import warnings

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


lowercase_  : Any								=					logging.get_logger(__name__)

lowercase_  : Optional[int]								=					{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   "mvp"
    __a						=   ["past_key_values"]
    __a						=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
    def __init__(	self			,			lowerCAmelCase=50267			,			lowerCAmelCase=1024			,			lowerCAmelCase=12			,			lowerCAmelCase=4096			,			lowerCAmelCase=16			,			lowerCAmelCase=12			,			lowerCAmelCase=4096			,			lowerCAmelCase=16			,			lowerCAmelCase=0.0			,			lowerCAmelCase=0.0			,			lowerCAmelCase="gelu"			,			lowerCAmelCase=1024			,			lowerCAmelCase=0.1			,			lowerCAmelCase=0.0			,			lowerCAmelCase=0.0			,			lowerCAmelCase=0.02			,			lowerCAmelCase=0.0			,			lowerCAmelCase=False			,			lowerCAmelCase=True			,			lowerCAmelCase=1			,			lowerCAmelCase=0			,			lowerCAmelCase=2			,			lowerCAmelCase=True			,			lowerCAmelCase=2			,			lowerCAmelCase=2			,			lowerCAmelCase=False			,			lowerCAmelCase=100			,			lowerCAmelCase=800			,			**lowerCAmelCase			,			)							->       Dict:
     SCREAMING_SNAKE_CASE__: List[Any]=   vocab_size
     SCREAMING_SNAKE_CASE__: Optional[int]=   max_position_embeddings
     SCREAMING_SNAKE_CASE__: int=   d_model
     SCREAMING_SNAKE_CASE__: Optional[Any]=   encoder_ffn_dim
     SCREAMING_SNAKE_CASE__: Dict=   encoder_layers
     SCREAMING_SNAKE_CASE__: Optional[Any]=   encoder_attention_heads
     SCREAMING_SNAKE_CASE__: List[str]=   decoder_ffn_dim
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   decoder_layers
     SCREAMING_SNAKE_CASE__: Dict=   decoder_attention_heads
     SCREAMING_SNAKE_CASE__: Any=   dropout
     SCREAMING_SNAKE_CASE__: str=   attention_dropout
     SCREAMING_SNAKE_CASE__: List[Any]=   activation_dropout
     SCREAMING_SNAKE_CASE__: Optional[int]=   activation_function
     SCREAMING_SNAKE_CASE__: Optional[int]=   init_std
     SCREAMING_SNAKE_CASE__: List[str]=   encoder_layerdrop
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   decoder_layerdrop
     SCREAMING_SNAKE_CASE__: List[Any]=   classifier_dropout
     SCREAMING_SNAKE_CASE__: List[Any]=   use_cache
     SCREAMING_SNAKE_CASE__: Dict=   encoder_layers
     SCREAMING_SNAKE_CASE__: List[Any]=   scale_embedding  # scale factor will be sqrt(d_model) if True
     SCREAMING_SNAKE_CASE__: int=   use_prompt
     SCREAMING_SNAKE_CASE__: List[Any]=   prompt_length
     SCREAMING_SNAKE_CASE__: str=   prompt_mid_dim

     super().__init__(
         pad_token_id=lowerCAmelCase			,			bos_token_id=lowerCAmelCase			,			eos_token_id=lowerCAmelCase			,			is_encoder_decoder=lowerCAmelCase			,			decoder_start_token_id=lowerCAmelCase			,			forced_eos_token_id=lowerCAmelCase			,			**lowerCAmelCase			,			)

     if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''			,			lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.bos_token_id
      warnings.warn(
          f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
          '''The config can simply be saved and uploaded again to be fixed.''' )









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   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				assert isinstance(snake_case,	snake_case			)
				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   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   tmp_path / '''cache'''
				__magic_name__		:int   =   {'''col_1''': '''string''', '''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]   =   ParquetDatasetReader(snake_case,	cache_dir=snake_case,	keep_in_memory=snake_case			).read()
				_check_parquet_dataset(snake_case,	snake_case			)

@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   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   tmp_path / '''cache'''
				__magic_name__		:int   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:Tuple   =   features.copy() if features else default_expected_features
				__magic_name__		:Union[str, Any]   =   (
				    Features({feature: Value(snake_case			) for feature, dtype in features.items()}			) if features is not None else None
				)
				__magic_name__		:int   =   ParquetDatasetReader(snake_case,	features=snake_case,	cache_dir=snake_case			).read()
				_check_parquet_dataset(snake_case,	snake_case			)

@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''			), '''train''', '''test''']			)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






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

@pytest.mark.parametrize('''path_type''',	[str, list]			)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				if issubclass(snake_case,	snake_case			):
								__magic_name__		:Union[str, Any]   =   parquet_path
				elif issubclass(snake_case,	snake_case			):
								__magic_name__		:Union[str, Any]   =   [parquet_path]
				__magic_name__		:Optional[int]   =   tmp_path / '''cache'''
				__magic_name__		:Optional[int]   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:str   =   ParquetDatasetReader(snake_case,	cache_dir=snake_case			).read()
				_check_parquet_dataset(snake_case,	snake_case			)

def   __lowercase       (			snake_case,	snake_case,	snake_case=("train",)			):







				"""simple docstring"""






				assert isinstance(snake_case,	snake_case			)
				for split in splits:
								__magic_name__		:Optional[Any]   =   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   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Any   =   tmp_path / '''cache'''
				__magic_name__		:Optional[int]   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__magic_name__		:Tuple   =   ParquetDatasetReader(
								    {'''train''': parquet_path},	cache_dir=snake_case,	keep_in_memory=snake_case			).read()
				_check_parquet_datasetdict(snake_case,	snake_case			)

@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   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   tmp_path / '''cache'''
				__magic_name__		:Dict   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:int   =   features.copy() if features else default_expected_features
				__magic_name__		:List[Any]   =   (
				    Features({feature: Value(snake_case			) for feature, dtype in features.items()}			) if features is not None else None
				)
				__magic_name__		:Optional[int]   =   ParquetDatasetReader({'''train''': parquet_path},	features=snake_case,	cache_dir=snake_case			).read()
				_check_parquet_datasetdict(snake_case,	snake_case			)

@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''			), '''train''', '''test''']			)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				if split:
								__magic_name__		:Dict   =   {split: parquet_path}
				else:
								__magic_name__		:Optional[int]   =   '''train'''
								__magic_name__		:Dict   =   {'''train''': parquet_path, '''test''': parquet_path}
				__magic_name__		:List[Any]   =   tmp_path / '''cache'''
				__magic_name__		:Optional[Any]   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:Optional[Any]   =   ParquetDatasetReader(snake_case,	cache_dir=snake_case			).read()
				_check_parquet_datasetdict(snake_case,	snake_case,	splits=list(path.keys()			)			)
				assert all(dataset[split].split == split for split in path.keys()			)

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:str   =   ParquetDatasetWriter(snake_case,	tmp_path / '''foo.parquet'''			)
				assert writer.write() > 0
				__magic_name__		:List[Any]   =   pq.ParquetFile(tmp_path / '''foo.parquet'''			)
				__magic_name__		:List[Any]   =   pf.read()
				assert dataset.data.table == output_table

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   str(shared_datadir / '''test_image_rgb.jpg'''			)
				__magic_name__		:Tuple   =   {'''image''': [image_path]}
				__magic_name__		:List[Any]   =   Features({'''image''': Image()}			)
				__magic_name__		:Tuple   =   Dataset.from_dict(snake_case,	features=snake_case			)
				__magic_name__		:Union[str, Any]   =   ParquetDatasetWriter(snake_case,	tmp_path / '''foo.parquet'''			)
				assert writer.write() > 0

				__magic_name__		:List[str]   =   Dataset.from_parquet(str(tmp_path / '''foo.parquet'''			)			)
				assert dataset.features == reloaded_dataset.features

				__magic_name__		:List[str]   =   ParquetDatasetReader(str(tmp_path / '''foo.parquet'''			),	streaming=snake_case			).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   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				assert get_writer_batch_size(snake_case			) == expected






