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


_UpperCAmelCase            =		pytest.mark.integration





@require_faiss
class __a						( __A  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    int         =				Dataset.from_dict({'filename': ['my_name-train' + '_' + str(lowercase ) for x in np.arange(3_0 ).tolist()]} )
						return dset
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						import faiss

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



						"""simple docstring"""





						import faiss

						A_		:    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		,     )
						A_		:    Optional[Any]         =				dset.get_nearest_examples('vecs'		,     np.ones(5		,     dtype=np.floataa ) )
						self.assertEqual(examples['filename'][0]		,     'my_name-train_29' )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						import faiss

						A_		:    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=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_		:    Dict         =				dset.get_nearest_examples('vecs2'		,     np.ones(5		,     dtype=np.floataa ) )
						self.assertEqual(examples['filename'][0]		,     'my_name-train_29' )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    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(lowercase		,     partial(dset.get_nearest_examples		,     'vecs2'		,     np.ones(5		,     dtype=np.floataa ) ) )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						from elasticsearch import Elasticsearch

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

								dset.add_elasticsearch_index('filename'		,     es_client=lowercase )
								A_		:    Optional[int]         =				dset.get_nearest_examples('filename'		,     'my_name-train_29' )
								self.assertEqual(examples['filename'][0]		,     'my_name-train_29' )





@require_faiss
class __a						( __A  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						import faiss

						A_		:    List[str]         =				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_		:    Any         =				np.zeros(5		,     dtype=np.floataa )
						A_		:    Dict         =				1
						A_		:    List[str]         =				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_		:    Union[str, Any]         =				np.eye(5		,     dtype=np.floataa )[::-1]
						A_		:    Union[str, Any]         =				index.search_batch(lowercase )
						self.assertRaises(lowercase		,     index.search_batch		,     queries[0] )
						A_		:    Optional[Any]         =				[scores[0] for scores in total_scores]
						A_		:    Tuple         =				[indices[0] for indices in total_indices]
						self.assertGreater(np.min(lowercase )		,     0 )
						self.assertListEqual([4, 3, 2, 1, 0]		,     lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						import faiss

						A_		:    int         =				FaissIndex(string_factory='Flat' )
						index.add_vectors(np.eye(5		,     dtype=np.floataa ) )
						self.assertIsInstance(index.faiss_index		,     faiss.IndexFlat )
						A_		:    Union[str, 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 ):
								A_		:    Union[str, Any]         =				FaissIndex(string_factory='Flat'		,     custom_index=faiss.IndexFlat(5 ) )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						import faiss

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







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						import faiss

						A_		:    Any         =				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_		:    Union[str, Any]         =				FaissIndex.load(tmp_file.name )
						os.unlink(tmp_file.name )

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




@require_faiss
def     UpperCamelCase						(	__lowercase						:			List[str]     ):
		'''simple docstring'''




		import faiss

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

		A_		:    List[str]         =				'index.faiss'
		A_		:    Dict         =				f'''mock://{index_name}'''
		index.save(__lowercase						,storage_options=mockfs.storage_options     )
		A_		:    Union[str, Any]         =				FaissIndex.load(__lowercase						,storage_options=mockfs.storage_options     )

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





@require_elasticsearch
class __a						( __A  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						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_		:    int         =				Elasticsearch()
								A_		:    List[Any]         =				{'acknowledged': True}
								A_		:    Tuple         =				ElasticSearchIndex(es_client=lowercase )
								mocked_bulk.return_value([(True, None)] * 3 )
								index.add_documents(['foo', 'bar', 'foobar'] )

								# single query
								A_		:    Tuple         =				'foo'
								A_		:    str         =				{'hits': {'hits': [{'_score': 1, '_id': 0}]}}
								A_		:    Dict         =				index.search(lowercase )
								self.assertEqual(scores[0]		,     1 )
								self.assertEqual(indices[0]		,     0 )

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

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

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







import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCAmelCase            =		{
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

_UpperCAmelCase            =		logging.get_logger(__name__)





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = '''mask2former'''
				lowerCamelCase_    = ['''swin''']
				lowerCamelCase_    = {'''hidden_size''': '''hidden_dim'''}
				def __init__(						self		,     lowercase = None		,     lowercase = 2_5_6		,     lowercase = 2_5_6		,     lowercase = 2_5_6		,     lowercase = 1_0_2_4		,     lowercase = "relu"		,     lowercase = 6		,     lowercase = 1_0		,     lowercase = 8		,     lowercase = 0.0		,     lowercase = 2_0_4_8		,     lowercase = False		,     lowercase = False		,     lowercase = 4		,     lowercase = 2_5_5		,     lowercase = 1_0_0		,     lowercase = 0.1		,     lowercase = 2.0		,     lowercase = 5.0		,     lowercase = 5.0		,     lowercase = 1_2_5_4_4		,     lowercase = 3.0		,     lowercase = 0.75		,     lowercase = 0.02		,     lowercase = 1.0		,     lowercase = True		,     lowercase = [4, 8, 1_6, 3_2]		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						if backbone_config is None:
								logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.' )
								A_		:    List[str]         =				CONFIG_MAPPING['swin'](
								    image_size=2_2_4		,     in_channels=3		,     patch_size=4		,     embed_dim=9_6		,     depths=[2, 2, 1_8, 2]		,     num_heads=[3, 6, 1_2, 2_4]		,     window_size=7		,     drop_path_rate=0.3		,     use_absolute_embeddings=lowercase		,     out_features=['stage1', 'stage2', 'stage3', 'stage4']		,     )

						if isinstance(lowercase		,     lowercase ):
								A_		:    str         =				backbone_config.pop('model_type' )
								A_		:    List[str]         =				CONFIG_MAPPING[backbone_model_type]
								A_		:    Tuple         =				config_class.from_dict(lowercase )

						# verify that the backbone is supported
						if backbone_config.model_type not in self.backbones_supported:
								logger.warning_once(
								    F'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
								    F'''Supported model types: {','.join(self.backbones_supported )}''' )

						A_		:    List[Any]         =				backbone_config
						A_		:    Optional[Any]         =				feature_size
						A_		:    int         =				mask_feature_size
						A_		:    Tuple         =				hidden_dim
						A_		:    Dict         =				encoder_feedforward_dim
						A_		:    int         =				activation_function
						A_		:    str         =				encoder_layers
						A_		:    Tuple         =				decoder_layers
						A_		:    Tuple         =				num_attention_heads
						A_		:    str         =				dropout
						A_		:    List[str]         =				dim_feedforward
						A_		:    List[str]         =				pre_norm
						A_		:    Tuple         =				enforce_input_projection
						A_		:    Dict         =				common_stride
						A_		:    Union[str, Any]         =				ignore_value
						A_		:    List[Any]         =				num_queries
						A_		:    List[Any]         =				no_object_weight
						A_		:    int         =				class_weight
						A_		:    int         =				mask_weight
						A_		:    Optional[Any]         =				dice_weight
						A_		:    int         =				train_num_points
						A_		:    Optional[int]         =				oversample_ratio
						A_		:    Tuple         =				importance_sample_ratio
						A_		:    Union[str, Any]         =				init_std
						A_		:    List[Any]         =				init_xavier_std
						A_		:    Optional[Any]         =				use_auxiliary_loss
						A_		:    Dict         =				feature_strides
						A_		:    List[Any]         =				output_auxiliary_logits
						A_		:    Optional[int]         =				decoder_layers

						super().__init__(**lowercase )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     lowercase		,     **lowercase ):



						"""simple docstring"""





						return cls(
						    backbone_config=lowercase		,     **lowercase		,     )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				copy.deepcopy(self.__dict__ )
						A_		:    Optional[int]         =				self.backbone_config.to_dict()
						A_		:    Dict         =				self.__class__.model_type
						return output






