from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_				:		Union[str, Any]       						=       logging.get_logger(__name__)

lowerCamelCase_				:		Tuple       						=       {
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}


class 						a__							(   __snake_case		):
			A__				:   List[str]								=			'tvlt'



			def __init__(						self      ,						UpperCAmelCase=2_2_4      ,						UpperCAmelCase=2_0_4_8      ,						UpperCAmelCase=1_2_8      ,						UpperCAmelCase=[1_6, 1_6]      ,						UpperCAmelCase=[1_6, 1_6]      ,						UpperCAmelCase=3      ,						UpperCAmelCase=1      ,						UpperCAmelCase=8      ,						UpperCAmelCase=7_6_8      ,						UpperCAmelCase=1_2      ,						UpperCAmelCase=1_2      ,						UpperCAmelCase=3_0_7_2      ,						UpperCAmelCase="gelu"      ,						UpperCAmelCase=0.0      ,						UpperCAmelCase=0.0      ,						UpperCAmelCase=0.02      ,						UpperCAmelCase=1e-6      ,						UpperCAmelCase=True      ,						UpperCAmelCase=False      ,						UpperCAmelCase=1_6      ,						UpperCAmelCase=5_1_2      ,						UpperCAmelCase=8      ,						UpperCAmelCase=2_0_4_8      ,						UpperCAmelCase=0.75      ,						UpperCAmelCase=0.15      ,						UpperCAmelCase="frame-level"      ,						UpperCAmelCase=True      ,						UpperCAmelCase=True      ,						UpperCAmelCase="classification"      ,						**UpperCAmelCase      ,						) ->    Optional[int]:
								super().__init__(**UpperCAmelCase    )

								if audio_mask_type not in ("frame-level", "patch_level"):
													raise ValueError(
													    'audio_mask_type must be one of two acceptable strategies - {\'frame_level\', \'patch-level\') '
													    f'''got {audio_mask_type}'''    )

								__a							=   image_size
								__a							=   spectrogram_length
								__a							=   frequency_length
								__a							=   image_patch_size
								__a							=   audio_patch_size
								__a							=   num_image_channels
								__a							=   num_audio_channels
								__a							=   num_frames

								__a							=   hidden_size
								__a							=   num_hidden_layers
								__a							=   num_attention_heads
								__a							=   intermediate_size
								__a							=   hidden_act
								__a							=   hidden_dropout_prob
								__a							=   attention_probs_dropout_prob
								__a							=   initializer_range
								__a							=   layer_norm_eps
								__a							=   qkv_bias
								__a							=   use_mean_pooling

								__a							=   decoder_num_attention_heads
								__a							=   decoder_hidden_size
								__a							=   decoder_num_hidden_layers
								__a							=   decoder_intermediate_size
								__a							=   pixel_mask_ratio
								__a							=   audio_mask_ratio
								__a							=   audio_mask_type

								__a							=   task_matching
								__a							=   task_mae
								__a							=   loss_type










import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def  lowerCAmelCase  (	UpperCAmelCase,	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,	UpperCAmelCase,	UpperCAmelCase       )  ->Any:






					"""simple docstring"""






					__magic_name__					:							Dict		   =							tmp_path / '''cache'''
					__magic_name__					:							Tuple		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__magic_name__					:							str		   =							CsvDatasetReader(UpperCAmelCase,	cache_dir=UpperCAmelCase,	keep_in_memory=UpperCAmelCase       ).read()
					_check_csv_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,	UpperCAmelCase,	UpperCAmelCase       )  ->Tuple:






					"""simple docstring"""






					__magic_name__					:							Tuple		   =							tmp_path / '''cache'''
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					__magic_name__					:							List[Any]		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							Tuple		   =							features.copy() if features else default_expected_features
					__magic_name__					:							str		   =							(
					    Features({feature: Value(UpperCAmelCase       ) for feature, dtype in features.items()}       ) if features is not None else None
					)
					__magic_name__					:							Dict		   =							CsvDatasetReader(UpperCAmelCase,	features=UpperCAmelCase,	cache_dir=UpperCAmelCase       ).read()
					_check_csv_dataset(UpperCAmelCase,	UpperCAmelCase       )







@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[Any]:






					"""simple docstring"""






					__magic_name__					:							str		   =							tmp_path / '''cache'''
					__magic_name__					:							List[str]		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							int		   =							CsvDatasetReader(UpperCAmelCase,	cache_dir=UpperCAmelCase,	split=UpperCAmelCase       ).read()
					_check_csv_dataset(UpperCAmelCase,	UpperCAmelCase       )
					assert dataset.split == split if split else "train"







@pytest.mark.parametrize('''path_type''',	[str, list]       )
def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->str:






					"""simple docstring"""






					if issubclass(UpperCAmelCase,	UpperCAmelCase       ):
										__magic_name__					:							str		   =							csv_path
					elif issubclass(UpperCAmelCase,	UpperCAmelCase       ):
										__magic_name__					:							str		   =							[csv_path]
					__magic_name__					:							Optional[int]		   =							tmp_path / '''cache'''
					__magic_name__					:							Any		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							Tuple		   =							CsvDatasetReader(UpperCAmelCase,	cache_dir=UpperCAmelCase       ).read()
					_check_csv_dataset(UpperCAmelCase,	UpperCAmelCase       )







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase=("train",)       )  ->Tuple:






					"""simple docstring"""






					assert isinstance(UpperCAmelCase,	UpperCAmelCase       )
					for split in splits:
										__magic_name__					:							int		   =							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,	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Tuple		   =							tmp_path / '''cache'''
					__magic_name__					:							int		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__magic_name__					:							Optional[Any]		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase,	keep_in_memory=UpperCAmelCase       ).read()
					_check_csv_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,	UpperCAmelCase,	UpperCAmelCase       )  ->List[str]:






					"""simple docstring"""






					__magic_name__					:							Any		   =							tmp_path / '''cache'''
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					__magic_name__					:							Dict		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							Dict		   =							features.copy() if features else default_expected_features
					__magic_name__					:							Any		   =							(
					    Features({feature: Value(UpperCAmelCase       ) for feature, dtype in features.items()}       ) if features is not None else None
					)
					__magic_name__					:							List[str]		   =							CsvDatasetReader({'''train''': csv_path},	features=UpperCAmelCase,	cache_dir=UpperCAmelCase       ).read()
					_check_csv_datasetdict(UpperCAmelCase,	UpperCAmelCase       )







@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[str]:






					"""simple docstring"""






					if split:
										__magic_name__					:							Dict		   =							{split: csv_path}
					else:
										__magic_name__					:							Any		   =							{'''train''': csv_path, '''test''': csv_path}
					__magic_name__					:							str		   =							tmp_path / '''cache'''
					__magic_name__					:							Union[str, Any]		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							int		   =							CsvDatasetReader(UpperCAmelCase,	cache_dir=UpperCAmelCase       ).read()
					_check_csv_datasetdict(UpperCAmelCase,	UpperCAmelCase,	splits=list(path.keys()       )       )
					assert all(dataset[split].split == split for split in path.keys()       )







def  lowerCAmelCase  (	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					with open(UpperCAmelCase,	encoding='''utf-8'''       ) as csvfile:
										yield from csv.reader(UpperCAmelCase       )







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Dict		   =							tmp_path / '''cache'''
					__magic_name__					:							List[Any]		   =							os.path.join(UpperCAmelCase,	'''tmp.csv'''       )
					__magic_name__					:							int		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase       ).read()
					CsvDatasetWriter(dataset['''train'''],	UpperCAmelCase,	num_proc=1       ).write()

					__magic_name__					:							Optional[int]		   =							iter_csv_file(UpperCAmelCase       )
					__magic_name__					:							Any		   =							iter_csv_file(UpperCAmelCase       )

					for rowa, rowa in zip(UpperCAmelCase,	UpperCAmelCase       ):
										assert rowa == rowa







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






					"""simple docstring"""






					__magic_name__					:							Optional[int]		   =							tmp_path / '''cache'''
					__magic_name__					:							Optional[Any]		   =							os.path.join(UpperCAmelCase,	'''tmp.csv'''       )
					__magic_name__					:							List[Any]		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase       ).read()
					CsvDatasetWriter(dataset['''train'''],	UpperCAmelCase,	num_proc=2       ).write()

					__magic_name__					:							Tuple		   =							iter_csv_file(UpperCAmelCase       )
					__magic_name__					:							List[str]		   =							iter_csv_file(UpperCAmelCase       )

					for rowa, rowa in zip(UpperCAmelCase,	UpperCAmelCase       ):
										assert rowa == rowa







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Optional[int]		   =							tmp_path / '''cache'''
					__magic_name__					:							Any		   =							os.path.join(UpperCAmelCase,	'''tmp.csv'''       )
					__magic_name__					:							Any		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase       ).read()
					with pytest.raises(UpperCAmelCase       ):
										CsvDatasetWriter(dataset['''train'''],	UpperCAmelCase,	num_proc=0       )



