'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__a:					Optional[Any]			  =  logging.get_logger(__name__)


if is_vision_available():
  import PIL



class      UpperCAmelCase    (		a__   ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE   						=       ["pixel_values"]

       def __init__(		self      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = PILImageResampling.BICUBIC      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase = 1 / 255      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = True      ,  **__lowerCAmelCase      ,  )    ->    str:
        super().__init__(**_snake_case					)
        lowercase__							:       List[str]										=   size if size is not None else {'''shortest_edge''': 224}
        lowercase__							:       Optional[Any]										=   get_size_dict(_snake_case      ,  default_to_square=_snake_case					)
        lowercase__							:       Union[str, Any]										=   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        lowercase__							:       str										=   get_size_dict(_snake_case      ,  default_to_square=_snake_case      ,  param_name='''crop_size'''					)

        lowercase__							:       List[Any]										=   do_resize
        lowercase__							:       Any										=   size
        lowercase__							:       Tuple										=   resample
        lowercase__							:       Dict										=   do_center_crop
        lowercase__							:       Optional[Any]										=   crop_size
        lowercase__							:       List[Any]										=   do_rescale
        lowercase__							:       Tuple										=   rescale_factor
        lowercase__							:       List[Any]										=   do_normalize
        lowercase__							:       str										=   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        lowercase__							:       List[Any]										=   image_std if image_std is not None else OPENAI_CLIP_STD
        lowercase__							:       Dict										=   do_convert_rgb

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase = PILImageResampling.BICUBIC      ,  __lowerCAmelCase = None      ,  **__lowerCAmelCase      ,  )    ->    int:
        lowercase__							:       Dict										=   get_size_dict(_snake_case      ,  default_to_square=_snake_case					)
        if "shortest_edge" not in size:
         raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""					)
        lowercase__							:       Tuple										=   get_resize_output_image_size(_snake_case      ,  size=size['''shortest_edge''']      ,  default_to_square=_snake_case					)
        return resize(_snake_case      ,  size=_snake_case      ,  resample=_snake_case      ,  data_format=_snake_case      ,  **_snake_case					)

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None      ,  **__lowerCAmelCase      ,  )    ->    Dict:
        lowercase__							:       str										=   get_size_dict(_snake_case					)
        if "height" not in size or "width" not in size:
         raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""					)
        return center_crop(_snake_case      ,  size=(size['''height'''], size['''width'''])      ,  data_format=_snake_case      ,  **_snake_case					)

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None      ,  **__lowerCAmelCase      ,  )    ->    int:
        return rescale(_snake_case      ,  scale=_snake_case      ,  data_format=_snake_case      ,  **_snake_case					)

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None      ,  **__lowerCAmelCase      ,  )    ->    Dict:
        return normalize(_snake_case      ,  mean=_snake_case      ,  std=_snake_case      ,  data_format=_snake_case      ,  **_snake_case					)


       def _lowerCAmelCase(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = ChannelDimension.FIRST      ,  **__lowerCAmelCase      ,  )    ->    Any:
        lowercase__							:       Optional[int]										=   do_resize if do_resize is not None else self.do_resize
        lowercase__							:       Any										=   size if size is not None else self.size
        lowercase__							:       int										=   get_size_dict(_snake_case      ,  param_name='''size'''      ,  default_to_square=_snake_case					)
        lowercase__							:       List[Any]										=   resample if resample is not None else self.resample
        lowercase__							:       str										=   do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase__							:       Any										=   crop_size if crop_size is not None else self.crop_size
        lowercase__							:       List[Any]										=   get_size_dict(_snake_case      ,  param_name='''crop_size'''      ,  default_to_square=_snake_case					)
        lowercase__							:       str										=   do_rescale if do_rescale is not None else self.do_rescale
        lowercase__							:       str										=   rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase__							:       Optional[Any]										=   do_normalize if do_normalize is not None else self.do_normalize
        lowercase__							:       Any										=   image_mean if image_mean is not None else self.image_mean
        lowercase__							:       Any										=   image_std if image_std is not None else self.image_std
        lowercase__							:       Any										=   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        lowercase__							:       str										=   make_list_of_images(_snake_case					)

        if not valid_images(_snake_case					):
         raise ValueError(
             '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
             '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

        if do_resize and size is None:
         raise ValueError('''Size must be specified if do_resize is True.'''					)

        if do_center_crop and crop_size is None:
         raise ValueError('''Crop size must be specified if do_center_crop is True.'''					)

        if do_rescale and rescale_factor is None:
         raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

        if do_normalize and (image_mean is None or image_std is None):
         raise ValueError('''Image mean and std must be specified if do_normalize is True.'''					)

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
         lowercase__							:       Optional[Any]										=   [convert_to_rgb(_snake_case					) for image in images]

        # All transformations expect numpy arrays.
        lowercase__							:       List[str]										=   [to_numpy_array(_snake_case					) for image in images]

        if do_resize:
         lowercase__							:       Optional[int]										=   [self.resize(image=_snake_case      ,  size=_snake_case      ,  resample=_snake_case					) for image in images]

        if do_center_crop:
         lowercase__							:       Optional[int]										=   [self.center_crop(image=_snake_case      ,  size=_snake_case					) for image in images]

        if do_rescale:
         lowercase__							:       Optional[Any]										=   [self.rescale(image=_snake_case      ,  scale=_snake_case					) for image in images]

        if do_normalize:
         lowercase__							:       Dict										=   [self.normalize(image=_snake_case      ,  mean=_snake_case      ,  std=_snake_case					) for image in images]

        lowercase__							:       Any										=   [to_channel_dimension_format(_snake_case      ,  _snake_case					) for image in images]

        lowercase__							:       Union[str, Any]										=   {'''pixel_values''': images}
        return BatchFeature(data=_snake_case      ,  tensor_type=_snake_case					)








"""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


__UpperCamelCase     :						Optional[int]						       =  pytest.mark.integration







@require_faiss
class       a							(   a__  ):



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= Dataset.from_dict({'filename': ['my_name-train' + '_' + str(_snake_case     ) for x in np.arange(30     ).tolist()]}     )
  return dset



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  import faiss

  lowerCAmelCase    				= self._create_dummy_dataset()
  lowerCAmelCase    				= dset.map(
      lambda _snake_case				,       _snake_case     : {"vecs": i * np.ones(5				,       dtype=np.floataa     )}				,       with_indices=_snake_case				,       keep_in_memory=_snake_case     )
  lowerCAmelCase    				= dset.add_faiss_index('vecs'				,       batch_size=1_00				,       metric_type=faiss.METRIC_INNER_PRODUCT     )
  lowerCAmelCase				,lowerCAmelCase    				= 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     ):







  """simple docstring"""






  import faiss

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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  import faiss

  lowerCAmelCase    				= 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     )

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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= 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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  from elasticsearch import Elasticsearch

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

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







@require_faiss
class       a							(   a__  ):



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  import faiss

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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  import faiss

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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  import faiss

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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  import faiss

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

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


@require_faiss
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Dict			):
 import faiss

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

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

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







@require_elasticsearch
class       a							(   a__  ):



 def    UpperCamelCase__  (     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:
   lowerCAmelCase    				= Elasticsearch()
   lowerCAmelCase    				= {'acknowledged': True}
   lowerCAmelCase    				= ElasticSearchIndex(es_client=_snake_case     )
   mocked_bulk.return_value([(True, None)] * 3     )
   index.add_documents(['foo', 'bar', 'foobar']     )

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

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

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





