



"""simple docstring"""


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


snake_case_    					=	pytest.mark.integration







@require_faiss
class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






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






						def      UpperCAmelCase__					( self			:int )    ->      Tuple:
												import faiss

												UpperCAmelCase					=			self._create_dummy_dataset()
												UpperCAmelCase					=			dset.map(
												    lambda lowercase_       ,  lowercase_ : {"vecs": i * np.ones(5       ,  dtype=np.floataa )}       ,  with_indices=lowercase_       ,  keep_in_memory=lowercase_ )
												UpperCAmelCase					=			dset.add_faiss_index('vecs'       ,  batch_size=1_00       ,  metric_type=faiss.METRIC_INNER_PRODUCT )
												UpperCAmelCase				,   UpperCAmelCase					=			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			:Dict )    ->      Union[str, Any]:
												import faiss

												UpperCAmelCase					=			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=1_00       ,  metric_type=faiss.METRIC_INNER_PRODUCT       ,  )
												UpperCAmelCase				,   UpperCAmelCase					=			dset.get_nearest_examples('vecs'       ,  np.ones(5       ,  dtype=np.floataa ) )
												self.assertEqual(examples['filename'][0]       ,  'my_name-train_29' )






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

												UpperCAmelCase					=			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 )

												UpperCAmelCase				,   UpperCAmelCase					=			dset.get_nearest_examples('vecs2'       ,  np.ones(5       ,  dtype=np.floataa ) )
												self.assertEqual(examples['filename'][0]       ,  'my_name-train_29' )






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

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

																		dset.add_elasticsearch_index('filename'       ,  es_client=lowercase_ )
																		UpperCAmelCase				,   UpperCAmelCase					=			dset.get_nearest_examples('filename'       ,  'my_name-train_29' )
																		self.assertEqual(examples['filename'][0]       ,  'my_name-train_29' )







@require_faiss
class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






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

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

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






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

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






						def      UpperCAmelCase__					( self			:str )    ->      str:
												import faiss

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






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

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

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


@require_faiss
def 			_lowerCAmelCase				(       lowercase_						):
						import faiss

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

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

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







@require_elasticsearch
class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:List[str] )    ->      List[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					=			Elasticsearch()
																		UpperCAmelCase					=			{'acknowledged': True}
																		UpperCAmelCase					=			ElasticSearchIndex(es_client=lowercase_ )
																		mocked_bulk.return_value([(True, None)] * 3 )
																		index.add_documents(['foo', 'bar', 'foobar'] )

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

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

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

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










"""simple docstring"""


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


snake_case_    					=	{
    """facebook/maskformer-swin-base-ade""": (
        """https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

snake_case_    					=	logging.get_logger(__name__)







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							"""maskformer"""
						__UpperCamelCase		    =							{"""hidden_size""": """mask_feature_size"""}
						__UpperCamelCase		    =							["""resnet""", """swin"""]
						__UpperCamelCase		    =							["""detr"""]






						def __init__( self			:Dict       ,  lowercase_			:int = 2_56       ,  lowercase_			:int = 2_56       ,  lowercase_			:float = 0.1       ,  lowercase_			:bool = False       ,  lowercase_			:Optional[Dict] = None       ,  lowercase_			:Optional[Dict] = None       ,  lowercase_			:float = 0.02       ,  lowercase_			:float = 1.0       ,  lowercase_			:float = 1.0       ,  lowercase_			:float = 1.0       ,  lowercase_			:float = 20.0       ,  lowercase_			:Optional[bool] = None       ,  **lowercase_			:List[str]       ,  )    ->      Optional[int]:
												if backbone_config is None:
																		# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
																		UpperCAmelCase					=			SwinConfig(
																		    image_size=3_84       ,  in_channels=3       ,  patch_size=4       ,  embed_dim=1_28       ,  depths=[2, 2, 18, 2]       ,  num_heads=[4, 8, 16, 32]       ,  window_size=12       ,  drop_path_rate=0.3       ,  out_features=['stage1', 'stage2', 'stage3', 'stage4']       ,  )

												if isinstance(lowercase_       ,  lowercase_ ):
																		UpperCAmelCase					=			backbone_config.pop('model_type' )
																		UpperCAmelCase					=			CONFIG_MAPPING[backbone_model_type]
																		UpperCAmelCase					=			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 MaskFormer. """
																		    f"""Supported model types: {','.join(self.backbones_supported )}""" )

												if decoder_config is None:
																		# fall back to https://huggingface.co/facebook/detr-resnet-50
																		UpperCAmelCase					=			DetrConfig()
												else:
																		# verify that the decoder is supported
																		UpperCAmelCase					=			(
																		    decoder_config.pop('model_type' ) if isinstance(lowercase_       ,  lowercase_ ) else decoder_config.model_type
																		)
																		if decoder_type not in self.decoders_supported:
																								raise ValueError(
																								    f"""Transformer Decoder {decoder_type} not supported, please use one of"""
																								    f""" {','.join(self.decoders_supported )}""" )
																		if isinstance(lowercase_       ,  lowercase_ ):
																								UpperCAmelCase					=			CONFIG_MAPPING[decoder_type]
																								UpperCAmelCase					=			config_class.from_dict(lowercase_ )

												UpperCAmelCase					=			backbone_config
												UpperCAmelCase					=			decoder_config
												# main feature dimension for the model
												UpperCAmelCase					=			fpn_feature_size
												UpperCAmelCase					=			mask_feature_size
												# initializer
												UpperCAmelCase					=			init_std
												UpperCAmelCase					=			init_xavier_std
												# Hungarian matcher && loss
												UpperCAmelCase					=			cross_entropy_weight
												UpperCAmelCase					=			dice_weight
												UpperCAmelCase					=			mask_weight
												UpperCAmelCase					=			use_auxiliary_loss
												UpperCAmelCase					=			no_object_weight
												UpperCAmelCase					=			output_auxiliary_logits

												UpperCAmelCase					=			self.decoder_config.encoder_attention_heads
												UpperCAmelCase					=			self.decoder_config.num_hidden_layers
												super().__init__(**lowercase_ )






						@classmethod
						def      UpperCAmelCase__					( cls			:int       ,  lowercase_			:PretrainedConfig       ,  lowercase_			:PretrainedConfig       ,  **lowercase_			:int )    ->      List[Any]:
												return cls(
												    backbone_config=lowercase_       ,  decoder_config=lowercase_       ,  **lowercase_       ,  )






						def      UpperCAmelCase__					( self			:Tuple )    ->      Dict[str, any]:
												UpperCAmelCase					=			copy.deepcopy(self.__dict__ )
												UpperCAmelCase					=			self.backbone_config.to_dict()
												UpperCAmelCase					=			self.decoder_config.to_dict()
												UpperCAmelCase					=			self.__class__.model_type
												return output





