'''simple docstring'''


import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def       lowerCAmelCase					(		UpperCamelCase__				:   Tuple    ,					UpperCamelCase__				:   str       ):


	"""simple docstring"""
	assert isinstance(UpperCamelCase__    ,					UpperCamelCase__       )
	assert dataset.num_rows == 4
	assert dataset.num_columns == 3
	assert dataset.column_names == ["col_1", "col_2", "col_3"]
	for feature, expected_dtype in expected_features.items():
		assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('''keep_in_memory'''    ,					[False, True]       )
def       lowerCAmelCase					(		UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Optional[int]    ,					UpperCamelCase__				:   Any       ):


	"""simple docstring"""
	__UpperCAmelCase							=			tmp_path / '''cache'''
	__UpperCAmelCase							=			{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		__UpperCAmelCase							=			ParquetDatasetReader(UpperCamelCase__    ,					cache_dir=UpperCamelCase__    ,					keep_in_memory=UpperCamelCase__       ).read()
	_check_parquet_dataset(UpperCamelCase__    ,					UpperCamelCase__       )





@pytest.mark.parametrize(
    '''features'''    ,					[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]    ,					)
def       lowerCAmelCase					(		UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Dict       ):


	"""simple docstring"""
	__UpperCAmelCase							=			tmp_path / '''cache'''
	__UpperCAmelCase							=			{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
	__UpperCAmelCase							=			features.copy() if features else default_expected_features
	__UpperCAmelCase							=			(
	    Features({feature: Value(UpperCamelCase__       ) for feature, dtype in features.items()}       ) if features is not None else None
	)
	__UpperCAmelCase							=			ParquetDatasetReader(UpperCamelCase__    ,					features=UpperCamelCase__    ,					cache_dir=UpperCamelCase__       ).read()
	_check_parquet_dataset(UpperCamelCase__    ,					UpperCamelCase__       )





@pytest.mark.parametrize('''split'''    ,					[None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def       lowerCAmelCase					(		UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   Union[str, Any]    ,					UpperCamelCase__				:   Tuple       ):


	"""simple docstring"""
	__UpperCAmelCase							=			tmp_path / '''cache'''
	__UpperCAmelCase							=			{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
	__UpperCAmelCase							=			ParquetDatasetReader(UpperCamelCase__    ,					cache_dir=UpperCamelCase__    ,					split=UpperCamelCase__       ).read()
	_check_parquet_dataset(UpperCamelCase__    ,					UpperCamelCase__       )
	assert dataset.split == split if split else "train"





@pytest.mark.parametrize('''path_type'''    ,					[str, list]       )
def       lowerCAmelCase					(		UpperCamelCase__				:   Tuple    ,					UpperCamelCase__				:   List[str]    ,					UpperCamelCase__				:   List[Any]       ):


	"""simple docstring"""
	if issubclass(UpperCamelCase__    ,					UpperCamelCase__       ):
		__UpperCAmelCase							=			parquet_path
	elif issubclass(UpperCamelCase__    ,					UpperCamelCase__       ):
		__UpperCAmelCase							=			[parquet_path]
	__UpperCAmelCase							=			tmp_path / '''cache'''
	__UpperCAmelCase							=			{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
	__UpperCAmelCase							=			ParquetDatasetReader(UpperCamelCase__    ,					cache_dir=UpperCamelCase__       ).read()
	_check_parquet_dataset(UpperCamelCase__    ,					UpperCamelCase__       )





def       lowerCAmelCase					(		UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   Tuple=("train",)       ):


	"""simple docstring"""
	assert isinstance(UpperCamelCase__    ,					UpperCamelCase__       )
	for split in splits:
		__UpperCAmelCase							=			dataset_dict[split]
		assert dataset.num_rows == 4
		assert dataset.num_columns == 3
		assert dataset.column_names == ["col_1", "col_2", "col_3"]
		for feature, expected_dtype in expected_features.items():
			assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('''keep_in_memory'''    ,					[False, True]       )
def       lowerCAmelCase					(		UpperCamelCase__				:   Tuple    ,					UpperCamelCase__				:   int    ,					UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			tmp_path / '''cache'''
	__UpperCAmelCase							=			{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		__UpperCAmelCase							=			ParquetDatasetReader(
		    {'''train''': parquet_path}    ,					cache_dir=UpperCamelCase__    ,					keep_in_memory=UpperCamelCase__       ).read()
	_check_parquet_datasetdict(UpperCamelCase__    ,					UpperCamelCase__       )





@pytest.mark.parametrize(
    '''features'''    ,					[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]    ,					)
def       lowerCAmelCase					(		UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   Union[str, Any]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			tmp_path / '''cache'''
	__UpperCAmelCase							=			{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
	__UpperCAmelCase							=			features.copy() if features else default_expected_features
	__UpperCAmelCase							=			(
	    Features({feature: Value(UpperCamelCase__       ) for feature, dtype in features.items()}       ) if features is not None else None
	)
	__UpperCAmelCase							=			ParquetDatasetReader({'''train''': parquet_path}    ,					features=UpperCamelCase__    ,					cache_dir=UpperCamelCase__       ).read()
	_check_parquet_datasetdict(UpperCamelCase__    ,					UpperCamelCase__       )





@pytest.mark.parametrize('''split'''    ,					[None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Optional[int]       ):


	"""simple docstring"""
	if split:
		__UpperCAmelCase							=			{split: parquet_path}
	else:
		__UpperCAmelCase							=			'''train'''
		__UpperCAmelCase							=			{'''train''': parquet_path, '''test''': parquet_path}
	__UpperCAmelCase							=			tmp_path / '''cache'''
	__UpperCAmelCase							=			{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
	__UpperCAmelCase							=			ParquetDatasetReader(UpperCamelCase__    ,					cache_dir=UpperCamelCase__       ).read()
	_check_parquet_datasetdict(UpperCamelCase__    ,					UpperCamelCase__    ,					splits=list(path.keys()       )       )
	assert all(dataset[split].split == split for split in path.keys()       )





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   Optional[Any]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			ParquetDatasetWriter(UpperCamelCase__    ,					tmp_path / '''foo.parquet'''       )
	assert writer.write() > 0
	__UpperCAmelCase							=			pq.ParquetFile(tmp_path / '''foo.parquet'''       )
	__UpperCAmelCase							=			pf.read()
	assert dataset.data.table == output_table





def       lowerCAmelCase					(		UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			str(shared_datadir / '''test_image_rgb.jpg'''       )
	__UpperCAmelCase							=			{'''image''': [image_path]}
	__UpperCAmelCase							=			Features({'''image''': Image()}       )
	__UpperCAmelCase							=			Dataset.from_dict(UpperCamelCase__    ,					features=UpperCamelCase__       )
	__UpperCAmelCase							=			ParquetDatasetWriter(UpperCamelCase__    ,					tmp_path / '''foo.parquet'''       )
	assert writer.write() > 0

	__UpperCAmelCase							=			Dataset.from_parquet(str(tmp_path / '''foo.parquet'''       )       )
	assert dataset.features == reloaded_dataset.features

	__UpperCAmelCase							=			ParquetDatasetReader(str(tmp_path / '''foo.parquet'''       )    ,					streaming=UpperCamelCase__       ).read()
	assert dataset.features == reloaded_iterable_dataset.features





@pytest.mark.parametrize(
    '''feature, expected'''    ,					[
        (Features({'''foo''': Value('''int32'''       )}       ), None),
        (Features({'''image''': Image(), '''foo''': Value('''int32'''       )}       ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'''nested''': Sequence(Audio()       )}       ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]    ,					)
def       lowerCAmelCase					(		UpperCamelCase__				:   int    ,					UpperCamelCase__				:   Optional[Any]       ):


	"""simple docstring"""
	assert get_writer_batch_size(UpperCamelCase__       ) == expected

'''simple docstring'''


from __future__ import annotations

import unittest

from transformers import DistilBertConfig, 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 tensorflow as tf

					from transformers.models.distilbert.modeling_tf_distilbert import (
					    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFDistilBertForMaskedLM,
					    TFDistilBertForMultipleChoice,
					    TFDistilBertForQuestionAnswering,
					    TFDistilBertForSequenceClassification,
					    TFDistilBertForTokenClassification,
					    TFDistilBertModel,
					)






class 						A   :





							def __init__(      self				:							List[Any]     ,		__a				:							Any     ,		)							->	Dict:
								__UpperCAmelCase							=			parent
								__UpperCAmelCase							=			1_3
								__UpperCAmelCase							=			7
								__UpperCAmelCase							=			True
								__UpperCAmelCase							=			True
								__UpperCAmelCase							=			False
								__UpperCAmelCase							=			True
								__UpperCAmelCase							=			9_9
								__UpperCAmelCase							=			3_2
								__UpperCAmelCase							=			2
								__UpperCAmelCase							=			4
								__UpperCAmelCase							=			3_7
								__UpperCAmelCase							=			'''gelu'''
								__UpperCAmelCase							=			0.1
								__UpperCAmelCase							=			0.1
								__UpperCAmelCase							=			5_1_2
								__UpperCAmelCase							=			1_6
								__UpperCAmelCase							=			2
								__UpperCAmelCase							=			0.0_2
								__UpperCAmelCase							=			3
								__UpperCAmelCase							=			4
								__UpperCAmelCase							=			None





							def       snake_case__						(      self				:							Optional[int]	)							->	Dict:
								__UpperCAmelCase							=			ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size	)

								__UpperCAmelCase							=			None
								if self.use_input_mask:
									__UpperCAmelCase							=			random_attention_mask([self.batch_size, self.seq_length]	)

								__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							=			DistilBertConfig(
								    vocab_size=self.vocab_size     ,		dim=self.hidden_size     ,		n_layers=self.num_hidden_layers     ,		n_heads=self.num_attention_heads     ,		hidden_dim=self.intermediate_size     ,		hidden_act=self.hidden_act     ,		dropout=self.hidden_dropout_prob     ,		attention_dropout=self.attention_probs_dropout_prob     ,		max_position_embeddings=self.max_position_embeddings     ,		initializer_range=self.initializer_range     ,		)

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





							def       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							List[str]     ,		__a				:							int     ,		__a				:							Union[str, Any]     ,		__a				:							Union[str, Any]     ,		__a				:							List[Any]     ,		__a				:							int	)							->	Any:
								__UpperCAmelCase							=			TFDistilBertModel(config=__a	)
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}

								__UpperCAmelCase							=			model(__a	)

								__UpperCAmelCase							=			[input_ids, input_mask]

								__UpperCAmelCase							=			model(__a	)

								self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size)	)





							def       snake_case__						(      self				:							Tuple     ,		__a				:							List[Any]     ,		__a				:							int     ,		__a				:							Tuple     ,		__a				:							List[Any]     ,		__a				:							Union[str, Any]     ,		__a				:							List[Any]	)							->	int:
								__UpperCAmelCase							=			TFDistilBertForMaskedLM(config=__a	)
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.vocab_size)	)





							def       snake_case__						(      self				:							Optional[int]     ,		__a				:							Any     ,		__a				:							Union[str, Any]     ,		__a				:							Optional[int]     ,		__a				:							int     ,		__a				:							Optional[Any]     ,		__a				:							Optional[int]	)							->	Dict:
								__UpperCAmelCase							=			TFDistilBertForQuestionAnswering(config=__a	)
								__UpperCAmelCase							=			{
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								}
								__UpperCAmelCase							=			model(__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)	)





							def       snake_case__						(      self				:							Any     ,		__a				:							Optional[Any]     ,		__a				:							List[str]     ,		__a				:							Dict     ,		__a				:							Dict     ,		__a				:							int     ,		__a				:							List[Any]	)							->	Dict:
								__UpperCAmelCase							=			self.num_labels
								__UpperCAmelCase							=			TFDistilBertForSequenceClassification(__a	)
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels)	)





							def       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							int     ,		__a				:							str     ,		__a				:							Union[str, Any]     ,		__a				:							Optional[int]     ,		__a				:							List[str]     ,		__a				:							Dict	)							->	str:
								__UpperCAmelCase							=			self.num_choices
								__UpperCAmelCase							=			TFDistilBertForMultipleChoice(__a	)
								__UpperCAmelCase							=			tf.tile(tf.expand_dims(__a     ,		1	)     ,		(1, self.num_choices, 1)	)
								__UpperCAmelCase							=			tf.tile(tf.expand_dims(__a     ,		1	)     ,		(1, self.num_choices, 1)	)
								__UpperCAmelCase							=			{
								    '''input_ids''': multiple_choice_inputs_ids,
								    '''attention_mask''': multiple_choice_input_mask,
								}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_choices)	)





							def       snake_case__						(      self				:							int     ,		__a				:							Optional[Any]     ,		__a				:							int     ,		__a				:							Tuple     ,		__a				:							int     ,		__a				:							Optional[int]     ,		__a				:							Optional[int]	)							->	int:
								__UpperCAmelCase							=			self.num_labels
								__UpperCAmelCase							=			TFDistilBertForTokenClassification(__a	)
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.num_labels)	)






							def       snake_case__						(      self				:							str	)							->	Any:
								__UpperCAmelCase							=			self.prepare_config_and_inputs()
								((__UpperCAmelCase)				,			(__UpperCAmelCase)				,			(__UpperCAmelCase)				,			(__UpperCAmelCase)				,			(__UpperCAmelCase)				,			(__UpperCAmelCase))							=			config_and_inputs
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict






@require_tf
class 						A   (   UpperCAmelCase       ,	UpperCAmelCase       ,	unittest.TestCase   ):
							a_        =      (
							    (
							        TFDistilBertModel,
							        TFDistilBertForMaskedLM,
							        TFDistilBertForQuestionAnswering,
							        TFDistilBertForSequenceClassification,
							        TFDistilBertForTokenClassification,
							        TFDistilBertForMultipleChoice,
							    )
							    if is_tf_available()
							    else None
							)
							a_        =      (
							    {
							        '''feature-extraction''': TFDistilBertModel,
							        '''fill-mask''': TFDistilBertForMaskedLM,
							        '''question-answering''': TFDistilBertForQuestionAnswering,
							        '''text-classification''': TFDistilBertForSequenceClassification,
							        '''token-classification''': TFDistilBertForTokenClassification,
							        '''zero-shot''': TFDistilBertForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							a_        =      False
							a_        =      False





							def       snake_case__						(      self				:							Any	)							->	Any:
								__UpperCAmelCase							=			TFDistilBertModelTester(self	)
								__UpperCAmelCase							=			ConfigTester(self     ,		config_class=__a     ,		dim=3_7	)





							def       snake_case__						(      self				:							List[Any]	)							->	Optional[int]:
								self.config_tester.run_common_tests()





							def       snake_case__						(      self				:							Any	)							->	str:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_model(*__a	)





							def       snake_case__						(      self				:							Tuple	)							->	Dict:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_masked_lm(*__a	)





							def       snake_case__						(      self				:							Union[str, Any]	)							->	Any:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_question_answering(*__a	)





							def       snake_case__						(      self				:							Optional[Any]	)							->	Dict:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_sequence_classification(*__a	)





							def       snake_case__						(      self				:							Any	)							->	int:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_multiple_choice(*__a	)





							def       snake_case__						(      self				:							List[str]	)							->	List[Any]:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_token_classification(*__a	)






							@slow
							def       snake_case__						(      self				:							Dict	)							->	Tuple:
								for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]	):
									__UpperCAmelCase							=			TFDistilBertModel.from_pretrained(__a	)
									self.assertIsNotNone(__a	)






@require_tf
class 						A   (   unittest.TestCase   ):





							@slow
							def       snake_case__						(      self				:							int	)							->	Dict:
								__UpperCAmelCase							=			TFDistilBertModel.from_pretrained('''distilbert-base-uncased'''	)
								__UpperCAmelCase							=			tf.constant([[0, 1, 2, 3, 4, 5]]	)
								__UpperCAmelCase							=			model(__a	)[0]

								__UpperCAmelCase							=			[1, 6, 7_6_8]
								self.assertEqual(output.shape     ,		__a	)

								__UpperCAmelCase							=			tf.constant(
								    [
								        [
								            [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
								            [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
								            [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
								        ]
								    ]	)
								tf.debugging.assert_near(output[:, :3, :3]     ,		__a     ,		atol=1e-4	)
