






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[Any]								=pytest.mark.integration






@require_faiss
class 						UpperCAmelCase__ (							__snake_case							):







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







		def  A__      (					self		):
						import faiss

						_A	:				Dataset   =      self._create_dummy_dataset()
						_A	:				List[str]   =      dset.map(
						    lambda A__       ,A__		: {"vecs": i * np.ones(5       ,dtype=np.floataa		)}       ,with_indices=A__       ,keep_in_memory=A__		)
						_A	:				Tuple   =      dset.add_faiss_index('''vecs'''       ,batch_size=100       ,metric_type=faiss.METRIC_INNER_PRODUCT		)
						_A  ,				_A	:				List[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  A__      (					self		):
						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=100       ,metric_type=faiss.METRIC_INNER_PRODUCT       ,)
						_A  ,				_A	:				Tuple   =      dset.get_nearest_examples('''vecs'''       ,np.ones(5       ,dtype=np.floataa		)		)
						self.assertEqual(examples['''filename'''][0]       ,'''my_name-train_29'''		)







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







		def  A__      (					self		):
						_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(A__       ,partial(dset.get_nearest_examples       ,'''vecs2'''       ,np.ones(5       ,dtype=np.floataa		)		)		)







		def  A__      (					self		):
						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	:				Optional[Any]   =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
										_A	:				Optional[Any]   =      Elasticsearch()

										dset.add_elasticsearch_index('''filename'''       ,es_client=A__		)
										_A  ,				_A	:				Tuple   =      dset.get_nearest_examples('''filename'''       ,'''my_name-train_29'''		)
										self.assertEqual(examples['''filename'''][0]       ,'''my_name-train_29'''		)






@require_faiss
class 						UpperCAmelCase__ (							__snake_case							):







		def  A__      (					self		):
						import faiss

						_A	:				Union[str, 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	:				str   =      np.zeros(5       ,dtype=np.floataa		)
						_A	:				Dict   =      1
						_A  ,				_A	:				Optional[int]   =      index.search(A__		)
						self.assertRaises(A__       ,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(A__		)
						self.assertRaises(A__       ,index.search_batch       ,queries[0]		)
						_A	:				Tuple   =      [scores[0] for scores in total_scores]
						_A	:				List[str]   =      [indices[0] for indices in total_indices]
						self.assertGreater(np.min(A__		)       ,0		)
						self.assertListEqual([4, 3, 2, 1, 0]       ,A__		)







		def  A__      (					self		):
						import faiss

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







		def  A__      (					self		):
						import faiss

						_A	:				Any   =      faiss.IndexFlat(5		)
						_A	:				Any   =      FaissIndex(custom_index=A__		)
						index.add_vectors(np.eye(5       ,dtype=np.floataa		)		)
						self.assertIsInstance(index.faiss_index       ,faiss.IndexFlat		)







		def  A__      (					self		):
						import faiss

						_A	:				Optional[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=A__		) as tmp_file:
										index.save(tmp_file.name		)
										_A	:				str   =      FaissIndex.load(tmp_file.name		)
						os.unlink(tmp_file.name		)

						_A	:				Tuple   =      np.zeros(5       ,dtype=np.floataa		)
						_A	:				Tuple   =      1
						_A  ,				_A	:				Union[str, Any]   =      index.search(A__		)
						self.assertGreater(scores[0]       ,0		)
						self.assertEqual(indices[0]       ,1		)



@require_faiss
def  a__ (__lowercase							:List[Any]   )		-> int:
				import faiss

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

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

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






@require_elasticsearch
class 						UpperCAmelCase__ (							__snake_case							):







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

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

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

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

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











from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
	import PIL

_UpperCamelCase			:	Union[str, Any]								=logging.get_logger(__name__)



def  a__ (__lowercase							:str   )		-> List[List[ImageInput]]:
				if isinstance(__lowercase					,  (list, tuple)   ) and isinstance(videos[0]					,  (list, tuple)   ) and is_valid_image(videos[0][0]   ):
								return videos

				elif isinstance(__lowercase					,  (list, tuple)   ) and is_valid_image(videos[0]   ):
								return [videos]

				elif is_valid_image(__lowercase   ):
								return [[videos]]

				raise ValueError(f"""Could not make batched video from {videos}"""   )






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[int]     =   ["pixel_values"]







		def __init__(					self       ,A__ = True       ,A__ = None       ,A__ = PILImageResampling.BILINEAR       ,A__ = True       ,A__ = None       ,A__ = True       ,A__ = 1 / 255       ,A__ = True       ,A__ = True       ,A__ = None       ,A__ = None       ,**A__       ,):
						super().__init__(**A__		)
						_A	:				Tuple   =      size if size is not None else {'''shortest_edge''': 256}
						_A	:				str   =      get_size_dict(A__       ,default_to_square=A__		)
						_A	:				str   =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
						_A	:				str   =      get_size_dict(A__       ,param_name='''crop_size'''		)

						_A	:				Tuple   =      do_resize
						_A	:				Optional[Any]   =      size
						_A	:				Optional[Any]   =      do_center_crop
						_A	:				List[str]   =      crop_size
						_A	:				Dict   =      resample
						_A	:				Tuple   =      do_rescale
						_A	:				int   =      rescale_factor
						_A	:				List[str]   =      offset
						_A	:				List[Any]   =      do_normalize
						_A	:				List[Any]   =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						_A	:				List[str]   =      image_std if image_std is not None else IMAGENET_STANDARD_STD







		def  A__      (					self       ,A__       ,A__       ,A__ = PILImageResampling.BILINEAR       ,A__ = None       ,**A__       ,):
						_A	:				str   =      get_size_dict(A__       ,default_to_square=A__		)
						if "shortest_edge" in size:
										_A	:				Optional[Any]   =      get_resize_output_image_size(A__       ,size['''shortest_edge''']       ,default_to_square=A__		)
						elif "height" in size and "width" in size:
										_A	:				str   =      (size['''height'''], size['''width'''])
						else:
										raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""		)
						return resize(A__       ,size=A__       ,resample=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						_A	:				Optional[int]   =      get_size_dict(A__		)
						if "height" not in size or "width" not in size:
										raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""		)
						return center_crop(A__       ,size=(size['''height'''], size['''width'''])       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = True       ,A__ = None       ,**A__       ,):
						_A	:				Any   =      image.astype(np.floataa		)
						if offset:
										_A	:				List[str]   =      image - (scale / 2)
						return rescale(A__       ,scale=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						return normalize(A__       ,mean=A__       ,std=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = ChannelDimension.FIRST       ,):
						if do_resize and size is None or resample is None:
										raise ValueError('''Size and resample 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.'''		)

						if offset and not do_rescale:
										raise ValueError('''For offset, do_rescale must also be set to True.'''		)

						# All transformations expect numpy arrays.
						_A	:				Dict   =      to_numpy_array(A__		)

						if do_resize:
										_A	:				Any   =      self.resize(image=A__       ,size=A__       ,resample=A__		)

						if do_center_crop:
										_A	:				str   =      self.center_crop(A__       ,size=A__		)

						if do_rescale:
										_A	:				Optional[int]   =      self.rescale(image=A__       ,scale=A__       ,offset=A__		)

						if do_normalize:
										_A	:				Dict   =      self.normalize(image=A__       ,mean=A__       ,std=A__		)

						_A	:				Union[str, Any]   =      to_channel_dimension_format(A__       ,A__		)
						return image







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = ChannelDimension.FIRST       ,**A__       ,):
						_A	:				Optional[int]   =      do_resize if do_resize is not None else self.do_resize
						_A	:				List[str]   =      resample if resample is not None else self.resample
						_A	:				Union[str, Any]   =      do_center_crop if do_center_crop is not None else self.do_center_crop
						_A	:				Tuple   =      do_rescale if do_rescale is not None else self.do_rescale
						_A	:				List[str]   =      rescale_factor if rescale_factor is not None else self.rescale_factor
						_A	:				List[str]   =      offset if offset is not None else self.offset
						_A	:				Optional[int]   =      do_normalize if do_normalize is not None else self.do_normalize
						_A	:				Optional[Any]   =      image_mean if image_mean is not None else self.image_mean
						_A	:				Dict   =      image_std if image_std is not None else self.image_std

						_A	:				str   =      size if size is not None else self.size
						_A	:				int   =      get_size_dict(A__       ,default_to_square=A__		)
						_A	:				Any   =      crop_size if crop_size is not None else self.crop_size
						_A	:				Optional[int]   =      get_size_dict(A__       ,param_name='''crop_size'''		)

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

						_A	:				List[str]   =      make_batched(A__		)

						_A	:				Tuple   =      [
						    [
						        self._preprocess_image(
						            image=A__       ,do_resize=A__       ,size=A__       ,resample=A__       ,do_center_crop=A__       ,crop_size=A__       ,do_rescale=A__       ,rescale_factor=A__       ,offset=A__       ,do_normalize=A__       ,image_mean=A__       ,image_std=A__       ,data_format=A__       ,)
						        for img in video
						    ]
						    for video in videos
						]

						_A	:				Optional[int]   =      {'''pixel_values''': videos}
						return BatchFeature(data=A__       ,tensor_type=A__		)



