




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    (		_lowerCamelCase						):






			'''simple docstring'''





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

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

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

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

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

								__magic_name__							:	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=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  )

								__magic_name__							,    __magic_name__							:	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			:   int  )						->   List[str]:
								__magic_name__							:	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(lowerCamelCase_       ,						partial(dset.get_nearest_examples       ,						'''vecs2'''       ,						np.ones(5       ,						dtype=np.floataa  )  )  )


			def 							UpperCAmelCase__       ( self			:   str  )						->   List[Any]:
								from elasticsearch import Elasticsearch

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

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






@require_faiss
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   str:
								import faiss

								__magic_name__							:	Tuple    							=							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__							:	str    							=							np.zeros(5       ,						dtype=np.floataa  )
								__magic_name__							:	Dict    							=							1
								__magic_name__							,    __magic_name__							:	Optional[Any]    							=							index.search(lowerCamelCase_  )
								self.assertRaises(lowerCamelCase_       ,						index.search       ,						query.reshape(-1       ,						1  )  )
								self.assertGreater(scores[0]       ,						0  )
								self.assertEqual(indices[0]       ,						1  )

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

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

								__magic_name__							:	Dict    							=							FaissIndex(string_factory='''Flat'''  )
								index.add_vectors(np.eye(5       ,						dtype=np.floataa  )  )
								self.assertIsInstance(index.faiss_index       ,						faiss.IndexFlat  )
								__magic_name__							:	Tuple    							=							FaissIndex(string_factory='''LSH'''  )
								index.add_vectors(np.eye(5       ,						dtype=np.floataa  )  )
								self.assertIsInstance(index.faiss_index       ,						faiss.IndexLSH  )
								with self.assertRaises(lowerCamelCase_  ):
													__magic_name__							:	Optional[int]    							=							FaissIndex(string_factory='''Flat'''       ,						custom_index=faiss.IndexFlat(5  )  )

			def 							UpperCAmelCase__       ( self			:   int  )						->   List[str]:
								import faiss

								__magic_name__							:	List[Any]    							=							faiss.IndexFlat(5  )
								__magic_name__							:	List[Any]    							=							FaissIndex(custom_index=lowerCamelCase_  )
								index.add_vectors(np.eye(5       ,						dtype=np.floataa  )  )
								self.assertIsInstance(index.faiss_index       ,						faiss.IndexFlat  )


			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   List[Any]:
								import faiss

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

								__magic_name__							:	int    							=							np.zeros(5       ,						dtype=np.floataa  )
								__magic_name__							:	Union[str, Any]    							=							1
								__magic_name__							,    __magic_name__							:	Optional[Any]    							=							index.search(lowerCamelCase_  )
								self.assertGreater(scores[0]       ,						0  )
								self.assertEqual(indices[0]       ,						1  )







@require_faiss
def 		lowercase__   (		__A:	int				):





					'''simple docstring'''






					import faiss

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

					__magic_name__							:	Tuple    							=							'''index.faiss'''
					__magic_name__							:	Optional[Any]    							=							F'''mock://{index_name}'''
					index.save(__A							,storage_options=mockfs.storage_options				)
					__magic_name__							:	Any    							=							FaissIndex.load(__A							,storage_options=mockfs.storage_options				)

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


@require_elasticsearch
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





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

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

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

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

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









import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


__lowerCamelCase   :						List[Any]				=			logging.get_logger(__name__)







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	Any    							=							os.getenv('''SM_HP_MP_PARAMETERS'''							,'''{}'''				)
					try:
										# Parse it and check the field "partitions" is included, it is required for model parallel.
										__magic_name__							:	Tuple    							=							json.loads(__A				)
										if "partitions" not in smp_options:
															return False
					except json.JSONDecodeError:
										return False

					# Get the sagemaker specific framework parameters from mpi_options variable.
					__magic_name__							:	int    							=							os.getenv('''SM_FRAMEWORK_PARAMS'''							,'''{}'''				)
					try:
										# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
										__magic_name__							:	List[Any]    							=							json.loads(__A				)
										if not mpi_options.get('''sagemaker_mpi_enabled'''							,__A				):
															return False
					except json.JSONDecodeError:
										return False
					# Lastly, check if the `smdistributed` module is present.
					return importlib.util.find_spec('''smdistributed'''				) is not None


if is_sagemaker_model_parallel_available():
	import smdistributed.modelparallel.torch as smp

	smp.init()


@dataclass
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=field(
			    default=''''''				,metadata={'''help''': '''Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'''}				,)

			def 							UpperCAmelCase__       ( self			:   str  )						->   List[str]:
								super().__post_init__()
								warnings.warn(
								    '''`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '''
								    '''`TrainingArguments` instead.'''       ,						lowerCamelCase_       ,						)

			@cached_property
			def 							UpperCAmelCase__       ( self			:   int  )						->   "torch.device":
								logger.info('''PyTorch: setting up devices'''  )
								if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
													logger.warning(
													    '''torch.distributed process group is initialized, but local_rank == -1. '''
													    '''In order to use Torch DDP, launch your script with `python -m torch.distributed.launch'''  )
								if self.no_cuda:
													__magic_name__							:	Optional[int]    							=							torch.device('''cpu'''  )
													__magic_name__							:	int    							=							0
								elif is_sagemaker_model_parallel_available():
													__magic_name__							:	Any    							=							smp.local_rank()
													__magic_name__							:	int    							=							torch.device('''cuda'''       ,						lowerCamelCase_  )
													__magic_name__							:	Optional[int]    							=							1
								elif is_sagemaker_dp_enabled():
													import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

													torch.distributed.init_process_group(backend='''smddp'''       ,						timeout=self.ddp_timeout_delta  )
													__magic_name__							:	Optional[int]    							=							int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''  )  )
													__magic_name__							:	Optional[int]    							=							torch.device('''cuda'''       ,						self.local_rank  )
													__magic_name__							:	str    							=							1
								elif self.local_rank == -1:
													# if n_gpu is > 1 we'll use nn.DataParallel.
													# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
													# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
													# trigger an error that a device index is missing. Index 0 takes into account the
													# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
													# will use the first GPU in that env, i.e. GPU#1
													__magic_name__							:	List[str]    							=							torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''  )
													# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
													# the default value.
													__magic_name__							:	Dict    							=							torch.cuda.device_count()
								else:
													# Here, we'll use torch.distributed.
													# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
													if not torch.distributed.is_initialized():
																		torch.distributed.init_process_group(backend='''nccl'''       ,						timeout=self.ddp_timeout_delta  )
													__magic_name__							:	str    							=							torch.device('''cuda'''       ,						self.local_rank  )
													__magic_name__							:	Dict    							=							1

								if device.type == "cuda":
													torch.cuda.set_device(lowerCamelCase_  )

								return device

			@property
			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   int:
								if is_sagemaker_model_parallel_available():
													return smp.dp_size()

								return super().world_size

			@property
			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   List[str]:
								return not is_sagemaker_model_parallel_available()


			@property
			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Tuple:
								return False



