

import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer







class 				lowercase       ( lowercase_    ):




    def 					a							(      self ):
           snake_case_           =					tempfile.mkdtemp()
           snake_case_           =					5

           # Realm tok
           snake_case_           =					[
               '[UNK]',
               '[CLS]',
               '[SEP]',
               '[PAD]',
               '[MASK]',
               'test',
               'question',
               'this',
               'is',
               'the',
               'first',
               'second',
               'third',
               'fourth',
               'fifth',
               'record',
               'want',
               '##want',
               '##ed',
               'wa',
               'un',
               'runn',
               '##ing',
               ',',
               'low',
               'lowest',
           ]
           snake_case_           =					os.path.join(self.tmpdirname   ,     'realm_tokenizer' )
           os.makedirs(UpperCAmelCase__   ,     exist_ok=UpperCAmelCase__ )
           snake_case_           =					os.path.join(UpperCAmelCase__   ,     VOCAB_FILES_NAMES['vocab_file'] )
           with open(self.vocab_file   ,     'w'   ,     encoding='utf-8' ) as vocab_writer:
                  vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )

           snake_case_           =					os.path.join(self.tmpdirname   ,     'realm_block_records' )
           os.makedirs(UpperCAmelCase__   ,     exist_ok=UpperCAmelCase__ )




    def 					a							(      self ):
           return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,     'realm_tokenizer' ) )




    def 					a							(      self ):
           shutil.rmtree(self.tmpdirname )




    def 					a							(      self ):
           snake_case_           =					RealmConfig(num_block_records=self.num_block_records )
           return config




    def 					a							(      self ):
           snake_case_           =					Dataset.from_dict(
               {
                   'id': ['0', '1'],
                   'question': ['foo', 'bar'],
                   'answers': [['Foo', 'Bar'], ['Bar']],
               } )
           return dataset




    def 					a							(      self ):
           snake_case_           =					np.array(
               [
                   b'This is the first record',
                   b'This is the second record',
                   b'This is the third record',
                   b'This is the fourth record',
                   b'This is the fifth record',
                   b'This is a longer longer longer record',
               ]   ,     dtype=UpperCAmelCase__   ,     )
           return block_records




    def 					a							(      self ):
           snake_case_           =					RealmRetriever(
               block_records=self.get_dummy_block_records()   ,     tokenizer=self.get_tokenizer()   ,     )
           return retriever




    def 					a							(      self ):
           snake_case_           =					self.get_config()
           snake_case_           =					self.get_dummy_retriever()
           snake_case_           =					retriever.tokenizer

           snake_case_           =					np.array([0, 3]   ,     dtype='long' )
           snake_case_           =					tokenizer(['Test question'] ).input_ids
           snake_case_           =					tokenizer(
               ['the fourth']   ,     add_special_tokens=UpperCAmelCase__   ,     return_token_type_ids=UpperCAmelCase__   ,     return_attention_mask=UpperCAmelCase__   ,     ).input_ids
           snake_case_           =					config.reader_seq_len

           snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_           =					retriever(
               UpperCAmelCase__   ,     UpperCAmelCase__   ,     answer_ids=UpperCAmelCase__   ,     max_length=UpperCAmelCase__   ,     return_tensors='np' )

           self.assertEqual(len(UpperCAmelCase__ )   ,     2 )
           self.assertEqual(len(UpperCAmelCase__ )   ,     2 )
           self.assertEqual(len(UpperCAmelCase__ )   ,     2 )
           self.assertEqual(concat_inputs.input_ids.shape   ,     (2, 10) )
           self.assertEqual(concat_inputs.attention_mask.shape   ,     (2, 10) )
           self.assertEqual(concat_inputs.token_type_ids.shape   ,     (2, 10) )
           self.assertEqual(concat_inputs.special_tokens_mask.shape   ,     (2, 10) )
           self.assertEqual(
               tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] )   ,     ['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'first', 'record', '[SEP]']   ,     )
           self.assertEqual(
               tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] )   ,     ['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'fourth', 'record', '[SEP]']   ,     )




    def 					a							(      self ):
           snake_case_           =					self.get_config()
           snake_case_           =					self.get_dummy_retriever()
           snake_case_           =					retriever.tokenizer

           snake_case_           =					np.array([0, 3, 5]   ,     dtype='long' )
           snake_case_           =					tokenizer(['Test question'] ).input_ids
           snake_case_           =					tokenizer(
               ['the fourth', 'longer longer']   ,     add_special_tokens=UpperCAmelCase__   ,     return_token_type_ids=UpperCAmelCase__   ,     return_attention_mask=UpperCAmelCase__   ,     ).input_ids
           snake_case_           =					config.reader_seq_len

           snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_           =					retriever(
               UpperCAmelCase__   ,     UpperCAmelCase__   ,     answer_ids=UpperCAmelCase__   ,     max_length=UpperCAmelCase__   ,     return_tensors='np' )

           self.assertEqual([False, True, True]   ,     UpperCAmelCase__ )
           self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]   ,     UpperCAmelCase__ )
           self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]   ,     UpperCAmelCase__ )




    def 					a							(      self ):
           snake_case_           =					self.get_dummy_retriever()
           retriever.save_pretrained(os.path.join(self.tmpdirname   ,     'realm_block_records' ) )

           # Test local path
           snake_case_           =					retriever.from_pretrained(os.path.join(self.tmpdirname   ,     'realm_block_records' ) )
           self.assertEqual(retriever.block_records[0]   ,     b'This is the first record' )

           # Test mocked remote path
           with patch('transformers.models.realm.retrieval_realm.hf_hub_download' ) as mock_hf_hub_download:
                  snake_case_           =					os.path.join(
                      os.path.join(self.tmpdirname   ,     'realm_block_records' )   ,     _REALM_BLOCK_RECORDS_FILENAME )
                  snake_case_           =					RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa' )

           self.assertEqual(retriever.block_records[0]   ,     b'This is the first record' )







import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


_UpperCAmelCase	:     Any		       = logging.get_logger(__name__)







class 				lowercase       ( lowercase_    ):




				def __init__(      self   ,     *snake_case   ,     **snake_case ):
											warnings.warn(
											    'The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
											    ' Please use DeformableDetrImageProcessor instead.'   ,     snake_case   ,     )
											super().__init__(*snake_case   ,     **snake_case )




