




import copy

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


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       {
    """SenseTime/deformable-detr""": """https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json""",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}







class 						lowercase							(   __UpperCamelCase		):
     _a							 =						"deformable_detr"
     _a							 =						{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }

     def __init__(  self		,     _a=True		,     _a=None		,     _a=3		,     _a=300		,     _a=1024		,     _a=6		,     _a=1024		,     _a=8		,     _a=6		,     _a=1024		,     _a=8		,     _a=0.0		,     _a=True		,     _a="relu"		,     _a=256		,     _a=0.1		,     _a=0.0		,     _a=0.0		,     _a=0.02		,     _a=1.0		,     _a=True		,     _a=False		,     _a="sine"		,     _a="resnet50"		,     _a=True		,     _a=False		,     _a=4		,     _a=4		,     _a=4		,     _a=False		,     _a=300		,     _a=False		,     _a=1		,     _a=5		,     _a=2		,     _a=1		,     _a=1		,     _a=5		,     _a=2		,     _a=0.1		,     _a=0.25		,     _a=False		,     **_a		,     )						->							int:
           if backbone_config is not None and use_timm_backbone:
                 raise ValueError("""You can\'t specify both `backbone_config` and `use_timm_backbone`."""				)

           if not use_timm_backbone:
                 if backbone_config is None:
                       logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""				)
                       _A							:     Optional[Any]			=						CONFIG_MAPPING['resnet'](out_features=["""stage4"""]				)
                 elif isinstance(_a		,     _a				):
                       _A							:     Optional[int]			=						backbone_config.get("""model_type"""				)
                       _A							:     int			=						CONFIG_MAPPING[backbone_model_type]
                       _A							:     Union[str, Any]			=						config_class.from_dict(_a				)
           _A							:     Optional[Any]			=						use_timm_backbone
           _A							:     List[Any]			=						backbone_config
           _A							:     Union[str, Any]			=						num_channels
           _A							:     str			=						num_queries
           _A							:     Any			=						max_position_embeddings
           _A							:     Optional[Any]			=						d_model
           _A							:     List[str]			=						encoder_ffn_dim
           _A							:     Optional[Any]			=						encoder_layers
           _A							:     Tuple			=						encoder_attention_heads
           _A							:     Optional[int]			=						decoder_ffn_dim
           _A							:     List[Any]			=						decoder_layers
           _A							:     Optional[Any]			=						decoder_attention_heads
           _A							:     str			=						dropout
           _A							:     Any			=						attention_dropout
           _A							:     Any			=						activation_dropout
           _A							:     Tuple			=						activation_function
           _A							:     Union[str, Any]			=						init_std
           _A							:     str			=						init_xavier_std
           _A							:     Optional[int]			=						encoder_layerdrop
           _A							:     Union[str, Any]			=						auxiliary_loss
           _A							:     List[Any]			=						position_embedding_type
           _A							:     List[Any]			=						backbone
           _A							:     int			=						use_pretrained_backbone
           _A							:     List[str]			=						dilation
           # deformable attributes
           _A							:     Any			=						num_feature_levels
           _A							:     Optional[int]			=						encoder_n_points
           _A							:     Union[str, Any]			=						decoder_n_points
           _A							:     Union[str, Any]			=						two_stage
           _A							:     str			=						two_stage_num_proposals
           _A							:     Union[str, Any]			=						with_box_refine
           if two_stage is True and with_box_refine is False:
                 raise ValueError("""If two_stage is True, with_box_refine must be True."""				)
           # Hungarian matcher
           _A							:     int			=						class_cost
           _A							:     str			=						bbox_cost
           _A							:     Optional[int]			=						giou_cost
           # Loss coefficients
           _A							:     int			=						mask_loss_coefficient
           _A							:     int			=						dice_loss_coefficient
           _A							:     Tuple			=						bbox_loss_coefficient
           _A							:     Union[str, Any]			=						giou_loss_coefficient
           _A							:     Any			=						eos_coefficient
           _A							:     Any			=						focal_alpha
           _A							:     Tuple			=						disable_custom_kernels
           super().__init__(is_encoder_decoder=_a		,     **_a				)

     @property
     def 					a__	(  self				)						->							int:
           return self.encoder_attention_heads

     @property
     def 					a__	(  self				)						->							int:
           return self.d_model



     def 					a__	(  self				)						->							List[Any]:
           _A							:     int			=						copy.deepcopy(self.__dict__				)
           if self.backbone_config is not None:
                 _A							:     Union[str, Any]			=						self.backbone_config.to_dict()
           _A							:     Dict			=						self.__class__.model_type
           return output








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				:       str	   =				pytest.mark.integration

@require_faiss
class    lowerCAmelCase							(					__UpperCamelCase			):
 def      A_	(      self  :      List[Any]	)			->	Union[str, Any]:
     lowerCamelCase__    :      List[Any]					 =		Dataset.from_dict({'filename': ['my_name-train' + '_' + str(UpperCAmelCase	) for x in np.arange(30	).tolist()]}	)
     return dset
 def      A_	(      self  :      Optional[Any]	)			->	Optional[int]:
     import faiss

     lowerCamelCase__    :      Dataset					 =		self._create_dummy_dataset()
     lowerCamelCase__    :      List[Any]					 =		dset.map(
         lambda UpperCAmelCase			,     UpperCAmelCase	: {"vecs": i * np.ones(5			,     dtype=np.floataa	)}			,     with_indices=UpperCAmelCase			,     keep_in_memory=UpperCAmelCase	)
     lowerCamelCase__    :      Tuple					 =		dset.add_faiss_index('vecs'			,     batch_size=100			,     metric_type=faiss.METRIC_INNER_PRODUCT	)
     lowerCamelCase__ ,							lowerCamelCase__    :      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      A_	(      self  :      Union[str, Any]	)			->	int:
     import faiss

     lowerCamelCase__    :      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			,     )
     lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		dset.get_nearest_examples('vecs'			,     np.ones(5			,     dtype=np.floataa	)	)
     self.assertEqual(examples['filename'][0]			,     'my_name-train_29'	)
 def      A_	(      self  :      List[str]	)			->	Tuple:
     import faiss

     lowerCamelCase__    :      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=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	)

     lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		dset.get_nearest_examples('vecs2'			,     np.ones(5			,     dtype=np.floataa	)	)
     self.assertEqual(examples['filename'][0]			,     'my_name-train_29'	)
 def      A_	(      self  :      Any	)			->	Optional[Any]:
     lowerCamelCase__    :      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(UpperCAmelCase			,     partial(dset.get_nearest_examples			,     'vecs2'			,     np.ones(5			,     dtype=np.floataa	)	)	)







 def      A_	(      self  :      Dict	)			->	Dict:
     from elasticsearch import Elasticsearch

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

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


@require_faiss
class    lowerCAmelCase							(					__UpperCamelCase			):
 def      A_	(      self  :      Any	)			->	Dict:
     import faiss

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

     # batched queries
     lowerCamelCase__    :      str					 =		np.eye(5			,     dtype=np.floataa	)[::-1]
     lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		index.search_batch(UpperCAmelCase	)
     self.assertRaises(UpperCAmelCase			,     index.search_batch			,     queries[0]	)
     lowerCamelCase__    :      str					 =		[scores[0] for scores in total_scores]
     lowerCamelCase__    :      List[str]					 =		[indices[0] for indices in total_indices]
     self.assertGreater(np.min(UpperCAmelCase	)			,     0	)
     self.assertListEqual([4, 3, 2, 1, 0]			,     UpperCAmelCase	)
 def      A_	(      self  :      List[Any]	)			->	List[Any]:
     import faiss

     lowerCamelCase__    :      Any					 =		FaissIndex(string_factory='Flat'	)
     index.add_vectors(np.eye(5			,     dtype=np.floataa	)	)
     self.assertIsInstance(index.faiss_index			,     faiss.IndexFlat	)
     lowerCamelCase__    :      Tuple					 =		FaissIndex(string_factory='LSH'	)
     index.add_vectors(np.eye(5			,     dtype=np.floataa	)	)
     self.assertIsInstance(index.faiss_index			,     faiss.IndexLSH	)
     with self.assertRaises(UpperCAmelCase	):
         lowerCamelCase__    :      List[str]					 =		FaissIndex(string_factory='Flat'			,     custom_index=faiss.IndexFlat(5	)	)
 def      A_	(      self  :      List[str]	)			->	Optional[int]:
     import faiss

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







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

     lowerCamelCase__    :      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=UpperCAmelCase	) as tmp_file:
         index.save(tmp_file.name	)
         lowerCamelCase__    :      List[Any]					 =		FaissIndex.load(tmp_file.name	)
     os.unlink(tmp_file.name	)

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


@require_faiss
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Any:
    import faiss

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

    lowerCamelCase__    :      Optional[int]					 =		'index.faiss'
    lowerCamelCase__    :      Optional[Any]					 =		F"""mock://{index_name}"""
    index.save(_UpperCAmelCase						,	storage_options=mockfs.storage_options )
    lowerCamelCase__    :      Tuple					 =		FaissIndex.load(_UpperCAmelCase						,	storage_options=mockfs.storage_options )

    lowerCamelCase__    :      Optional[int]					 =		np.zeros(5						,	dtype=np.floataa )
    lowerCamelCase__    :      Dict					 =		1
    lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		index.search(_UpperCAmelCase )
    assert scores[0] > 0
    assert indices[0] == 1


@require_elasticsearch
class    lowerCAmelCase							(					__UpperCamelCase			):
 def      A_	(      self  :      Dict	)			->	List[Any]:
     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:
         lowerCamelCase__    :      Any					 =		Elasticsearch()
         lowerCamelCase__    :      Tuple					 =		{'acknowledged': True}
         lowerCamelCase__    :      Tuple					 =		ElasticSearchIndex(es_client=UpperCAmelCase	)
         mocked_bulk.return_value([(True, None)] * 3	)
         index.add_documents(['foo', 'bar', 'foobar']	)

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

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

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

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





