
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


lowercase						:      List[Any]									= pytest.mark.integration




@require_faiss
class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:Dict		)	->				int:
								__UpperCamelCase			:     Tuple         =      Dataset.from_dict({"filename": ["my_name-train" + "_" + str(a		) for x in np.arange(3_0		).tolist()]}		)
								return dset







	def      _lowerCamelCase       (      self	:Tuple		)	->				Any:
								import faiss

								__UpperCamelCase			:     Dataset         =      self._create_dummy_dataset()
								__UpperCamelCase			:     int         =      dset.map(
								    lambda a  ,     a		: {"vecs": i * np.ones(5  ,     dtype=np.floataa		)}  ,     with_indices=a  ,     keep_in_memory=a		)
								__UpperCamelCase			:     str         =      dset.add_faiss_index("vecs"  ,     batch_size=1_0_0  ,     metric_type=faiss.METRIC_INNER_PRODUCT		)
								__UpperCamelCase     ,		__UpperCamelCase			:     List[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      _lowerCamelCase       (      self	:int		)	->				List[str]:
								import faiss

								__UpperCamelCase			:     Dataset         =      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  ,     )
								__UpperCamelCase     ,		__UpperCamelCase			:     List[str]         =      dset.get_nearest_examples("vecs"  ,     np.ones(5  ,     dtype=np.floataa		)		)
								self.assertEqual(examples["filename"][0]  ,     "my_name-train_29"		)







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

								__UpperCamelCase			:     Dataset         =      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=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		)

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







	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				Optional[int]:
								__UpperCamelCase			:     Dataset         =      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(a  ,     partial(dset.get_nearest_examples  ,     "vecs2"  ,     np.ones(5  ,     dtype=np.floataa		)		)		)







	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				int:
								from elasticsearch import Elasticsearch

								__UpperCamelCase			:     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:
															__UpperCamelCase			:     List[Any]         =      {"acknowledged": True}
															mocked_bulk.return_value([(True, None)] * 3_0		)
															__UpperCamelCase			:     Dict         =      {"hits": {"hits": [{"_score": 1, "_id": 2_9}]}}
															__UpperCamelCase			:     List[Any]         =      Elasticsearch()

															dset.add_elasticsearch_index("filename"  ,     es_client=a		)
															__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      dset.get_nearest_examples("filename"  ,     "my_name-train_29"		)
															self.assertEqual(examples["filename"][0]  ,     "my_name-train_29"		)




@require_faiss
class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




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

								__UpperCamelCase			:     Optional[int]         =      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
								__UpperCamelCase			:     Union[str, Any]         =      np.zeros(5  ,     dtype=np.floataa		)
								__UpperCamelCase			:     Union[str, Any]         =      1
								__UpperCamelCase     ,		__UpperCamelCase			:     List[str]         =      index.search(a		)
								self.assertRaises(a  ,     index.search  ,     query.reshape(-1  ,     1		)		)
								self.assertGreater(scores[0]  ,     0		)
								self.assertEqual(indices[0]  ,     1		)

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







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

								__UpperCamelCase			:     Optional[Any]         =      FaissIndex(string_factory="Flat"		)
								index.add_vectors(np.eye(5  ,     dtype=np.floataa		)		)
								self.assertIsInstance(index.faiss_index  ,     faiss.IndexFlat		)
								__UpperCamelCase			:     str         =      FaissIndex(string_factory="LSH"		)
								index.add_vectors(np.eye(5  ,     dtype=np.floataa		)		)
								self.assertIsInstance(index.faiss_index  ,     faiss.IndexLSH		)
								with self.assertRaises(a		):
															__UpperCamelCase			:     Union[str, Any]         =      FaissIndex(string_factory="Flat"  ,     custom_index=faiss.IndexFlat(5		)		)







	def      _lowerCamelCase       (      self	:List[str]		)	->				Optional[Any]:
								import faiss

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







	def      _lowerCamelCase       (      self	:Any		)	->				Any:
								import faiss

								__UpperCamelCase			:     int         =      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		)
															__UpperCamelCase			:     int         =      FaissIndex.load(tmp_file.name		)
								os.unlink(tmp_file.name		)

								__UpperCamelCase			:     Any         =      np.zeros(5  ,     dtype=np.floataa		)
								__UpperCamelCase			:     List[str]         =      1
								__UpperCamelCase     ,		__UpperCamelCase			:     Optional[Any]         =      index.search(a		)
								self.assertGreater(scores[0]  ,     0		)
								self.assertEqual(indices[0]  ,     1		)




@require_faiss
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict)      ->					Any:






							'''simple docstring'''





							import faiss

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

							__UpperCamelCase			:     str         =      "index.faiss"
							__UpperCamelCase			:     Dict         =      F'mock://{index_name}'
							index.save(_lowerCamelCase		,						storage_options=mockfs.storage_options)
							__UpperCamelCase			:     int         =      FaissIndex.load(_lowerCamelCase		,						storage_options=mockfs.storage_options)

							__UpperCamelCase			:     List[Any]         =      np.zeros(5		,						dtype=np.floataa)
							__UpperCamelCase			:     Optional[int]         =      1
							__UpperCamelCase     ,		__UpperCamelCase			:     List[str]         =      index.search(_lowerCamelCase)
							assert scores[0] > 0
							assert indices[0] == 1




@require_elasticsearch
class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:List[str]		)	->				str:
								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:
															__UpperCamelCase			:     Tuple         =      Elasticsearch()
															__UpperCamelCase			:     Any         =      {"acknowledged": True}
															__UpperCamelCase			:     str         =      ElasticSearchIndex(es_client=a		)
															mocked_bulk.return_value([(True, None)] * 3		)
															index.add_documents(["foo", "bar", "foobar"]		)

															# single query
															__UpperCamelCase			:     List[Any]         =      "foo"
															__UpperCamelCase			:     Tuple         =      {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
															__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      index.search(a		)
															self.assertEqual(scores[0]  ,     1		)
															self.assertEqual(indices[0]  ,     0		)

															# single query with timeout
															__UpperCamelCase			:     Any         =      "foo"
															__UpperCamelCase			:     Union[str, Any]         =      {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
															__UpperCamelCase     ,		__UpperCamelCase			:     int         =      index.search(a  ,     request_timeout=3_0		)
															self.assertEqual(scores[0]  ,     1		)
															self.assertEqual(indices[0]  ,     0		)

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

															# batched queries with timeout
															__UpperCamelCase			:     Optional[int]         =      ["foo", "bar", "foobar"]
															__UpperCamelCase			:     Dict         =      {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
															__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      index.search_batch(a  ,     request_timeout=3_0		)
															__UpperCamelCase			:     List[str]         =      [scores[0] for scores in total_scores]
															__UpperCamelCase			:     List[str]         =      [indices[0] for indices in total_indices]
															self.assertGreater(np.min(a		)  ,     0		)
															self.assertListEqual([1, 1, 1]  ,     a		)

from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
		from .deepspeed import (
		    DeepSpeedEngineWrapper,
		    DeepSpeedOptimizerWrapper,
		    DeepSpeedSchedulerWrapper,
		    DummyOptim,
		    DummyScheduler,
		    HfDeepSpeedConfig,
		)

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers