


'''simple docstring'''







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

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							pa.array(TypedSequence([1, 2, 3] ) )
            self.assertEqual(arr.type   ,						pa.intaa() )







     def a						(			self ):

            '''simple docstring'''

            with self.assertRaises(snake_case__ ):
                   _lowerCAmelCase     :				str       =							pa.array(TypedSequence([1, 2, 3] )   ,						type=pa.intaa() )







     def a						(			self ):

            '''simple docstring'''

            with self.assertRaises(snake_case__ ):
                   _lowerCAmelCase     :				Any       =							pa.array(TypedSequence([1, 2, 3]   ,						try_type=Value('bool' )   ,						type=Value('int64' ) ) )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							pa.array(TypedSequence([1, 2, 3]   ,						type=Value('int32' ) ) )
            self.assertEqual(arr.type   ,						pa.intaa() )







     def a						(			self ):

            '''simple docstring'''

            with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
                   _lowerCAmelCase     :				Dict       =							pa.array(TypedSequence(['foo', 'bar']   ,						type=Value('int64' ) ) )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							pa.array(TypedSequence([1, 2, 3]   ,						try_type=Value('int32' ) ) )
            self.assertEqual(arr.type   ,						pa.intaa() )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							pa.array(TypedSequence(['foo', 'bar']   ,						try_type=Value('int64' ) ) )
            self.assertEqual(arr.type   ,						pa.string() )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							pa.array(TypedSequence([[[1, 2, 3]]]   ,						type=ArrayaD((1, 3)   ,						'int64' ) ) )
            self.assertEqual(arr.type   ,						ArrayaDExtensionType((1, 3)   ,						'int64' ) )







     def a						(			self ):

            '''simple docstring'''

            with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
                   _lowerCAmelCase     :				Tuple       =							pa.array(TypedSequence(['foo', 'bar']   ,						type=ArrayaD((1, 3)   ,						'int64' ) ) )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							pa.array(TypedSequence([[[1, 2, 3]]]   ,						try_type=ArrayaD((1, 3)   ,						'int64' ) ) )
            self.assertEqual(arr.type   ,						ArrayaDExtensionType((1, 3)   ,						'int64' ) )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							pa.array(TypedSequence(['foo', 'bar']   ,						try_type=ArrayaD((1, 3)   ,						'int64' ) ) )
            self.assertEqual(arr.type   ,						pa.string() )







     @require_pil
     def a						(			self ):

            '''simple docstring'''

            import PIL.Image

            _lowerCAmelCase     :				int       =							PIL.Image.fromarray(np.arange(10   ,						dtype=np.uinta ).reshape(2   ,						5 ) )
            with patch(
                'datasets.arrow_writer.cast_to_python_objects'   ,						side_effect=snake_case__ ) as mock_cast_to_python_objects:
                   _lowerCAmelCase     :				Optional[int]       =							pa.array(TypedSequence([{'path': None, 'bytes': b'image_bytes'}, pil_image]   ,						type=Image() ) )
                   _lowerCAmelCase      ,  _lowerCAmelCase     :				List[Any]       =							mock_cast_to_python_objects.call_args_list[-1]
                   self.assertIn('optimize_list_casting'   ,						snake_case__ )
                   self.assertFalse(kwargs['optimize_list_casting'] )






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				str       =							pa.BufferReader(_A       ) if isinstance(_A ,    pa.Buffer       ) else pa.memory_map(_A       )
       _lowerCAmelCase     :				Union[str, Any]       =							pa.ipc.open_stream(_A       )
       _lowerCAmelCase     :				pa.Table       =							f.read_all()
       assert len(pa_table.to_batches()       ) == expected_num_chunks
       assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
       del pa_table






@pytest.mark.parametrize('writer_batch_size' ,    [None, 1, 1_0]       )
@pytest.mark.parametrize(
    'fields' ,    [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}]       )
def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Any       =							pa.BufferOutputStream()
       _lowerCAmelCase     :				List[Any]       =							pa.schema(_A       ) if fields else None
       with ArrowWriter(stream=_A ,    schema=_A ,    writer_batch_size=_A       ) as writer:
              writer.write({'col_1': 'foo', 'col_2': 1}       )
              writer.write({'col_1': 'bar', 'col_2': 2}       )
              _lowerCAmelCase      ,  _lowerCAmelCase     :				str       =							writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              _lowerCAmelCase     :				int       =							{'col_1': pa.string(), 'col_2': pa.intaa()}
       assert writer._schema == pa.schema(_A ,    metadata=writer._schema.metadata       )
       _check_output(output.getvalue() ,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1       )






def       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				Union[str, Any]       =							pa.BufferOutputStream()
       _lowerCAmelCase     :				Optional[int]       =							Features({'labels': ClassLabel(names=['neg', 'pos']       )}       )
       with ArrowWriter(stream=_A ,    features=_A       ) as writer:
              writer.write({'labels': 0}       )
              writer.write({'labels': 1}       )
              _lowerCAmelCase      ,  _lowerCAmelCase     :				int       =							writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       assert writer._schema == features.arrow_schema
       assert writer._schema.metadata == features.arrow_schema.metadata
       _lowerCAmelCase     :				List[Any]       =							pa.BufferReader(output.getvalue()       )
       _lowerCAmelCase     :				Tuple       =							pa.ipc.open_stream(_A       )
       _lowerCAmelCase     :				pa.Table       =							f.read_all()
       _lowerCAmelCase     :				Optional[int]       =							pa_table.schema
       assert pa_table.num_rows == 2
       assert schema == features.arrow_schema
       assert schema.metadata == features.arrow_schema.metadata
       assert features == Features.from_arrow_schema(_A       )






@pytest.mark.parametrize('writer_batch_size' ,    [None, 1, 1_0]       )
def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				str       =							pa.BufferOutputStream()
       with ArrowWriter(
           stream=_A ,    writer_batch_size=_A ,    hash_salt='split_name' ,    check_duplicates=_A ,    ) as writer:
              with pytest.raises(_A       ):
                     writer.write({'col_1': 'foo', 'col_2': 1} ,    key=[1, 2]       )
                     _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[Any]       =							writer.finalize()






@pytest.mark.parametrize('writer_batch_size' ,    [None, 2, 1_0]       )
def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Dict       =							pa.BufferOutputStream()
       with ArrowWriter(
           stream=_A ,    writer_batch_size=_A ,    hash_salt='split_name' ,    check_duplicates=_A ,    ) as writer:
              with pytest.raises(_A       ):
                     writer.write({'col_1': 'foo', 'col_2': 1} ,    key=1_0       )
                     writer.write({'col_1': 'bar', 'col_2': 2} ,    key=1_0       )
                     _lowerCAmelCase      ,  _lowerCAmelCase     :				Any       =							writer.finalize()






@pytest.mark.parametrize('writer_batch_size' ,    [None, 2, 1_0]       )
def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Tuple       =							pa.BufferOutputStream()
       with ArrowWriter(
           stream=_A ,    writer_batch_size=_A ,    hash_salt='split_name' ,    check_duplicates=_A ,    ) as writer:
              writer.write({'col_1': 'foo', 'col_2': 1} ,    key=1       )
              writer.write({'col_1': 'bar', 'col_2': 2} ,    key=2       )
              _lowerCAmelCase      ,  _lowerCAmelCase     :				Any       =							writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       _check_output(output.getvalue() ,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1       )






@pytest.mark.parametrize('writer_batch_size' ,    [None, 1, 1_0]       )
@pytest.mark.parametrize(
    'fields' ,    [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}]       )
def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Dict       =							pa.BufferOutputStream()
       _lowerCAmelCase     :				List[Any]       =							pa.schema(_A       ) if fields else None
       with ArrowWriter(stream=_A ,    schema=_A ,    writer_batch_size=_A       ) as writer:
              writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]}       )
              writer.write_batch({'col_1': [], 'col_2': []}       )
              _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              _lowerCAmelCase     :				Any       =							{'col_1': pa.string(), 'col_2': pa.intaa()}
       assert writer._schema == pa.schema(_A ,    metadata=writer._schema.metadata       )
       _check_output(output.getvalue() ,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1       )






@pytest.mark.parametrize('writer_batch_size' ,    [None, 1, 1_0]       )
@pytest.mark.parametrize(
    'fields' ,    [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}]       )
def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				List[str]       =							pa.BufferOutputStream()
       _lowerCAmelCase     :				Optional[Any]       =							pa.schema(_A       ) if fields else None
       with ArrowWriter(stream=_A ,    schema=_A ,    writer_batch_size=_A       ) as writer:
              writer.write_table(pa.Table.from_pydict({'col_1': ['foo', 'bar'], 'col_2': [1, 2]}       )       )
              _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[Any]       =							writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              _lowerCAmelCase     :				Union[str, Any]       =							{'col_1': pa.string(), 'col_2': pa.intaa()}
       assert writer._schema == pa.schema(_A ,    metadata=writer._schema.metadata       )
       _check_output(output.getvalue() ,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1       )






@pytest.mark.parametrize('writer_batch_size' ,    [None, 1, 1_0]       )
@pytest.mark.parametrize(
    'fields' ,    [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}]       )
def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				int       =							pa.BufferOutputStream()
       _lowerCAmelCase     :				List[Any]       =							pa.schema(_A       ) if fields else None
       with ArrowWriter(stream=_A ,    schema=_A ,    writer_batch_size=_A       ) as writer:
              writer.write_row(pa.Table.from_pydict({'col_1': ['foo'], 'col_2': [1]}       )       )
              writer.write_row(pa.Table.from_pydict({'col_1': ['bar'], 'col_2': [2]}       )       )
              _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[Any]       =							writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              _lowerCAmelCase     :				List[Any]       =							{'col_1': pa.string(), 'col_2': pa.intaa()}
       assert writer._schema == pa.schema(_A ,    metadata=writer._schema.metadata       )
       _check_output(output.getvalue() ,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1       )






def       lowercase					():







       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmp_dir:
              _lowerCAmelCase     :				Union[str, Any]       =							{'col_1': pa.string(), 'col_2': pa.intaa()}
              _lowerCAmelCase     :				Optional[Any]       =							os.path.join(_A ,    'test.arrow'       )
              with ArrowWriter(path=_A ,    schema=pa.schema(_A       )       ) as writer:
                     writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]}       )
                     _lowerCAmelCase      ,  _lowerCAmelCase     :				str       =							writer.finalize()
              assert num_examples == 2
              assert num_bytes > 0
              assert writer._schema == pa.schema(_A ,    metadata=writer._schema.metadata       )
              _check_output(_A ,    1       )






def       lowercase					(_A       ):







       """simple docstring"""







       if pa.types.is_list(_A       ):
              return get_base_dtype(arr_type.value_type       )
       else:
              return arr_type






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       if isinstance(lst[0] ,    _A       ):
              change_first_primitive_element_in_list(lst[0] ,    _A       )
       else:
              _lowerCAmelCase     :				Tuple       =							value






@pytest.mark.parametrize('optimized_int_type, expected_dtype' ,    [(None, pa.intaa()), (Value('int32'       ), pa.intaa())]       )
@pytest.mark.parametrize('sequence' ,    [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]       )
def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[int]       =							pa.array(TypedSequence(_A ,    optimized_int_type=_A       )       )
       assert get_base_dtype(arr.type       ) == expected_dtype






@pytest.mark.parametrize(
    'col, expected_dtype' ,    [
        ('attention_mask', pa.inta()),
        ('special_tokens_mask', pa.inta()),
        ('token_type_ids', pa.inta()),
        ('input_ids', pa.intaa()),
        ('other', pa.intaa()),
    ] ,    )
@pytest.mark.parametrize('sequence' ,    [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]       )
def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[int]       =							pa.array(OptimizedTypedSequence(_A ,    col=_A       )       )
       assert get_base_dtype(arr.type       ) == expected_dtype

       # not in range
       if col != "other":
              # avoids errors due to in-place modifications
              _lowerCAmelCase     :				Any       =							copy.deepcopy(_A       )
              _lowerCAmelCase     :				List[Any]       =							np.iinfo(expected_dtype.to_pandas_dtype()       ).max + 1
              change_first_primitive_element_in_list(_A ,    _A       )
              _lowerCAmelCase     :				int       =							pa.array(OptimizedTypedSequence(_A ,    col=_A       )       )
              assert get_base_dtype(arr.type       ) == pa.intaa()






@pytest.mark.parametrize('raise_exception' ,    [False, True]       )
def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Dict       =							str(tmp_path / 'dataset-train.arrow'       )
       try:
              with ArrowWriter(path=_A       ) as writer:
                     if raise_exception:
                            raise pa.lib.ArrowInvalid()
                     else:
                            writer.stream.close()
       except pa.lib.ArrowInvalid:
              pass
       finally:
              assert writer.stream.closed






def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[Any]       =							'mock://dataset-train.arrow'
       with ArrowWriter(path=_A ,    storage_options=mockfs.storage_options       ) as writer:
              assert isinstance(writer._fs ,    type(_A       )       )
              assert writer._fs.storage_options == mockfs.storage_options
              writer.write({'col_1': 'foo', 'col_2': 1}       )
              writer.write({'col_1': 'bar', 'col_2': 2}       )
              _lowerCAmelCase      ,  _lowerCAmelCase     :				List[Any]       =							writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       assert mockfs.exists(_A       )






def       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				Any       =							pa.BufferOutputStream()
       with ParquetWriter(stream=_A       ) as writer:
              writer.write({'col_1': 'foo', 'col_2': 1}       )
              writer.write({'col_1': 'bar', 'col_2': 2}       )
              _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[Any]       =							writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       _lowerCAmelCase     :				Optional[Any]       =							pa.BufferReader(output.getvalue()       )
       _lowerCAmelCase     :				pa.Table       =							pq.read_table(_A       )
       assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}






@require_pil
@pytest.mark.parametrize('embed_local_files' ,    [False, True]       )
def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       import PIL.Image

       _lowerCAmelCase     :				str       =							str(tmp_path / 'test_image_rgb.jpg'       )
       PIL.Image.fromarray(np.zeros((5, 5) ,    dtype=np.uinta       )       ).save(_A ,    format='png'       )
       _lowerCAmelCase     :				str       =							pa.BufferOutputStream()
       with ParquetWriter(
           stream=_A ,    features=Features({'image': Image()}       ) ,    embed_local_files=_A       ) as writer:
              writer.write({'image': image_path}       )
              writer.finalize()
       _lowerCAmelCase     :				Dict       =							pa.BufferReader(output.getvalue()       )
       _lowerCAmelCase     :				pa.Table       =							pq.read_table(_A       )
       _lowerCAmelCase     :				int       =							pa_table.to_pydict()
       if embed_local_files:
              assert isinstance(out['image'][0]['path'] ,    _A       )
              with open(_A ,    'rb'       ) as f:
                     assert out["image"][0]["bytes"] == f.read()
       else:
              assert out["image"][0]["path"] == image_path
              assert out["image"][0]["bytes"] is None






def       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				Tuple       =							pa.schema([pa.field('col_1' ,    pa.string() ,    nullable=_A       )]       )
       _lowerCAmelCase     :				Union[str, Any]       =							pa.BufferOutputStream()
       with ArrowWriter(stream=_A       ) as writer:
              writer._build_writer(inferred_schema=_A       )
              assert writer._schema == pa.schema([pa.field('col_1' ,    pa.string()       )]       )





'''simple docstring'''







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__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							tempfile.mkdtemp()
            _lowerCAmelCase     :				List[str]       =							5

            # Realm tok
            _lowerCAmelCase     :				str       =							[
                '[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',
            ]
            _lowerCAmelCase     :				Union[str, Any]       =							os.path.join(self.tmpdirname   ,						'realm_tokenizer' )
            os.makedirs(snake_case__   ,						exist_ok=snake_case__ )
            _lowerCAmelCase     :				Dict       =							os.path.join(snake_case__   ,						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] ) )

            _lowerCAmelCase     :				List[str]       =							os.path.join(self.tmpdirname   ,						'realm_block_records' )
            os.makedirs(snake_case__   ,						exist_ok=snake_case__ )







     def a						(			self ):

            '''simple docstring'''

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







     def a						(			self ):

            '''simple docstring'''

            shutil.rmtree(self.tmpdirname )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							RealmConfig(num_block_records=self.num_block_records )
            return config







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							Dataset.from_dict(
                {
                    'id': ['0', '1'],
                    'question': ['foo', 'bar'],
                    'answers': [['Foo', 'Bar'], ['Bar']],
                } )
            return dataset







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							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=snake_case__   ,						)
            return block_records







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							RealmRetriever(
                block_records=self.get_dummy_block_records()   ,						tokenizer=self.get_tokenizer()   ,						)
            return retriever







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.get_config()
            _lowerCAmelCase     :				List[Any]       =							self.get_dummy_retriever()
            _lowerCAmelCase     :				Any       =							retriever.tokenizer

            _lowerCAmelCase     :				Tuple       =							np.array([0, 3]   ,						dtype='long' )
            _lowerCAmelCase     :				Dict       =							tokenizer(['Test question'] ).input_ids
            _lowerCAmelCase     :				int       =							tokenizer(
                ['the fourth']   ,						add_special_tokens=snake_case__   ,						return_token_type_ids=snake_case__   ,						return_attention_mask=snake_case__   ,						).input_ids
            _lowerCAmelCase     :				Tuple       =							config.reader_seq_len

            _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							retriever(
                snake_case__   ,						snake_case__   ,						answer_ids=snake_case__   ,						max_length=snake_case__   ,						return_tensors='np' )

            self.assertEqual(len(snake_case__ )   ,						2 )
            self.assertEqual(len(snake_case__ )   ,						2 )
            self.assertEqual(len(snake_case__ )   ,						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 ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.get_config()
            _lowerCAmelCase     :				int       =							self.get_dummy_retriever()
            _lowerCAmelCase     :				List[str]       =							retriever.tokenizer

            _lowerCAmelCase     :				List[Any]       =							np.array([0, 3, 5]   ,						dtype='long' )
            _lowerCAmelCase     :				str       =							tokenizer(['Test question'] ).input_ids
            _lowerCAmelCase     :				Union[str, Any]       =							tokenizer(
                ['the fourth', 'longer longer']   ,						add_special_tokens=snake_case__   ,						return_token_type_ids=snake_case__   ,						return_attention_mask=snake_case__   ,						).input_ids
            _lowerCAmelCase     :				Optional[int]       =							config.reader_seq_len

            _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							retriever(
                snake_case__   ,						snake_case__   ,						answer_ids=snake_case__   ,						max_length=snake_case__   ,						return_tensors='np' )

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







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							self.get_dummy_retriever()
            retriever.save_pretrained(os.path.join(self.tmpdirname   ,						'realm_block_records' ) )

            # Test local path
            _lowerCAmelCase     :				Union[str, Any]       =							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:
                   _lowerCAmelCase     :				Any       =							os.path.join(
                       os.path.join(self.tmpdirname   ,						'realm_block_records' )   ,						_REALM_BLOCK_RECORDS_FILENAME )
                   _lowerCAmelCase     :				str       =							RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa' )

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

