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


__SCREAMING_SNAKE_CASE						:   str         =			pytest.mark.integration



@require_faiss
class        lowercase_   (					__snake_case		):



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   Dataset.from_dict({"filename": ["my_name-train" + "_" + str(lowercase_    ) for x in np.arange(30    ).tolist()]}    )
													return dset



							def 	UpperCamelCase	(						self    ):
													import faiss

													_snake_case						:					Dataset    =   self._create_dummy_dataset()
													_snake_case						:					Dict    =   dset.map(
													    lambda lowercase_				,    lowercase_    : {"vecs": i * np.ones(5				,    dtype=np.floataa    )}				,    with_indices=lowercase_				,    keep_in_memory=lowercase_    )
													_snake_case						:					Any    =   dset.add_faiss_index("vecs"				,    batch_size=100				,    metric_type=faiss.METRIC_INNER_PRODUCT    )
													_snake_case    ,_snake_case						:					List[str]    =   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    ):
													import faiss

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



							def 	UpperCamelCase	(						self    ):
													import faiss

													_snake_case						:					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=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    )

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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					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(lowercase_				,    partial(dset.get_nearest_examples				,    "vecs2"				,    np.ones(5				,    dtype=np.floataa    )    )    )



							def 	UpperCamelCase	(						self    ):
													from elasticsearch import Elasticsearch

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

																			dset.add_elasticsearch_index("filename"				,    es_client=lowercase_    )
																			_snake_case    ,_snake_case						:					Union[str, Any]    =   dset.get_nearest_examples("filename"				,    "my_name-train_29"    )
																			self.assertEqual(examples["filename"][0]				,    "my_name-train_29"    )




@require_faiss
class        lowercase_   (					__snake_case		):



							def 	UpperCamelCase	(						self    ):
													import faiss

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

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



							def 	UpperCamelCase	(						self    ):
													import faiss

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



							def 	UpperCamelCase	(						self    ):
													import faiss

													_snake_case						:					str    =   faiss.IndexFlat(5    )
													_snake_case						:					str    =   FaissIndex(custom_index=lowercase_    )
													index.add_vectors(np.eye(5				,    dtype=np.floataa    )    )
													self.assertIsInstance(index.faiss_index				,    faiss.IndexFlat    )



							def 	UpperCamelCase	(						self    ):
													import faiss

													_snake_case						:					List[str]    =   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    )
																			_snake_case						:					Optional[Any]    =   FaissIndex.load(tmp_file.name    )
													os.unlink(tmp_file.name    )

													_snake_case						:					str    =   np.zeros(5				,    dtype=np.floataa    )
													_snake_case						:					Dict    =   1
													_snake_case    ,_snake_case						:					List[str]    =   index.search(lowercase_    )
													self.assertGreater(scores[0]				,    0    )
													self.assertEqual(indices[0]				,    1    )




@require_faiss
def  snake_case						(__lowercase						)	->	List[Any]:

						'''simple docstring'''



						import faiss

						_snake_case						:					List[Any]    =   FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT						)
						index.add_vectors(np.eye(5 ,      dtype=np.floataa						)						)

						_snake_case						:					Tuple    =   "index.faiss"
						_snake_case						:					Any    =   F"""mock://{index_name}"""
						index.save(__lowercase ,      storage_options=mockfs.storage_options						)
						_snake_case						:					Dict    =   FaissIndex.load(__lowercase ,      storage_options=mockfs.storage_options						)

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



@require_elasticsearch
class        lowercase_   (					__snake_case		):



							def 	UpperCamelCase	(						self    ):
													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:
																			_snake_case						:					Optional[Any]    =   Elasticsearch()
																			_snake_case						:					Union[str, Any]    =   {"acknowledged": True}
																			_snake_case						:					List[str]    =   ElasticSearchIndex(es_client=lowercase_    )
																			mocked_bulk.return_value([(True, None)] * 3    )
																			index.add_documents(["foo", "bar", "foobar"]    )

																			# single query
																			_snake_case						:					Optional[Any]    =   "foo"
																			_snake_case						:					Union[str, Any]    =   {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
																			_snake_case    ,_snake_case						:					Optional[int]    =   index.search(lowercase_    )
																			self.assertEqual(scores[0]				,    1    )
																			self.assertEqual(indices[0]				,    0    )

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

																			# batched queries
																			_snake_case						:					Union[str, Any]    =   ["foo", "bar", "foobar"]
																			_snake_case						:					Optional[int]    =   {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
																			_snake_case    ,_snake_case						:					int    =   index.search_batch(lowercase_    )
																			_snake_case						:					Any    =   [scores[0] for scores in total_scores]
																			_snake_case						:					Optional[int]    =   [indices[0] for indices in total_indices]
																			self.assertGreater(np.min(lowercase_    )				,    0    )
																			self.assertListEqual([1, 1, 1]				,    lowercase_    )

																			# batched queries with timeout
																			_snake_case						:					int    =   ["foo", "bar", "foobar"]
																			_snake_case						:					List[str]    =   {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
																			_snake_case    ,_snake_case						:					Dict    =   index.search_batch(lowercase_				,    request_timeout=30    )
																			_snake_case						:					Optional[int]    =   [scores[0] for scores in total_scores]
																			_snake_case						:					str    =   [indices[0] for indices in total_indices]
																			self.assertGreater(np.min(lowercase_    )				,    0    )
																			self.assertListEqual([1, 1, 1]				,    lowercase_    )
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE						:   Optional[int]         =			{
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[int]         =			['DistilBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   int         =			[
				    'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DistilBertForMaskedLM',
				    'DistilBertForMultipleChoice',
				    'DistilBertForQuestionAnswering',
				    'DistilBertForSequenceClassification',
				    'DistilBertForTokenClassification',
				    'DistilBertModel',
				    'DistilBertPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Any         =			[
				    'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFDistilBertForMaskedLM',
				    'TFDistilBertForMultipleChoice',
				    'TFDistilBertForQuestionAnswering',
				    'TFDistilBertForSequenceClassification',
				    'TFDistilBertForTokenClassification',
				    'TFDistilBertMainLayer',
				    'TFDistilBertModel',
				    'TFDistilBertPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			[
				    'FlaxDistilBertForMaskedLM',
				    'FlaxDistilBertForMultipleChoice',
				    'FlaxDistilBertForQuestionAnswering',
				    'FlaxDistilBertForSequenceClassification',
				    'FlaxDistilBertForTokenClassification',
				    'FlaxDistilBertModel',
				    'FlaxDistilBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_distilbert import (
				    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    DistilBertConfig,
				    DistilBertOnnxConfig,
				)
				from .tokenization_distilbert import DistilBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_distilbert_fast import DistilBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_distilbert import (
								    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DistilBertForMaskedLM,
								    DistilBertForMultipleChoice,
								    DistilBertForQuestionAnswering,
								    DistilBertForSequenceClassification,
								    DistilBertForTokenClassification,
								    DistilBertModel,
								    DistilBertPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_distilbert import (
								    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDistilBertForMaskedLM,
								    TFDistilBertForMultipleChoice,
								    TFDistilBertForQuestionAnswering,
								    TFDistilBertForSequenceClassification,
								    TFDistilBertForTokenClassification,
								    TFDistilBertMainLayer,
								    TFDistilBertModel,
								    TFDistilBertPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_distilbert import (
								    FlaxDistilBertForMaskedLM,
								    FlaxDistilBertForMultipleChoice,
								    FlaxDistilBertForQuestionAnswering,
								    FlaxDistilBertForSequenceClassification,
								    FlaxDistilBertForTokenClassification,
								    FlaxDistilBertModel,
								    FlaxDistilBertPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)