

'''simple docstring'''

from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

A_							    =				logging.get_logger(__name__)





@add_end_docstrings(UpperCAmelCase__    )
class 				_snake_case			(							UpperCAmelCase__    ):
   def __init__(			self      :   Any  ,**SCREAMING_SNAKE_CASE__      :   Union[str, Any]				):
     super().__init__(**__lowerCAmelCase				)
     requires_backends(self  ,"vision"				)
     requires_backends(self  ,"torch"				)

     if self.framework != "pt":
       raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''				)

     self.check_model_type(__lowerCAmelCase				)
   def      __UpperCamelCase    (			self      :   Optional[Any]  ,**SCREAMING_SNAKE_CASE__      :   Union[str, Any]				):
     SCREAMING_SNAKE_CASE:Dict       =       {}
     SCREAMING_SNAKE_CASE:Optional[int]       =       {}
     SCREAMING_SNAKE_CASE:Optional[Any]       =       {}
     # preprocess args
     if "points_per_batch" in kwargs:
       SCREAMING_SNAKE_CASE:Union[str, Any]       =       kwargs["points_per_batch"]
     if "points_per_crop" in kwargs:
       SCREAMING_SNAKE_CASE:Tuple       =       kwargs["points_per_crop"]
     if "crops_n_layers" in kwargs:
       SCREAMING_SNAKE_CASE:int       =       kwargs["crops_n_layers"]
     if "crop_overlap_ratio" in kwargs:
       SCREAMING_SNAKE_CASE:List[Any]       =       kwargs["crop_overlap_ratio"]
     if "crop_n_points_downscale_factor" in kwargs:
       SCREAMING_SNAKE_CASE:Tuple       =       kwargs["crop_n_points_downscale_factor"]
     # postprocess args
     if "pred_iou_thresh" in kwargs:
       SCREAMING_SNAKE_CASE:Optional[Any]       =       kwargs["pred_iou_thresh"]
     if "stability_score_offset" in kwargs:
       SCREAMING_SNAKE_CASE:List[str]       =       kwargs["stability_score_offset"]
     if "mask_threshold" in kwargs:
       SCREAMING_SNAKE_CASE:Optional[Any]       =       kwargs["mask_threshold"]
     if "stability_score_thresh" in kwargs:
       SCREAMING_SNAKE_CASE:Dict       =       kwargs["stability_score_thresh"]
     if "crops_nms_thresh" in kwargs:
       SCREAMING_SNAKE_CASE:Tuple       =       kwargs["crops_nms_thresh"]
     if "output_rle_mask" in kwargs:
       SCREAMING_SNAKE_CASE:Optional[Any]       =       kwargs["output_rle_mask"]
     if "output_bboxes_mask" in kwargs:
       SCREAMING_SNAKE_CASE:int       =       kwargs["output_bboxes_mask"]
     return preprocess_kwargs, forward_params, postprocess_kwargs
   def __call__(			self      :   List[Any]  ,SCREAMING_SNAKE_CASE__      :   Optional[Any]  ,*SCREAMING_SNAKE_CASE__      :   Union[str, Any]  ,SCREAMING_SNAKE_CASE__      :   Tuple=None  ,SCREAMING_SNAKE_CASE__      :   Optional[int]=None  ,**SCREAMING_SNAKE_CASE__      :   Dict				):
     return super().__call__(__lowerCAmelCase  ,*__lowerCAmelCase  ,num_workers=__lowerCAmelCase  ,batch_size=__lowerCAmelCase  ,**__lowerCAmelCase				)
   def      __UpperCamelCase    (			self      :   str  ,SCREAMING_SNAKE_CASE__      :   Tuple  ,SCREAMING_SNAKE_CASE__      :   List[str]=64  ,SCREAMING_SNAKE_CASE__      :   int = 0  ,SCREAMING_SNAKE_CASE__      :   float = 512 / 1_500  ,SCREAMING_SNAKE_CASE__      :   Optional[int] = 32  ,SCREAMING_SNAKE_CASE__      :   Optional[int] = 1  ,):
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       load_image(__lowerCAmelCase				)
     SCREAMING_SNAKE_CASE:List[str]       =       self.image_processor.size["longest_edge"]
     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:Optional[int]       =       self.image_processor.generate_crop_boxes(
         __lowerCAmelCase  ,__lowerCAmelCase  ,__lowerCAmelCase  ,__lowerCAmelCase  ,__lowerCAmelCase  ,__lowerCAmelCase				)
     SCREAMING_SNAKE_CASE:Tuple       =       self.image_processor(images=__lowerCAmelCase  ,return_tensors="pt"				)

     with self.device_placement():
       if self.framework == "pt":
         SCREAMING_SNAKE_CASE:str       =       self.get_inference_context()
         with inference_context():
           SCREAMING_SNAKE_CASE:Optional[int]       =       self._ensure_tensor_on_device(__lowerCAmelCase  ,device=self.device				)
           SCREAMING_SNAKE_CASE:Optional[Any]       =       self.model.get_image_embeddings(model_inputs.pop("pixel_values"				)				)
           SCREAMING_SNAKE_CASE:Union[str, Any]       =       image_embeddings

     SCREAMING_SNAKE_CASE:Dict       =       grid_points.shape[1]
     SCREAMING_SNAKE_CASE:int       =       points_per_batch if points_per_batch is not None else n_points

     if points_per_batch <= 0:
       raise ValueError(
           "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
           "To return all points at once, set points_per_batch to None"				)

     for i in range(0  ,__lowerCAmelCase  ,__lowerCAmelCase				):
       SCREAMING_SNAKE_CASE:Optional[Any]       =       grid_points[:, i : i + points_per_batch, :, :]
       SCREAMING_SNAKE_CASE:List[Any]       =       input_labels[:, i : i + points_per_batch]
       SCREAMING_SNAKE_CASE:Optional[int]       =       i == n_points - points_per_batch
       yield {
           "input_points": batched_points,
           "input_labels": labels,
           "input_boxes": crop_boxes,
           "is_last": is_last,
           **model_inputs,
       }
   def      __UpperCamelCase    (			self      :   Optional[int]  ,SCREAMING_SNAKE_CASE__      :   Optional[Any]  ,SCREAMING_SNAKE_CASE__      :   List[str]=0.88  ,SCREAMING_SNAKE_CASE__      :   Optional[int]=0.95  ,SCREAMING_SNAKE_CASE__      :   Dict=0  ,SCREAMING_SNAKE_CASE__      :   Optional[Any]=1  ,):
     SCREAMING_SNAKE_CASE:Tuple       =       model_inputs.pop("input_boxes"				)
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       model_inputs.pop("is_last"				)
     SCREAMING_SNAKE_CASE:str       =       model_inputs.pop("original_sizes"				).tolist()
     SCREAMING_SNAKE_CASE:int       =       model_inputs.pop("reshaped_input_sizes"				).tolist()

     SCREAMING_SNAKE_CASE:Any       =       self.model(**__lowerCAmelCase				)

     # post processing happens here in order to avoid CPU GPU copies of ALL the masks
     SCREAMING_SNAKE_CASE:List[Any]       =       model_outputs["pred_masks"]
     SCREAMING_SNAKE_CASE:List[Any]       =       self.image_processor.post_process_masks(
         __lowerCAmelCase  ,__lowerCAmelCase  ,__lowerCAmelCase  ,__lowerCAmelCase  ,binarize=__lowerCAmelCase				)
     SCREAMING_SNAKE_CASE:Optional[Any]       =       model_outputs["iou_scores"]
     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:Optional[Any]       =       self.image_processor.filter_masks(
         masks[0]  ,iou_scores[0]  ,original_sizes[0]  ,input_boxes[0]  ,__lowerCAmelCase  ,__lowerCAmelCase  ,__lowerCAmelCase  ,__lowerCAmelCase  ,)
     return {
         "masks": masks,
         "is_last": is_last,
         "boxes": boxes,
         "iou_scores": iou_scores,
     }







   def      __UpperCamelCase    (			self      :   int  ,SCREAMING_SNAKE_CASE__      :   Tuple  ,SCREAMING_SNAKE_CASE__      :   Union[str, Any]=False  ,SCREAMING_SNAKE_CASE__      :   int=False  ,SCREAMING_SNAKE_CASE__      :   Dict=0.7  ,):
     SCREAMING_SNAKE_CASE:Any       =       []
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       []
     SCREAMING_SNAKE_CASE:List[Any]       =       []
     for model_output in model_outputs:
       all_scores.append(model_output.pop("iou_scores"				)				)
       all_masks.extend(model_output.pop("masks"				)				)
       all_boxes.append(model_output.pop("boxes"				)				)

     SCREAMING_SNAKE_CASE:Tuple       =       torch.cat(__lowerCAmelCase				)
     SCREAMING_SNAKE_CASE:Dict       =       torch.cat(__lowerCAmelCase				)
     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:Dict       =       self.image_processor.post_process_for_mask_generation(
         __lowerCAmelCase  ,__lowerCAmelCase  ,__lowerCAmelCase  ,__lowerCAmelCase				)

     SCREAMING_SNAKE_CASE:Optional[int]       =       defaultdict(__lowerCAmelCase				)
     for output in model_outputs:
       for k, v in output.items():
         extra[k].append(__lowerCAmelCase				)

     SCREAMING_SNAKE_CASE:List[str]       =       {}
     if output_rle_mask:
       SCREAMING_SNAKE_CASE:List[str]       =       rle_mask

     if output_bboxes_mask:
       SCREAMING_SNAKE_CASE:Optional[Any]       =       bounding_boxes

     return {"masks": output_masks, "scores": iou_scores, **optional, **extra}












'''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


_lowercase  =			pytest.mark.integration
@require_faiss
class 						a_				(			UpperCAmelCase__						):




	def   lowercase__   (       self     :  List[Any]      ):
							__snake_case						 =   Dataset.from_dict({'filename': ['my_name-train' + '_' + str(__lowerCAmelCase      ) for x in np.arange(3_0      ).tolist()]}      )
							return dset




	def   lowercase__   (       self     :  List[str]      ):
							import faiss

							__snake_case						 =   self._create_dummy_dataset()
							__snake_case						 =   dset.map(
							    lambda __lowerCAmelCase       ,		__lowerCAmelCase      : {"vecs": i * np.ones(5       ,		dtype=np.floataa      )}       ,		with_indices=__lowerCAmelCase       ,		keep_in_memory=__lowerCAmelCase      )
							__snake_case						 =   dset.add_faiss_index('vecs'       ,		batch_size=1_0_0       ,		metric_type=faiss.METRIC_INNER_PRODUCT      )
							__snake_case ,       __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   lowercase__   (       self     :  Optional[int]      ):
							import faiss

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




	def   lowercase__   (       self     :  Dict      ):
							import faiss

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




	def   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   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(__lowerCAmelCase       ,		partial(dset.get_nearest_examples       ,		'vecs2'       ,		np.ones(5       ,		dtype=np.floataa      )      )      )




	def   lowercase__   (       self     :  List[str]      ):
							from elasticsearch import Elasticsearch

							__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:
													__snake_case						 =   {'acknowledged': True}
													mocked_bulk.return_value([(True, None)] * 3_0      )
													__snake_case						 =   {'hits': {'hits': [{'_score': 1, '_id': 2_9}]}}
													__snake_case						 =   Elasticsearch()

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





@require_faiss
class 						a_				(			UpperCAmelCase__						):




	def   lowercase__   (       self     :  Dict      ):
							import faiss

							__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       ,		1_0      )

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

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




	def   lowercase__   (       self     :  Optional[int]      ):
							import faiss

							__snake_case						 =   FaissIndex(string_factory='Flat'      )
							index.add_vectors(np.eye(5       ,		dtype=np.floataa      )      )
							self.assertIsInstance(index.faiss_index       ,		faiss.IndexFlat      )
							__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(__lowerCAmelCase      ):
													__snake_case						 =   FaissIndex(string_factory='Flat'       ,		custom_index=faiss.IndexFlat(5      )      )




	def   lowercase__   (       self     :  int      ):
							import faiss

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




	def   lowercase__   (       self     :  Tuple      ):
							import faiss

							__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=__lowerCAmelCase      ) as tmp_file:
													index.save(tmp_file.name      )
													__snake_case						 =   FaissIndex.load(tmp_file.name      )
							os.unlink(tmp_file.name      )

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





@require_faiss
def 						lowerCamelCase__   (				a							):
						import faiss

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

						__snake_case						 =   'index.faiss'
						__snake_case						 =   f'mock://{index_name}'
						index.save(a    ,	storage_options=mockfs.storage_options							)
						__snake_case						 =   FaissIndex.load(a    ,	storage_options=mockfs.storage_options							)

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






@require_elasticsearch
class 						a_				(			UpperCAmelCase__						):




	def   lowercase__   (       self     :  int      ):
							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						 =   Elasticsearch()
													__snake_case						 =   {'acknowledged': True}
													__snake_case						 =   ElasticSearchIndex(es_client=__lowerCAmelCase      )
													mocked_bulk.return_value([(True, None)] * 3      )
													index.add_documents(['foo', 'bar', 'foobar']      )

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

													# single query with timeout
													__snake_case						 =   'foo'
													__snake_case						 =   {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
													__snake_case ,       __snake_case						 =   index.search(__lowerCAmelCase       ,		request_timeout=3_0      )
													self.assertEqual(scores[0]       ,		1      )
													self.assertEqual(indices[0]       ,		0      )

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

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

