
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							=       pytest.mark.integration






@require_faiss
class   UpperCAmelCase		(						__a ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	List[Any]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Optional[int]:


          '''simple docstring'''


          import faiss

          snake_case       :   Dataset							=      self._create_dummy_dataset()
          snake_case       :   Any							=      dset.map(
              lambda snake_case__    ,						snake_case__ : {"vecs": i * np.ones(5    ,						dtype=np.floataa )}    ,						with_indices=a_    ,						keep_in_memory=a_ )
          snake_case       :   List[Any]							=      dset.add_faiss_index("vecs"    ,						batch_size=1_00    ,						metric_type=faiss.METRIC_INNER_PRODUCT )
          snake_case       :   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 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	List[str]:


          '''simple docstring'''


          import faiss

          snake_case       :   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    ,						)
          snake_case       :   Any							=      dset.get_nearest_examples("vecs"    ,						np.ones(5    ,						dtype=np.floataa ) )
          self.assertEqual(examples["filename"][0]    ,						"my_name-train_29" )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Any:


          '''simple docstring'''


          import faiss

          snake_case       :   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 )

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	str:


          '''simple docstring'''


          snake_case       :   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 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	List[str]:


          '''simple docstring'''


          from elasticsearch import Elasticsearch

          snake_case       :   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:
                 snake_case       :   Tuple							=      {"""acknowledged""": True}
                 mocked_bulk.return_value([(True, None)] * 30 )
                 snake_case       :   List[str]							=      {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 29}]}}
                 snake_case       :   Dict							=      Elasticsearch()

                 dset.add_elasticsearch_index("filename"    ,						es_client=a_ )
                 snake_case       :   int							=      dset.get_nearest_examples("filename"    ,						"my_name-train_29" )
                 self.assertEqual(examples["filename"][0]    ,						"my_name-train_29" )






@require_faiss
class   UpperCAmelCase		(						__a ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Optional[Any]:


          '''simple docstring'''


          import faiss

          snake_case       :   int							=      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
          snake_case       :   List[Any]							=      np.zeros(5    ,						dtype=np.floataa )
          snake_case       :   List[str]							=      1
          snake_case       :   str							=      index.search(a_ )
          self.assertRaises(a_    ,						index.search    ,						query.reshape(-1    ,						1 ) )
          self.assertGreater(scores[0]    ,						0 )
          self.assertEqual(indices[0]    ,						1 )

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Dict:


          '''simple docstring'''


          import faiss

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Optional[Any]:


          '''simple docstring'''


          import faiss

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Tuple:


          '''simple docstring'''


          import faiss

          snake_case       :   List[str]							=      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 )
                 snake_case       :   str							=      FaissIndex.load(tmp_file.name )
          os.unlink(tmp_file.name )

          snake_case       :   int							=      np.zeros(5    ,						dtype=np.floataa )
          snake_case       :   List[str]							=      1
          snake_case       :   List[Any]							=      index.search(a_ )
          self.assertGreater(scores[0]    ,						0 )
          self.assertEqual(indices[0]    ,						1 )



@require_faiss
def 			UpperCamelCase					(    __lowerCamelCase     : List[str]    ):
       import faiss

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

       snake_case       :   Any							=      """index.faiss"""
       snake_case       :   int							=      f"""mock://{index_name}"""
       index.save(snake_case__				,					storage_options=mockfs.storage_options    )
       snake_case       :   Optional[Any]							=      FaissIndex.load(snake_case__				,					storage_options=mockfs.storage_options    )

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






@require_elasticsearch
class   UpperCAmelCase		(						__a ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[int]:


          '''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:
                 snake_case       :   Tuple							=      Elasticsearch()
                 snake_case       :   Union[str, Any]							=      {"""acknowledged""": True}
                 snake_case       :   List[str]							=      ElasticSearchIndex(es_client=a_ )
                 mocked_bulk.return_value([(True, None)] * 3 )
                 index.add_documents(["foo", "bar", "foobar"] )

                 # single query
                 snake_case       :   str							=      """foo"""
                 snake_case       :   Dict							=      {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
                 snake_case       :   Tuple							=      index.search(a_ )
                 self.assertEqual(scores[0]    ,						1 )
                 self.assertEqual(indices[0]    ,						0 )

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

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

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











"""simple docstring"""


import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					["image_processor", "tokenizer"]
 lowercase__ 					=					"AutoImageProcessor"
 lowercase__ 					=					"AutoTokenizer"


 def __init__(				self:		List[str],      a_:		List[str]=None,      a_:		Tuple=None,      **a_:		Tuple		):




   '''simple docstring'''



   _snake_case							:		str								=  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_,      )
     _snake_case							:		str								=  kwargs.pop("""feature_extractor"""		)

   _snake_case							:		Union[str, Any]								=  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_		)
   _snake_case							:		Dict								=  self.image_processor
   _snake_case							:		Any								=  False


 def __call__(				self:		Any,      *a_:		Any,      **a_:		Tuple		):




   '''simple docstring'''



   if self._in_target_context_manager:
     return self.current_processor(*a_,      **a_		)

   _snake_case							:		Dict								=  kwargs.pop("""images""",      a_		)
   _snake_case							:		Optional[Any]								=  kwargs.pop("""text""",      a_		)
   if len(a_		) > 0:
     _snake_case							:		Optional[int]								=  args[0]
     _snake_case							:		Tuple								=  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:
     _snake_case							:		Tuple								=  self.image_processor(a_,      *a_,      **a_		)
   if text is not None:
     _snake_case							:		Tuple								=  self.tokenizer(a_,      **a_		)

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


 def  UpperCamelCase_   (				self:		Optional[int],      *a_:		Tuple,      **a_:		List[str]		):




   '''simple docstring'''



   return self.tokenizer.batch_decode(*a_,      **a_		)


 def  UpperCamelCase_   (				self:		int,      *a_:		List[str],      **a_:		int		):




   '''simple docstring'''



   return self.tokenizer.decode(*a_,      **a_		)


 @contextmanager
 def  UpperCamelCase_   (				self:		Dict		):




   '''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."""		)
   _snake_case							:		Any								=  True
   _snake_case							:		Optional[int]								=  self.tokenizer
   yield
   _snake_case							:		int								=  self.image_processor
   _snake_case							:		Optional[int]								=  False


 def  UpperCamelCase_   (				self:		Dict,      a_:		Optional[Any],      a_:		str=False,      a_:		Optional[Any]=None		):




   '''simple docstring'''



   if added_vocab is None:
     _snake_case							:		Dict								=  self.tokenizer.get_added_vocab()

   _snake_case							:		str								=  {}

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

       _snake_case							:		Any								=  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  UpperCamelCase_   (				self:		Optional[int]		):




   '''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  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



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


