



'''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


lowerCAmelCase: int							=						pytest.mark.integration

@require_faiss
class 							a__( lowerCamelCase__     ):







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







    def       lowercase_	(				self			:  Tuple     ):
      import faiss

      a						: Dataset					       =					self._create_dummy_dataset()
      a						: Any					       =					dset.map(
          lambda __snake_case						,      __snake_case     : {"vecs": i * np.ones(5						,      dtype=np.floataa     )}						,      with_indices=__snake_case						,      keep_in_memory=__snake_case     )
      a						: Optional[int]					       =					dset.add_faiss_index('vecs'						,      batch_size=1_00						,      metric_type=faiss.METRIC_INNER_PRODUCT     )
      a       ,     a						: Optional[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       lowercase_	(				self			:  List[str]     ):
      import faiss

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







    def       lowercase_	(				self			:  str     ):
      import faiss

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







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: 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(__snake_case						,      partial(dset.get_nearest_examples						,      'vecs2'						,      np.ones(5						,      dtype=np.floataa     )     )     )







    def       lowercase_	(				self			:  Union[str, Any]     ):
      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)] * 30     )
        a						: List[str]					       =					{'hits': {'hits': [{'_score': 1, '_id': 29}]}}
        a						: str					       =					Elasticsearch()

        dset.add_elasticsearch_index('filename'						,      es_client=__snake_case     )
        a       ,     a						: Union[str, Any]					       =					dset.get_nearest_examples('filename'						,      'my_name-train_29'     )
        self.assertEqual(examples['filename'][0]						,      'my_name-train_29'     )

@require_faiss
class 							a__( lowerCamelCase__     ):







    def       lowercase_	(				self			:  Tuple     ):
      import faiss

      a						: List[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
      a						: Dict					       =					np.zeros(5						,      dtype=np.floataa     )
      a						: int					       =					1
      a       ,     a						: Tuple					       =					index.search(__snake_case     )
      self.assertRaises(__snake_case						,      index.search						,      query.reshape(-1						,      1     )     )
      self.assertGreater(scores[0]						,      0     )
      self.assertEqual(indices[0]						,      1     )

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







    def       lowercase_	(				self			:  Optional[Any]     ):
      import faiss

      a						: List[str]					       =					FaissIndex(string_factory='Flat'     )
      index.add_vectors(np.eye(5						,      dtype=np.floataa     )     )
      self.assertIsInstance(index.faiss_index						,      faiss.IndexFlat     )
      a						: Any					       =					FaissIndex(string_factory='LSH'     )
      index.add_vectors(np.eye(5						,      dtype=np.floataa     )     )
      self.assertIsInstance(index.faiss_index						,      faiss.IndexLSH     )
      with self.assertRaises(__snake_case     ):
        a						: List[str]					       =					FaissIndex(string_factory='Flat'						,      custom_index=faiss.IndexFlat(5     )     )







    def       lowercase_	(				self			:  Optional[Any]     ):
      import faiss

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







    def       lowercase_	(				self			:  Union[str, Any]     ):
      import faiss

      a						: int					       =					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=__snake_case     ) as tmp_file:
        index.save(tmp_file.name     )
        a						: Any					       =					FaissIndex.load(tmp_file.name     )
      os.unlink(tmp_file.name     )

      a						: int					       =					np.zeros(5						,      dtype=np.floataa     )
      a						: Union[str, Any]					       =					1
      a       ,     a						: List[Any]					       =					index.search(__snake_case     )
      self.assertGreater(scores[0]						,      0     )
      self.assertEqual(indices[0]						,      1     )
@require_faiss
def 				lowerCamelCase__	(						_A    ):
  import faiss

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

  a						: List[Any]					       =					'index.faiss'
  a						: Optional[Any]					       =					f"""mock://{index_name}"""
  index.save(_A			,      storage_options=mockfs.storage_options    )
  a						: str					       =					FaissIndex.load(_A			,      storage_options=mockfs.storage_options    )

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

@require_elasticsearch
class 							a__( lowerCamelCase__     ):







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

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

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

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

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




'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase: Any							=						logging.get_logger(__name__)

class 							a__( lowerCamelCase__     ):
    lowercase__			=   ["""pixel_values"""]







    def __init__(				self			:  List[str]						,      __snake_case			:  bool = True						,      __snake_case			:  int = 32						,      __snake_case			:  Union[str, Any]=PILImageResampling.BILINEAR						,      __snake_case			:  bool = True						,      **__snake_case			:  List[Any]						,      ):
      a						: Optional[Any]					       =					do_resize
      a						: Union[str, Any]					       =					do_rescale
      a						: Union[str, Any]					       =					size_divisor
      a						: List[Any]					       =					resample
      super().__init__(**__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  np.ndarray						,      __snake_case			:  int						,      __snake_case			:  Tuple						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Tuple     ):
      a       ,     a						: Optional[int]					       =					get_image_size(__snake_case     )
      # Rounds the height and width down to the closest multiple of size_divisor
      a						: int					       =					height // size_divisor * size_divisor
      a						: int					       =					width // size_divisor * size_divisor
      a						: Any					       =					resize(__snake_case						,      (new_h, new_w)						,      resample=__snake_case						,      data_format=__snake_case						,      **__snake_case     )
      return image







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  np.ndarray						,      __snake_case			:  float						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Optional[Any]     ):
      return rescale(image=__snake_case						,      scale=__snake_case						,      data_format=__snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[int] = None						,      __snake_case			:  Dict=None						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[Union[TensorType, str]] = None						,      __snake_case			:  ChannelDimension = ChannelDimension.FIRST						,      **__snake_case			:  Any						,      ):
      a						: List[str]					       =					do_resize if do_resize is not None else self.do_resize
      a						: Tuple					       =					do_rescale if do_rescale is not None else self.do_rescale
      a						: Optional[Any]					       =					size_divisor if size_divisor is not None else self.size_divisor
      a						: Union[str, Any]					       =					resample if resample is not None else self.resample

      if do_resize and size_divisor is None:
        raise ValueError('size_divisor is required for resizing'     )

      a						: Tuple					       =					make_list_of_images(__snake_case     )

      if not valid_images(__snake_case     ):
        raise ValueError('Invalid image(s)'     )

      # All transformations expect numpy arrays.
      a						: str					       =					[to_numpy_array(__snake_case     ) for img in images]

      if do_resize:
        a						: int					       =					[self.resize(__snake_case						,      size_divisor=__snake_case						,      resample=__snake_case     ) for image in images]

      if do_rescale:
        a						: List[str]					       =					[self.rescale(__snake_case						,      scale=1 / 2_55     ) for image in images]

      a						: Any					       =					[to_channel_dimension_format(__snake_case						,      __snake_case     ) for image in images]

      a						: Any					       =					{'pixel_values': images}
      return BatchFeature(data=__snake_case						,      tensor_type=__snake_case     )