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					:   Dict							=		pytest.mark.integration


@require_faiss
class 						lowercase    (     a     ):




							def 	__snake_case(						self   :					Tuple      )					->				Tuple:



												'''simple docstring'''

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




							def 	__snake_case(						self   :					Any      )					->				int:



												'''simple docstring'''

												import faiss

												SCREAMING_SNAKE_CASE    			=       self._create_dummy_dataset()
												SCREAMING_SNAKE_CASE    			=       dset.map(
												    lambda _UpperCamelCase	,							_UpperCamelCase      : {"vecs": i * np.ones(5	,							dtype=np.floataa      )}	,							with_indices=_UpperCamelCase	,							keep_in_memory=_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       dset.add_faiss_index("vecs"	,							batch_size=100	,							metric_type=faiss.METRIC_INNER_PRODUCT      )
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       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   :					Any      )					->				Dict:



												'''simple docstring'''

												import faiss

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




							def 	__snake_case(						self   :					List[Any]      )					->				int:



												'''simple docstring'''

												import faiss

												SCREAMING_SNAKE_CASE    			=       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      )

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       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]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       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   :					str      )					->				Dict:



												'''simple docstring'''

												from elasticsearch import Elasticsearch

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

																	dset.add_elasticsearch_index("filename"	,							es_client=_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       dset.get_nearest_examples("filename"	,							"my_name-train_29"      )
																	self.assertEqual(examples["filename"][0]	,							"my_name-train_29"      )



@require_faiss
class 						lowercase    (     a     ):




							def 	__snake_case(						self   :					Optional[Any]      )					->				Optional[int]:



												'''simple docstring'''

												import faiss

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

												# batched queries
												SCREAMING_SNAKE_CASE    			=       np.eye(5	,							dtype=np.floataa      )[::-1]
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       index.search_batch(_UpperCamelCase      )
												self.assertRaises(_UpperCamelCase	,							index.search_batch	,							queries[0]      )
												SCREAMING_SNAKE_CASE    			=       [scores[0] for scores in total_scores]
												SCREAMING_SNAKE_CASE    			=       [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   :					List[str]      )					->				Tuple:



												'''simple docstring'''

												import faiss

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




							def 	__snake_case(						self   :					str      )					->				Tuple:



												'''simple docstring'''

												import faiss

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





							def 	__snake_case(						self   :					Optional[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												import faiss

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

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





@require_faiss
def 	__lowerCamelCase							(UpperCAmelCase__     :							Union[str, Any]  ):
					import faiss

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

					SCREAMING_SNAKE_CASE    			=       "index.faiss"
					SCREAMING_SNAKE_CASE    			=       F"mock://{index_name}"
					index.save(UpperCAmelCase__      , storage_options=mockfs.storage_options  )
					SCREAMING_SNAKE_CASE    			=       FaissIndex.load(UpperCAmelCase__      , storage_options=mockfs.storage_options  )

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


@require_elasticsearch
class 						lowercase    (     a     ):




							def 	__snake_case(						self   :					Optional[int]      )					->				Union[str, Any]:



												'''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:
																	SCREAMING_SNAKE_CASE    			=       Elasticsearch()
																	SCREAMING_SNAKE_CASE    			=       {"acknowledged": True}
																	SCREAMING_SNAKE_CASE    			=       ElasticSearchIndex(es_client=_UpperCamelCase      )
																	mocked_bulk.return_value([(True, None)] * 3      )
																	index.add_documents(["foo", "bar", "foobar"]      )

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

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

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

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





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment


class 						lowercase    (     unittest.TestCase     ):




							def 	__snake_case(						self   :					Union[str, Any]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       inspect.getfile(accelerate.test_utils      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_script.py"]      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(
												    mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_distributed_data_loop.py"]      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_ops.py"]      )




							@require_multi_gpu
							def 	__snake_case(						self   :					Optional[int]      )					->				Any:



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices."      )
												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
												with patch_environment(omp_num_threads=1      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )




							@require_multi_gpu
							def 	__snake_case(						self   :					List[Any]      )					->				int:



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices."      )
												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.operation_file_path]
												print(F"Command: {cmd}"      )
												with patch_environment(omp_num_threads=1      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )




							@require_multi_gpu
							def 	__snake_case(						self   :					int      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__      )]
												with patch_environment(omp_num_threads=1      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )





							@require_multi_gpu
							def 	__snake_case(						self   :					int      )					->				int:



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices, using 2 devices only"      )
												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.data_loop_file_path]
												with patch_environment(omp_num_threads=1	,							cuda_visible_devices="0,1"      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )


if __name__ == "__main__":
							_lowerCamelCase					:   str							=		Accelerator()
							_lowerCamelCase					:   List[str]							=		(accelerator.state.process_index + 2, 10)
							_lowerCamelCase					:   str							=		torch.randint(0, 10, shape).to(accelerator.device)

							_lowerCamelCase					:   Optional[Any]							=		''''''

							_lowerCamelCase					:   str							=		accelerator.pad_across_processes(tensor)
							if tensora.shape[0] != accelerator.state.num_processes + 1:
														error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
							if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
														error_msg += "Tensors have different values."
							if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
														error_msg += "Padding was not done with the right value (0)."

							_lowerCamelCase					:   Any							=		accelerator.pad_across_processes(tensor, pad_first=True)
							if tensora.shape[0] != accelerator.state.num_processes + 1:
														error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
							_lowerCamelCase					:   int							=		accelerator.state.num_processes - accelerator.state.process_index - 1
							if not torch.equal(tensora[index:], tensor):
														error_msg += "Tensors have different values."
							if not torch.all(tensora[:index] == 0):
														error_msg += "Padding was not done with the right value (0)."

							# Raise error at the end to make sure we don't stop at the first failure.
							if len(error_msg) > 0:
														raise ValueError(error_msg)




