



"""simple docstring"""




import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		["image_processor", "tokenizer"]
				__UpperCamelCase    						=		"AutoImageProcessor"
				__UpperCamelCase    						=		"AutoTokenizer"





				def __init__( self	,				_a=None	,				_a=None	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
										    """ instead."""	,				_a	,				)
										lowerCamelCase       =      kwargs.pop("""feature_extractor"""				)

							lowerCamelCase       =      image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
										raise ValueError("""You need to specify an `image_processor`."""				)
							if tokenizer is None:
										raise ValueError("""You need to specify a `tokenizer`."""				)

							super().__init__(_a	,				_a				)
							lowerCamelCase       =      self.image_processor
							lowerCamelCase       =      False





				def __call__( self	,				*_a	,				**_a				):





							"""simple docstring"""
							# For backward compatibility
							if self._in_target_context_manager:
										return self.current_processor(*_a	,				**_a				)

							lowerCamelCase       =      kwargs.pop("""images"""	,				_a				)
							lowerCamelCase       =      kwargs.pop("""text"""	,				_a				)
							if len(_a				) > 0:
										lowerCamelCase       =      args[0]
										lowerCamelCase       =      args[1:]

							if images is None and text is None:
										raise ValueError("""You need to specify either an `images` or `text` input to process."""				)

							if images is not None:
										lowerCamelCase       =      self.image_processor(_a	,				*_a	,				**_a				)
							if text is not None:
										lowerCamelCase       =      self.tokenizer(_a	,				**_a				)

							if text is None:
										return inputs
							elif images is None:
										return encodings
							else:
										lowerCamelCase       =      encodings["""input_ids"""]
										return inputs





				def      _lowerCAmelCase       ( self	,				*_a	,				**_a				):





							"""simple docstring"""
							return self.tokenizer.batch_decode(*_a	,				**_a				)





				def      _lowerCAmelCase       ( self	,				*_a	,				**_a				):





							"""simple docstring"""
							return self.tokenizer.decode(*_a	,				**_a				)





				@contextmanager
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							warnings.warn(
							    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
							    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
							    """your images inputs, or in a separate call."""				)
							lowerCamelCase       =      True
							lowerCamelCase       =      self.tokenizer
							yield
							lowerCamelCase       =      self.image_processor
							lowerCamelCase       =      False





				def      _lowerCAmelCase       ( self	,				_a	,				_a=False	,				_a=None				):





							"""simple docstring"""
							if added_vocab is None:
										lowerCamelCase       =      self.tokenizer.get_added_vocab()

							lowerCamelCase       =      {}

							while tokens:
										lowerCamelCase       =      re.search(r"""<s_(.*?)>"""	,				_a	,				re.IGNORECASE				)
										if start_token is None:
													break
										lowerCamelCase       =      start_token.group(1				)
										lowerCamelCase       =      re.search(rf'</s_{key}>'	,				_a	,				re.IGNORECASE				)
										lowerCamelCase       =      start_token.group()
										if end_token is None:
													lowerCamelCase       =      tokens.replace(_a	,				""""""				)
										else:
													lowerCamelCase       =      end_token.group()
													lowerCamelCase       =      re.escape(_a				)
													lowerCamelCase       =      re.escape(_a				)
													lowerCamelCase       =      re.search(f'{start_token_escaped}(.*?){end_token_escaped}'	,				_a	,				re.IGNORECASE				)
													if content is not None:
																lowerCamelCase       =      content.group(1				).strip()
																if r"<s_" in content and r"</s_" in content:  # non-leaf node
																			lowerCamelCase       =      self.tokenajson(_a	,				is_inner_value=_a	,				added_vocab=_a				)
																			if value:
																						if len(_a				) == 1:
																									lowerCamelCase       =      value[0]
																						lowerCamelCase       =      value
																else:  # leaf nodes
																			lowerCamelCase       =      []
																			for leaf in content.split(r"""<sep/>"""				):
																						lowerCamelCase       =      leaf.strip()
																						if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																									lowerCamelCase       =      leaf[1:-2]  # for categorical special tokens
																						output[key].append(_a				)
																			if len(output[key]				) == 1:
																						lowerCamelCase       =      output[key][0]

													lowerCamelCase       =      tokens[tokens.find(_a				) + len(_a				) :].strip()
													if tokens[:6] == r"<sep/>":  # non-leaf nodes
																return [output] + self.tokenajson(tokens[6:]	,				is_inner_value=_a	,				added_vocab=_a				)

							if len(_a				):
										return [output] if is_inner_value else output
							else:
										return [] if is_inner_value else {"text_sequence": tokens}





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							warnings.warn(
							    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""	,				_a	,				)
							return self.image_processor_class





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							warnings.warn(
							    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""	,				_a	,				)
							return self.image_processor









"""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							:    List[Any]  							=							pytest.mark.integration






@require_faiss
class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      Dataset.from_dict({"""filename""": ["""my_name-train""" + """_""" + str(_a				) for x in np.arange(30				).tolist()]}				)
							return dset





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							import faiss

							lowerCamelCase       =      self._create_dummy_dataset()
							lowerCamelCase       =      dset.map(
							    lambda _a	,				_a				: {"vecs": i * np.ones(5	,				dtype=np.floataa				)}	,				with_indices=_a	,				keep_in_memory=_a				)
							lowerCamelCase       =      dset.add_faiss_index("""vecs"""	,				batch_size=100	,				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      _lowerCAmelCase       ( 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=100	,				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      _lowerCAmelCase       ( 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=_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				)

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





				def      _lowerCAmelCase       ( 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(_a	,				partial(dset.get_nearest_examples	,				"""vecs2"""	,				np.ones(5	,				dtype=np.floataa				)				)				)





				def      _lowerCAmelCase       ( 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=_a				)
										lowerCamelCase   ,     lowerCamelCase       =      dset.get_nearest_examples("""filename"""	,				"""my_name-train_29"""				)
										self.assertEqual(examples["""filename"""][0]	,				"""my_name-train_29"""				)






@require_faiss
class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( 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(_a				)
							self.assertRaises(_a	,				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(_a				)
							self.assertRaises(_a	,				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(_a				)	,				0				)
							self.assertListEqual([4, 3, 2, 1, 0]	,				_a				)





				def      _lowerCAmelCase       ( 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(_a				):
										lowerCamelCase       =      FaissIndex(string_factory="""Flat"""	,				custom_index=faiss.IndexFlat(5				)				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							import faiss

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





				def      _lowerCAmelCase       ( 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=_a				) 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(_a				)
							self.assertGreater(scores[0]	,				0				)
							self.assertEqual(indices[0]	,				1				)




@require_faiss
def 					a__				(       snake_case__       )		->      Tuple:
			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(snake_case__ ,				storage_options=mockfs.storage_options       )
			lowerCamelCase       =      FaissIndex.load(snake_case__ ,				storage_options=mockfs.storage_options       )

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






@require_elasticsearch
class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( 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=_a				)
										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(_a				)
										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(_a	,				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(_a				)
										lowerCamelCase       =      [scores[0] for scores in total_scores]
										lowerCamelCase       =      [indices[0] for indices in total_indices]
										self.assertGreater(np.min(_a				)	,				0				)
										self.assertListEqual([1, 1, 1]	,				_a				)

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



