






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 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  def    _UpperCAmelCase     (     self )			-> Dict:
     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 )			-> RealmTokenizer:
     return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname		,		'realm_tokenizer' ) )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     shutil.rmtree(self.tmpdirname )




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     RealmConfig(num_block_records=self.num_block_records )
     return config




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




  def    _UpperCAmelCase     (     self )			-> Dict:
     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 )			-> Union[str, Any]:
     UpperCamelCase_									=     RealmRetriever(
         block_records=self.get_dummy_block_records()		,		tokenizer=self.get_tokenizer()		,		)
     return retriever




  def    _UpperCAmelCase     (     self )			-> str:
     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 )			-> Optional[int]:
     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 )			-> List[Any]:
     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' )













import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor


@require_tokenizers
@require_vision
class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     tempfile.mkdtemp()

     # fmt: off
     UpperCamelCase_									=     ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
     # fmt: on
     UpperCamelCase_									=     os.path.join(self.tmpdirname		,		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_									=     {
         'do_resize': True,
         'size': {'height': 18, 'width': 18},
         'do_normalize': True,
         'image_mean': [0.5, 0.5, 0.5],
         'image_std': [0.5, 0.5, 0.5],
     }
     UpperCamelCase_									=     os.path.join(self.tmpdirname		,		_UpperCAmelCase )
     with open(self.image_processor_file		,		'w'		,		encoding='utf-8' ) as fp:
        json.dump(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		**_UpperCAmelCase )			-> Optional[Any]:
     return BertTokenizer.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		**_UpperCAmelCase )			-> Optional[int]:
     return ViTImageProcessor.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Any:
     shutil.rmtree(self.tmpdirname )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     [np.random.randint(255		,		size=(3, 30, 400)		,		dtype=np.uinta )]

     UpperCamelCase_									=     [Image.fromarray(np.moveaxis(_UpperCAmelCase		,		0		,		-1 ) ) for x in image_inputs]

     return image_inputs




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.get_tokenizer()
     UpperCamelCase_									=     self.get_image_processor()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     processor.save_pretrained(self.tmpdirname )
     UpperCamelCase_									=     VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )

     self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer.get_vocab() )
     self.assertIsInstance(processor.tokenizer		,		(BertTokenizer, BertTokenizerFast) )

     self.assertEqual(processor.image_processor.to_json_string()		,		image_processor.to_json_string() )
     self.assertIsInstance(processor.image_processor		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     VisionTextDualEncoderProcessor(
         tokenizer=self.get_tokenizer()		,		image_processor=self.get_image_processor() )
     processor.save_pretrained(self.tmpdirname )

     UpperCamelCase_									=     self.get_tokenizer(bos_token='(BOS)'		,		eos_token='(EOS)' )
     UpperCamelCase_									=     self.get_image_processor(do_normalize=_UpperCAmelCase		,		padding_value=1.0 )

     UpperCamelCase_									=     VisionTextDualEncoderProcessor.from_pretrained(
         self.tmpdirname		,		bos_token='(BOS)'		,		eos_token='(EOS)'		,		do_normalize=_UpperCAmelCase		,		padding_value=1.0 )

     self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer_add_kwargs.get_vocab() )
     self.assertIsInstance(processor.tokenizer		,		(BertTokenizer, BertTokenizerFast) )

     self.assertEqual(processor.image_processor.to_json_string()		,		image_processor_add_kwargs.to_json_string() )
     self.assertIsInstance(processor.image_processor		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     image_processor(_UpperCAmelCase		,		return_tensors='np' )
     UpperCamelCase_									=     processor(images=_UpperCAmelCase		,		return_tensors='np' )

     for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()		,		input_processor[key].sum()		,		delta=1e-2 )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'

     UpperCamelCase_									=     processor(text=_UpperCAmelCase )

     UpperCamelCase_									=     tokenizer(_UpperCAmelCase )

     for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]		,		encoded_processor[key] )




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'
     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     processor(text=_UpperCAmelCase		,		images=_UpperCAmelCase )

     self.assertListEqual(list(inputs.keys() )		,		['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )

     # test if it raises when no input is passed
     with self.assertRaises(_UpperCAmelCase ):
        processor()




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     UpperCamelCase_									=     processor.batch_decode(_UpperCAmelCase )
     UpperCamelCase_									=     tokenizer.batch_decode(_UpperCAmelCase )

     self.assertListEqual(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'
     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     processor(text=_UpperCAmelCase		,		images=_UpperCAmelCase )

     self.assertListEqual(list(inputs.keys() )		,		processor.model_input_names )





