






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


a_		:						str             =    pytest.mark.integration

@require_faiss
class 		_snake_case  (  A__					):






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
      SCREAMING_SNAKE_CASE						= Dataset.from_dict({'filename': ['my_name-train' + '_' + str(__lowerCAmelCase) for x in np.arange(30).tolist()]})
      return dset






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
      import faiss

      SCREAMING_SNAKE_CASE						= self._create_dummy_dataset()
      SCREAMING_SNAKE_CASE						= dset.map(
          lambda a		,					a: {"vecs": i * np.ones(5		,					dtype=np.floataa)}		,					with_indices=__lowerCAmelCase		,					keep_in_memory=__lowerCAmelCase)
      SCREAMING_SNAKE_CASE						= dset.add_faiss_index('vecs'		,					batch_size=100		,					metric_type=faiss.METRIC_INNER_PRODUCT)
      SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= 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)      ->       Optional[int]:
      import faiss

      SCREAMING_SNAKE_CASE						= 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		,					)
      SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= 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:
      import faiss

      SCREAMING_SNAKE_CASE						= 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=__lowerCAmelCase) as tmp_file:
         dset.save_faiss_index('vecs'		,					tmp_file.name)
         dset.load_faiss_index('vecs2'		,					tmp_file.name)
      os.unlink(tmp_file.name)

      SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= 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)      ->       Any:
      SCREAMING_SNAKE_CASE						= 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(__lowerCAmelCase		,					partial(dset.get_nearest_examples		,					'vecs2'		,					np.ones(5		,					dtype=np.floataa)))







   def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
      from elasticsearch import Elasticsearch

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

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



@require_faiss
class 		_snake_case  (  A__					):






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
      import faiss

      SCREAMING_SNAKE_CASE						= 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
      SCREAMING_SNAKE_CASE						= np.zeros(5		,					dtype=np.floataa)
      SCREAMING_SNAKE_CASE						= 1
      SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= index.search(__lowerCAmelCase)
      self.assertRaises(__lowerCAmelCase		,					index.search		,					query.reshape(-1		,					1))
      self.assertGreater(scores[0]		,					0)
      self.assertEqual(indices[0]		,					1)

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






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
      import faiss

      SCREAMING_SNAKE_CASE						= FaissIndex(string_factory='Flat')
      index.add_vectors(np.eye(5		,					dtype=np.floataa))
      self.assertIsInstance(index.faiss_index		,					faiss.IndexFlat)
      SCREAMING_SNAKE_CASE						= FaissIndex(string_factory='LSH')
      index.add_vectors(np.eye(5		,					dtype=np.floataa))
      self.assertIsInstance(index.faiss_index		,					faiss.IndexLSH)
      with self.assertRaises(__lowerCAmelCase):
         SCREAMING_SNAKE_CASE						= FaissIndex(string_factory='Flat'		,					custom_index=faiss.IndexFlat(5))






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
      import faiss

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







   def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
      import faiss

      SCREAMING_SNAKE_CASE						= 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=__lowerCAmelCase) as tmp_file:
         index.save(tmp_file.name)
         SCREAMING_SNAKE_CASE						= FaissIndex.load(tmp_file.name)
      os.unlink(tmp_file.name)

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






@require_faiss
def 				lowerCamelCase__				(_UpperCAmelCase):
   import faiss

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

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

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







@require_elasticsearch
class 		_snake_case  (  A__					):






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
      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:
         SCREAMING_SNAKE_CASE						= Elasticsearch()
         SCREAMING_SNAKE_CASE						= {'acknowledged': True}
         SCREAMING_SNAKE_CASE						= ElasticSearchIndex(es_client=__lowerCAmelCase)
         mocked_bulk.return_value([(True, None)] * 3)
         index.add_documents(['foo', 'bar', 'foobar'])

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

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

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

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












from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 		_snake_case  (  A__					):
			_lowercase		:    Union[str, Any]			     =							['''image_processor''', '''tokenizer''']
			_lowercase		:    Optional[int]			     =							'''Pix2StructImageProcessor'''
			_lowercase		:    str			     =							('''T5Tokenizer''', '''T5TokenizerFast''')






			def __init__(       self		,					a		,					a)      ->       Any:
						SCREAMING_SNAKE_CASE						= False
						super().__init__(a		,					a)






			def __call__(       self		,					a=None		,					a = None		,					a = True		,					a = False		,					a = None		,					a = None		,					a = 2048		,					a = 0		,					a = None		,					a = None		,					a = False		,					a = False		,					a = False		,					a = False		,					a = False		,					a = True		,					a = None		,					**a		,					)      ->       BatchEncoding:
						if images is None and text is None:
									raise ValueError('You have to specify either images or text.')

						# Get only text
						if images is None and not self.image_processor.is_vqa:
									SCREAMING_SNAKE_CASE						= self.tokenizer
									SCREAMING_SNAKE_CASE						= self.tokenizer(
									    text=a		,					add_special_tokens=a		,					padding=a		,					truncation=a		,					max_length=a		,					stride=a		,					pad_to_multiple_of=a		,					return_attention_mask=a		,					return_overflowing_tokens=a		,					return_special_tokens_mask=a		,					return_offsets_mapping=a		,					return_token_type_ids=a		,					return_length=a		,					verbose=a		,					return_tensors=a		,					**a		,					)
									return text_encoding

						if not self.image_processor.is_vqa:
									# add pixel_values
									SCREAMING_SNAKE_CASE						= self.image_processor(
									    a		,					return_tensors=a		,					max_patches=a		,					**a)
						else:
									# add pixel_values and bbox
									SCREAMING_SNAKE_CASE						= self.image_processor(
									    a		,					return_tensors=a		,					max_patches=a		,					header_text=a		,					**a)

						if text is not None and not self.image_processor.is_vqa:
									SCREAMING_SNAKE_CASE						= self.tokenizer(
									    text=a		,					add_special_tokens=a		,					padding=a		,					truncation=a		,					max_length=a		,					stride=a		,					pad_to_multiple_of=a		,					return_attention_mask=a		,					return_overflowing_tokens=a		,					return_special_tokens_mask=a		,					return_offsets_mapping=a		,					return_token_type_ids=a		,					return_length=a		,					verbose=a		,					return_tensors=a		,					**a		,					)

									if "attention_mask" in text_encoding:
												SCREAMING_SNAKE_CASE						= text_encoding.pop('attention_mask')
									if "input_ids" in text_encoding:
												SCREAMING_SNAKE_CASE						= text_encoding.pop('input_ids')
						else:
									SCREAMING_SNAKE_CASE						= None

						if text_encoding is not None:
									encoding_image_processor.update(a)

						return encoding_image_processor






			def    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       List[Any]:
						return self.tokenizer.batch_decode(*a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       Optional[int]:
						return self.tokenizer.decode(*a		,					**a)







			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.tokenizer.model_input_names
						SCREAMING_SNAKE_CASE						= self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))




