predictions are not conditioned on one another."""
    ),
)

parser.add_argument("""--seed""", type=int, default=42, help="""random seed for initialization""")

parser.add_argument(
    """--dataset_name""",
    type=str,
    default=None,
    required=True,
    help="""The name of the dataset to use (via the datasets library).""",
)
parser.add_argument(
    """--dataset_config_name""",
    type=str,
    default=None,
    help="""The configuration name of the dataset to use (via the datasets library).""",
)
parser.add_argument(
    """--preprocessing_num_workers""", type=int, default=4, help="""A csv or a json file containing the training data."""
)
parser.add_argument("""--overwrite_cache""", action="""store_true""", help="""Overwrite the cached training and evaluation sets""")
parser.add_argument(
    """--fp16""",
    action="""store_true""",
    help="""Whether to use 16-bit (mixed) precision instead of 32-bit""",
)
parser.add_argument(
    """--int8""",
    action="""store_true""",
    help="""Whether to use INT8""",
)

_a								=						parser.parse_args()

if args.tokenizer_name:
				_a								=						AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
				raise ValueError(
				    """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
				    """You can do it from another script, save it, and load it from here, using --tokenizer_name."""
				)

logger.info("""Training/evaluation parameters %s""", args)

_a								=						args.per_device_eval_batch_size

_a								=						(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_a								=						True

_a								=						"""temp_engine/bert-fp32.engine"""
if args.fpaa:
				_a								=						"""temp_engine/bert-fp16.engine"""
if args.inta:
				_a								=						"""temp_engine/bert-int8.engine"""

# import ONNX file
if not os.path.exists("""temp_engine"""):
				os.makedirs("""temp_engine""")

_a								=						1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
				with open(args.onnx_model_path, """rb""") as model:
								if not parser.parse(model.read()):
												for error in range(parser.num_errors):
																print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
				_a								=						[network.get_input(i) for i in range(network.num_inputs)]
				_a								=						[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

				with builder.create_builder_config() as config:
								_a								=						1 << 50
								if STRICT_TYPES:
												config.set_flag(trt.BuilderFlag.STRICT_TYPES)
								if args.fpaa:
												config.set_flag(trt.BuilderFlag.FPaa)
								if args.inta:
												config.set_flag(trt.BuilderFlag.INTa)
								_a								=						builder.create_optimization_profile()
								config.add_optimization_profile(profile)
								for i in range(len(input_names)):
												profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
								_a								=						builder.build_engine(network, config)

								# serialize_engine and store in file (can be directly loaded and deserialized):
								with open(engine_name, """wb""") as f:
												f.write(engine.serialize())



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						Tuple:


					"""simple docstring"""





					_UpperCamelCase									=    np.asarray(inputs['''input_ids'''],	dtype=np.intaa      )
					_UpperCamelCase									=    np.asarray(inputs['''attention_mask'''],	dtype=np.intaa      )
					_UpperCamelCase									=    np.asarray(inputs['''token_type_ids'''],	dtype=np.intaa      )

					# Copy inputs
					cuda.memcpy_htod_async(d_inputs[0],	input_ids.ravel(),	__snake_case      )
					cuda.memcpy_htod_async(d_inputs[1],	attention_mask.ravel(),	__snake_case      )
					cuda.memcpy_htod_async(d_inputs[2],	token_type_ids.ravel(),	__snake_case      )
					# start time
					_UpperCamelCase									=    time.time()
					# Run inference
					context.execute_async(
					    bindings=[int(__snake_case      ) for d_inp in d_inputs] + [int(__snake_case      ), int(__snake_case      )],	stream_handle=stream.handle      )
					# Transfer predictions back from GPU
					cuda.memcpy_dtoh_async(__snake_case,	__snake_case,	__snake_case      )
					cuda.memcpy_dtoh_async(__snake_case,	__snake_case,	__snake_case      )
					# Synchronize the stream and take time
					stream.synchronize()
					# end time
					_UpperCamelCase									=    time.time()
					_UpperCamelCase									=    end_time - start_time
					_UpperCamelCase									=    (h_outputa, h_outputa)
					# print(outputs)
					return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
_a								=						Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
				datasets.utils.logging.set_verbosity_warning()
				transformers.utils.logging.set_verbosity_info()
else:
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
				set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
				# Downloading and loading a dataset from the hub.
				_a								=						load_dataset(args.dataset_name, args.dataset_config_name)
else:
				raise ValueError("""Evaluation requires a dataset name""")
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

_a								=						raw_datasets["""validation"""].column_names

_a								=						"""question""" if """question""" in column_names else column_names[0]
_a								=						"""context""" if """context""" in column_names else column_names[1]
_a								=						"""answers""" if """answers""" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
_a								=						tokenizer.padding_side == """right"""

if args.max_seq_length > tokenizer.model_max_length:
				logger.warning(
				    F"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
				    F"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
				)

_a								=						min(args.max_seq_length, tokenizer.model_max_length)



def  lowerCamelCase__						(     __snake_case      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    [q.lstrip() for q in examples[question_column_name]]

					# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
					# in one example possible giving several features when a context is long, each of those features having a
					# context that overlaps a bit the context of the previous feature.
					_UpperCamelCase									=    tokenizer(
					    examples[question_column_name if pad_on_right else context_column_name],	examples[context_column_name if pad_on_right else question_column_name],	truncation='''only_second''' if pad_on_right else '''only_first''',	max_length=__snake_case,	stride=args.doc_stride,	return_overflowing_tokens=__snake_case,	return_offsets_mapping=__snake_case,	padding='''max_length''',	)

					# Since one example might give us several features if it has a long context, we need a map from a feature to
					# its corresponding example. This key gives us just that.
					_UpperCamelCase									=    tokenized_examples.pop('''overflow_to_sample_mapping'''      )

					# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
					# corresponding example_id and we will store the offset mappings.
					_UpperCamelCase									=    []

					for i in range(len(tokenized_examples['''input_ids''']      )      ):
										# Grab the sequence corresponding to that example (to know what is the context and what is the question).
										_UpperCamelCase									=    tokenized_examples.sequence_ids(__snake_case      )
										_UpperCamelCase									=    1 if pad_on_right else 0

										# One example can give several spans, this is the index of the example containing this span of text.
										_UpperCamelCase									=    sample_mapping[i]
										tokenized_examples["example_id"].append(examples['''id'''][sample_index]      )

										# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
										# position is part of the context or not.
										_UpperCamelCase									=    [
										    (o if sequence_ids[k] == context_index else None)
										    for k, o in enumerate(tokenized_examples['''offset_mapping'''][i]      )
										]

					return tokenized_examples


_a								=						raw_datasets["""validation"""]
# Validation Feature Creation
_a								=						eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc="""Running tokenizer on validation dataset""",
)

_a								=						default_data_collator

_a								=						eval_dataset.remove_columns(["""example_id""", """offset_mapping"""])
_a								=						DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case="eval"      )				->						str:


					"""simple docstring"""





					_UpperCamelCase									=    postprocess_qa_predictions(
					    examples=__snake_case,	features=__snake_case,	predictions=__snake_case,	version_2_with_negative=args.version_2_with_negative,	n_best_size=args.n_best_size,	max_answer_length=args.max_answer_length,	null_score_diff_threshold=args.null_score_diff_threshold,	output_dir=args.output_dir,	prefix=__snake_case,	)
					# Format the result to the format the metric expects.
					if args.version_2_with_negative:
										_UpperCamelCase									=    [
										    {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
										]
					else:
										_UpperCamelCase									=    [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

					_UpperCamelCase									=    [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
					return EvalPrediction(predictions=__snake_case,	label_ids=__snake_case      )


_a								=						load_metric("""squad_v2""" if args.version_2_with_negative else """squad""")

# Evaluation!
logger.info("""Loading ONNX model %s for evaluation""", args.onnx_model_path)
with open(engine_name, """rb""") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
				# setup for TRT inferrence
				for i in range(len(input_names)):
								context.set_binding_shape(i, INPUT_SHAPE)
				assert context.all_binding_shapes_specified



				def  lowerCamelCase__						(     __snake_case      )				->						List[str]:


									"""simple docstring"""





									return trt.volume(engine.get_binding_shape(__snake_case      )      ) * engine.get_binding_dtype(__snake_case      ).itemsize

				# Allocate device memory for inputs and outputs.
				_a								=						[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

				# Allocate output buffer
				_a								=						cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
				_a								=						cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
				_a								=						cuda.mem_alloc(h_outputa.nbytes)
				_a								=						cuda.mem_alloc(h_outputa.nbytes)

				# Create a stream in which to copy inputs/outputs and run inference.
				_a								=						cuda.Stream()

				# Evaluation
				logger.info("""***** Running Evaluation *****""")
				logger.info(F"""  Num examples = {len(eval_dataset)}""")
				logger.info(F"""  Batch size = {args.per_device_eval_batch_size}""")

				_a								=						0.0
				_a								=						0
				_a								=						timeit.default_timer()

				_a								=						None
				for step, batch in enumerate(eval_dataloader):
								_a				,				_a								=						model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
								total_time += infer_time
								niter += 1

								_a				,				_a								=						outputs
								_a								=						torch.tensor(start_logits)
								_a								=						torch.tensor(end_logits)

								# necessary to pad predictions and labels for being gathered
								_a								=						accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
								_a								=						accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

								_a								=						(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
								_a								=						logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

				if all_preds is not None:
								_a								=						nested_truncate(all_preds, len(eval_dataset))

				_a								=						timeit.default_timer() - start_time
				logger.info("""  Evaluation done in total %f secs (%f sec per example)""", evalTime, evalTime / len(eval_dataset))
				# Inference time from TRT
				logger.info("""Average Inference Time = {:.3f} ms""".format(total_time * 1000 / niter))
				logger.info("""Total Inference Time =  {:.3f} ms""".format(total_time * 1000))
				logger.info("""Total Number of Inference =  %d""", niter)

_a								=						post_processing_function(eval_examples, eval_dataset, all_preds)
_a								=						metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(F"""Evaluation metrics: {eval_metric}""")









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      :					List[str]					    =      pytest.mark.integration





@require_faiss
class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(lowercase__     ) for x in np.arange(3_0     ).tolist()]}     )
             return dset

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             import faiss

             __A    =self._create_dummy_dataset()
             __A    =dset.map(
                 lambda lowercase__ ,   lowercase__     : {"vecs": i * np.ones(5 ,   dtype=np.floataa     )} ,   with_indices=lowercase__ ,   keep_in_memory=lowercase__     )
             __A    =dset.add_faiss_index('''vecs''' ,   batch_size=1_0_0 ,   metric_type=faiss.METRIC_INNER_PRODUCT     )
             __A    ,		__A    =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     ):







             '''simple docstring'''



             import faiss

             __A    =self._create_dummy_dataset()
             dset.add_faiss_index_from_external_arrays(
                 external_arrays=np.ones((3_0, 5)     ) * np.arange(3_0     ).reshape(-1 ,   1     ) ,   index_name='''vecs''' ,   batch_size=1_0_0 ,   metric_type=faiss.METRIC_INNER_PRODUCT ,   )
             __A    ,		__A    =dset.get_nearest_examples('''vecs''' ,   np.ones(5 ,   dtype=np.floataa     )     )
             self.assertEqual(examples['''filename'''][0] ,   '''my_name-train_29'''     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             import faiss

             __A    =self._create_dummy_dataset()
             dset.add_faiss_index_from_external_arrays(
                 external_arrays=np.ones((3_0, 5)     ) * np.arange(3_0     ).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=lowercase__     ) 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    =dset.get_nearest_examples('''vecs2''' ,   np.ones(5 ,   dtype=np.floataa     )     )
             self.assertEqual(examples['''filename'''][0] ,   '''my_name-train_29'''     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self._create_dummy_dataset()
             dset.add_faiss_index_from_external_arrays(
                 external_arrays=np.ones((3_0, 5)     ) * np.arange(3_0     ).reshape(-1 ,   1     ) ,   index_name='''vecs'''     )
             dset.drop_index('''vecs'''     )
             self.assertRaises(lowercase__ ,   partial(dset.get_nearest_examples ,   '''vecs2''' ,   np.ones(5 ,   dtype=np.floataa     )     )     )


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             from elasticsearch import Elasticsearch

             __A    =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    ={'''acknowledged''': True}
                   mocked_bulk.return_value([(True, None)] * 3_0     )
                   __A    ={'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 2_9}]}}
                   __A    =Elasticsearch()

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





@require_faiss
class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             import faiss

             __A    =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 ,   1_0     )

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

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             import faiss

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             import faiss

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


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             import faiss

             __A    =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=lowercase__     ) as tmp_file:
                   index.save(tmp_file.name     )
                   __A    =FaissIndex.load(tmp_file.name     )
             os.unlink(tmp_file.name     )

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





@require_faiss
def      A__					(			__A  :						Dict			)					->List[Any]:
      import faiss

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

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

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





@require_elasticsearch
class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def     __UpperCamelCase     (							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:
                   __A    =Elasticsearch()
                   __A    ={'''acknowledged''': True}
                   __A    =ElasticSearchIndex(es_client=lowercase__     )
                   mocked_bulk.return_value([(True, None)] * 3     )
                   index.add_documents(['''foo''', '''bar''', '''foobar''']     )

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

                   # single query with timeout
                   __A    ='''foo'''
                   __A    ={'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
                   __A    ,		__A    =index.search(lowercase__ ,   request_timeout=3_0     )
                   self.assertEqual(scores[0] ,   1     )
                   self.assertEqual(indices[0] ,   0     )

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

                   # batched queries with timeout
                   __A    =['''foo''', '''bar''', '''foobar''']
                   __A    ={'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
                   __A    ,		__A    =index.search_batch(lowercase__ ,   request_timeout=3_0     )
                   __A    =[scores[0] for scores in total_scores]
                   __A    =[indices[0] for indices in total_indices]
                   self.assertGreater(np.min(lowercase__     ) ,   0     )
                   self.assertListEqual([1, 1, 1] ,   lowercase__     )


