
"""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(       lowerCamelCase      ):



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    pa.array(TypedSequence([1, 2, 3]))
							self.assertEqual(arr.type							,						pa.intaa())



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							with self.assertRaises(__a):
												_UpperCamelCase									=    pa.array(TypedSequence([1, 2, 3])							,						type=pa.intaa())



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							with self.assertRaises(__a):
												_UpperCamelCase									=    pa.array(TypedSequence([1, 2, 3]							,						try_type=Value('''bool''')							,						type=Value('''int64''')))



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    pa.array(TypedSequence([1, 2, 3]							,						type=Value('''int32''')))
							self.assertEqual(arr.type							,						pa.intaa())



		def 		UpperCAmelCase				(   self)			->			int:






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



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    pa.array(TypedSequence([1, 2, 3]							,						try_type=Value('''int32''')))
							self.assertEqual(arr.type							,						pa.intaa())



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    pa.array(TypedSequence(['''foo''', '''bar''']							,						try_type=Value('''int64''')))
							self.assertEqual(arr.type							,						pa.string())



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    pa.array(TypedSequence([[[1, 2, 3]]]							,						type=ArrayaD((1, 3)							,						'''int64''')))
							self.assertEqual(arr.type							,						ArrayaDExtensionType((1, 3)							,						'''int64'''))



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






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



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    pa.array(TypedSequence([[[1, 2, 3]]]							,						try_type=ArrayaD((1, 3)							,						'''int64''')))
							self.assertEqual(arr.type							,						ArrayaDExtensionType((1, 3)							,						'''int64'''))



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    pa.array(TypedSequence(['''foo''', '''bar''']							,						try_type=ArrayaD((1, 3)							,						'''int64''')))
							self.assertEqual(arr.type							,						pa.string())




		@require_pil
		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							import PIL.Image

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





def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Dict:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferReader(__snake_case      ) if isinstance(__snake_case,	pa.Buffer      ) else pa.memory_map(__snake_case      )
					_UpperCamelCase									=    pa.ipc.open_stream(__snake_case      )
					_UpperCamelCase									=    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, 10]      )
@pytest.mark.parametrize(
    '''fields''',	[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]      )
def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Optional[int]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferOutputStream()
					_UpperCamelCase									=    Features({'''labels''': ClassLabel(names=['''neg''', '''pos''']      )}      )
					with ArrowWriter(stream=__snake_case,	features=__snake_case      ) as writer:
										writer.write({'''labels''': 0}      )
										writer.write({'''labels''': 1}      )
										_UpperCamelCase					,    _UpperCamelCase									=    writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					assert writer._schema == features.arrow_schema
					assert writer._schema.metadata == features.arrow_schema.metadata
					_UpperCamelCase									=    pa.BufferReader(output.getvalue()      )
					_UpperCamelCase									=    pa.ipc.open_stream(__snake_case      )
					_UpperCamelCase									=    f.read_all()
					_UpperCamelCase									=    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(__snake_case      )



@pytest.mark.parametrize('''writer_batch_size''',	[None, 1, 10]      )
def  lowerCamelCase__						(     __snake_case      )				->						Dict:


					"""simple docstring"""





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



@pytest.mark.parametrize('''writer_batch_size''',	[None, 2, 10]      )
def  lowerCamelCase__						(     __snake_case      )				->						Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferOutputStream()
					with ArrowWriter(
					    stream=__snake_case,	writer_batch_size=__snake_case,	hash_salt='''split_name''',	check_duplicates=__snake_case,	) as writer:
										with pytest.raises(__snake_case      ):
															writer.write({'''col_1''': '''foo''', '''col_2''': 1},	key=10      )
															writer.write({'''col_1''': '''bar''', '''col_2''': 2},	key=10      )
															_UpperCamelCase					,    _UpperCamelCase									=    writer.finalize()



@pytest.mark.parametrize('''writer_batch_size''',	[None, 2, 10]      )
def  lowerCamelCase__						(     __snake_case      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferOutputStream()
					with ArrowWriter(
					    stream=__snake_case,	writer_batch_size=__snake_case,	hash_salt='''split_name''',	check_duplicates=__snake_case,	) as writer:
										writer.write({'''col_1''': '''foo''', '''col_2''': 1},	key=1      )
										writer.write({'''col_1''': '''bar''', '''col_2''': 2},	key=2      )
										_UpperCamelCase					,    _UpperCamelCase									=    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, 10]      )
@pytest.mark.parametrize(
    '''fields''',	[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]      )
def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferOutputStream()
					_UpperCamelCase									=    pa.schema(__snake_case      ) if fields else None
					with ArrowWriter(stream=__snake_case,	schema=__snake_case,	writer_batch_size=__snake_case      ) as writer:
										writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}      )
										writer.write_batch({'''col_1''': [], '''col_2''': []}      )
										_UpperCamelCase					,    _UpperCamelCase									=    writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										_UpperCamelCase									=    {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
					assert writer._schema == pa.schema(__snake_case,	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, 10]      )
@pytest.mark.parametrize(
    '''fields''',	[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]      )
def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferOutputStream()
					_UpperCamelCase									=    pa.schema(__snake_case      ) if fields else None
					with ArrowWriter(stream=__snake_case,	schema=__snake_case,	writer_batch_size=__snake_case      ) as writer:
										writer.write_table(pa.Table.from_pydict({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}      )      )
										_UpperCamelCase					,    _UpperCamelCase									=    writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										_UpperCamelCase									=    {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
					assert writer._schema == pa.schema(__snake_case,	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, 10]      )
@pytest.mark.parametrize(
    '''fields''',	[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]      )
def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Any:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferOutputStream()
					_UpperCamelCase									=    pa.schema(__snake_case      ) if fields else None
					with ArrowWriter(stream=__snake_case,	schema=__snake_case,	writer_batch_size=__snake_case      ) 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]}      )      )
										_UpperCamelCase					,    _UpperCamelCase									=    writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										_UpperCamelCase									=    {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
					assert writer._schema == pa.schema(__snake_case,	metadata=writer._schema.metadata      )
					_check_output(output.getvalue(),	expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )



def  lowerCamelCase__						(     )				->						str:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case      )				->						Optional[int]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						List[Any]:


					"""simple docstring"""





					if isinstance(lst[0],	__snake_case      ):
										change_first_primitive_element_in_list(lst[0],	__snake_case      )
					else:
										_UpperCamelCase									=    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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    pa.array(TypedSequence(__snake_case,	optimized_int_type=__snake_case      )      )
					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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase									=    pa.array(OptimizedTypedSequence(__snake_case,	col=__snake_case      )      )
					assert get_base_dtype(arr.type      ) == expected_dtype

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



@pytest.mark.parametrize('''raise_exception''',	[False, True]      )
def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Any:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case      )				->						Union[str, Any]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     )				->						Tuple:


					"""simple docstring"""





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



@require_pil
@pytest.mark.parametrize('''embed_local_files''',	[False, True]      )
def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						List[str]:


					"""simple docstring"""





					import PIL.Image

					_UpperCamelCase									=    str(tmp_path / '''test_image_rgb.jpg'''      )
					PIL.Image.fromarray(np.zeros((5, 5),	dtype=np.uinta      )      ).save(__snake_case,	format='''png'''      )
					_UpperCamelCase									=    pa.BufferOutputStream()
					with ParquetWriter(
					    stream=__snake_case,	features=Features({'''image''': Image()}      ),	embed_local_files=__snake_case      ) as writer:
										writer.write({'''image''': image_path}      )
										writer.finalize()
					_UpperCamelCase									=    pa.BufferReader(output.getvalue()      )
					_UpperCamelCase									=    pq.read_table(__snake_case      )
					_UpperCamelCase									=    pa_table.to_pydict()
					if embed_local_files:
										assert isinstance(out['''image'''][0]['''path'''],	__snake_case      )
										with open(__snake_case,	'''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  lowerCamelCase__						(     )				->						int:


					"""simple docstring"""





					_UpperCamelCase									=    pa.schema([pa.field('''col_1''',	pa.string(),	nullable=__snake_case      )]      )
					_UpperCamelCase									=    pa.BufferOutputStream()
					with ArrowWriter(stream=__snake_case      ) as writer:
										writer._build_writer(inferred_schema=__snake_case      )
										assert writer._schema == pa.schema([pa.field('''col_1''',	pa.string()      )]      )








"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import numpy
				import tensorflow as tf

				from transformers import (
				    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BertConfig,
				    DPRConfig,
				    TFDPRContextEncoder,
				    TFDPRQuestionEncoder,
				    TFDPRReader,
				)






class        _UpperCAmelCase:



		def __init__(   self							,						__a							,						__a=13							,						__a=7							,						__a=True							,						__a=True							,						__a=True							,						__a=True							,						__a=99							,						__a=32							,						__a=2							,						__a=4							,						__a=37							,						__a="gelu"							,						__a=0.1							,						__a=0.1							,						__a=5_12							,						__a=16							,						__a=2							,						__a=0.02							,						__a=3							,						__a=4							,						__a=None							,						__a=0							,						)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    parent
							_UpperCamelCase									=    batch_size
							_UpperCamelCase									=    seq_length
							_UpperCamelCase									=    is_training
							_UpperCamelCase									=    use_input_mask
							_UpperCamelCase									=    use_token_type_ids
							_UpperCamelCase									=    use_labels
							_UpperCamelCase									=    vocab_size
							_UpperCamelCase									=    hidden_size
							_UpperCamelCase									=    num_hidden_layers
							_UpperCamelCase									=    num_attention_heads
							_UpperCamelCase									=    intermediate_size
							_UpperCamelCase									=    hidden_act
							_UpperCamelCase									=    hidden_dropout_prob
							_UpperCamelCase									=    attention_probs_dropout_prob
							_UpperCamelCase									=    max_position_embeddings
							_UpperCamelCase									=    type_vocab_size
							_UpperCamelCase									=    type_sequence_label_size
							_UpperCamelCase									=    initializer_range
							_UpperCamelCase									=    num_labels
							_UpperCamelCase									=    num_choices
							_UpperCamelCase									=    scope
							_UpperCamelCase									=    projection_dim



		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size)

							_UpperCamelCase									=    None
							if self.use_input_mask:
												# follow test_modeling_tf_ctrl.py
												_UpperCamelCase									=    random_attention_mask([self.batch_size, self.seq_length])

							_UpperCamelCase									=    None
							if self.use_token_type_ids:
												_UpperCamelCase									=    ids_tensor([self.batch_size, self.seq_length]							,						self.type_vocab_size)

							_UpperCamelCase									=    None
							_UpperCamelCase									=    None
							_UpperCamelCase									=    None
							if self.use_labels:
												_UpperCamelCase									=    ids_tensor([self.batch_size]							,						self.type_sequence_label_size)
												_UpperCamelCase									=    ids_tensor([self.batch_size, self.seq_length]							,						self.num_labels)
												_UpperCamelCase									=    ids_tensor([self.batch_size]							,						self.num_choices)

							_UpperCamelCase									=    BertConfig(
							    vocab_size=self.vocab_size							,						hidden_size=self.hidden_size							,						num_hidden_layers=self.num_hidden_layers							,						num_attention_heads=self.num_attention_heads							,						intermediate_size=self.intermediate_size							,						hidden_act=self.hidden_act							,						hidden_dropout_prob=self.hidden_dropout_prob							,						attention_probs_dropout_prob=self.attention_probs_dropout_prob							,						max_position_embeddings=self.max_position_embeddings							,						type_vocab_size=self.type_vocab_size							,						is_decoder=__a							,						initializer_range=self.initializer_range							,						)
							_UpperCamelCase									=    DPRConfig(projection_dim=self.projection_dim							,						**config.to_dict())

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    TFDPRContextEncoder(config=__a)
							_UpperCamelCase									=    model(__a							,						attention_mask=__a							,						token_type_ids=__a)
							_UpperCamelCase									=    model(__a							,						token_type_ids=__a)
							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.pooler_output.shape							,						(self.batch_size, self.projection_dim or self.hidden_size))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    TFDPRQuestionEncoder(config=__a)
							_UpperCamelCase									=    model(__a							,						attention_mask=__a							,						token_type_ids=__a)
							_UpperCamelCase									=    model(__a							,						token_type_ids=__a)
							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.pooler_output.shape							,						(self.batch_size, self.projection_dim or self.hidden_size))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    TFDPRReader(config=__a)
							_UpperCamelCase									=    model(__a							,						attention_mask=__a)

							self.parent.assertEqual(result.start_logits.shape							,						(self.batch_size, self.seq_length))
							self.parent.assertEqual(result.end_logits.shape							,						(self.batch_size, self.seq_length))
							self.parent.assertEqual(result.relevance_logits.shape							,						(self.batch_size,))




		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.prepare_config_and_inputs()
							(
							    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    
							)									=    config_and_inputs
							_UpperCamelCase									=    {'''input_ids''': input_ids}
							return config, inputs_dict






@require_tf
class        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = (
		    (
		        TFDPRContextEncoder,
		        TFDPRQuestionEncoder,
		        TFDPRReader,
		    )
		    if is_tf_available()
		    else ()
		)
		lowercase__         = {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}

		lowercase__         = False
		lowercase__         = False
		lowercase__         = False
		lowercase__         = False
		lowercase__         = False



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    TFDPRModelTester(self)
							_UpperCamelCase									=    ConfigTester(self							,						config_class=__a							,						hidden_size=37)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							self.config_tester.run_common_tests()



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_context_encoder(*__a)



		def 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_question_encoder(*__a)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_reader(*__a)




		@slow
		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase									=    TFDPRContextEncoder.from_pretrained(__a)
												self.assertIsNotNone(__a)

							for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase									=    TFDPRContextEncoder.from_pretrained(__a)
												self.assertIsNotNone(__a)

							for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase									=    TFDPRQuestionEncoder.from_pretrained(__a)
												self.assertIsNotNone(__a)

							for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase									=    TFDPRReader.from_pretrained(__a)
												self.assertIsNotNone(__a)






@require_tf
class        _UpperCAmelCase(       unittest.TestCase      ):



		@slow
		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base''')

							_UpperCamelCase									=    tf.constant(
							    [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]])  # [CLS] hello, is my dog cute? [SEP]
							_UpperCamelCase									=    model(__a)[0]  # embedding shape = (1, 768)
							# compare the actual values for a slice.
							_UpperCamelCase									=    tf.constant(
							    [
							        [
							            0.0323_6253,
							            0.1275_3335,
							            0.1681_8509,
							            0.0027_9786,
							            0.389_6933,
							            0.2426_4945,
							            0.217_8971,
							            -0.0233_5227,
							            -0.0848_1959,
							            -0.1432_4117,
							        ]
							    ])
							self.assertTrue(numpy.allclose(output[:, :10].numpy()							,						expected_slice.numpy()							,						atol=1e-4))






