





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 			__UpperCamelCase    (							A__    ):





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   tempfile.mkdtemp()
        _UpperCAmelCase           =   5

        # Realm tok
        _UpperCAmelCase           =   [
            '''[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''',
        ]
        _UpperCAmelCase           =   os.path.join(self.tmpdirname				,							'''realm_tokenizer'''					)
        os.makedirs(_UpperCamelCase				,							exist_ok=_UpperCamelCase					)
        _UpperCAmelCase           =   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]					)					)

        _UpperCAmelCase           =   os.path.join(self.tmpdirname				,							'''realm_block_records'''					)
        os.makedirs(_UpperCamelCase				,							exist_ok=_UpperCamelCase					)





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





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





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   RealmConfig(num_block_records=self.num_block_records					)
        return config





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





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   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 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   RealmRetriever(
            block_records=self.get_dummy_block_records()				,							tokenizer=self.get_tokenizer()				,							)
        return retriever





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.get_config()
        _UpperCAmelCase           =   self.get_dummy_retriever()
        _UpperCAmelCase           =   retriever.tokenizer

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

        _UpperCAmelCase       ,      _UpperCAmelCase       ,      _UpperCAmelCase       ,      _UpperCAmelCase           =   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 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.get_config()
        _UpperCAmelCase           =   self.get_dummy_retriever()
        _UpperCAmelCase           =   retriever.tokenizer

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

        _UpperCAmelCase       ,      _UpperCAmelCase       ,      _UpperCAmelCase       ,      _UpperCAmelCase           =   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 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.get_dummy_retriever()
        retriever.save_pretrained(os.path.join(self.tmpdirname				,							'''realm_block_records'''					)					)

        # Test local path
        _UpperCAmelCase           =   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:
           _UpperCAmelCase           =   os.path.join(
               os.path.join(self.tmpdirname				,							'''realm_block_records'''					)				,							_REALM_BLOCK_RECORDS_FILENAME					)
           _UpperCAmelCase           =   RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa'''					)

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


'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_a					:			List[Any]							=    logging.get_logger(__name__)



class 							lowercase_						(		a			):

  '''simple docstring'''





  def __init__(  self ,			*a_ ,			**a_  )				->					None:


    """simple docstring"""
    warnings.warn(
        'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
        ' use DonutImageProcessor instead.' ,			a_ ,			)
    super().__init__(*a_ ,			**a_  )


