


import warnings

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


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

UpperCAmelCase_   :				Tuple						      = {
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  int       =		'''mvp'''
      snake_case__       :  Tuple       =		['''past_key_values''']
      snake_case__       :  str       =		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
      def __init__( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=5_0_2_6_7  ,							SCREAMING_SNAKE_CASE__       : Tuple=1_0_2_4  ,							SCREAMING_SNAKE_CASE__       : int=1_2  ,							SCREAMING_SNAKE_CASE__       : Any=4_0_9_6  ,							SCREAMING_SNAKE_CASE__       : Dict=1_6  ,							SCREAMING_SNAKE_CASE__       : int=1_2  ,							SCREAMING_SNAKE_CASE__       : List[str]=4_0_9_6  ,							SCREAMING_SNAKE_CASE__       : List[Any]=1_6  ,							SCREAMING_SNAKE_CASE__       : Tuple=0.0  ,							SCREAMING_SNAKE_CASE__       : List[str]=0.0  ,							SCREAMING_SNAKE_CASE__       : str="gelu"  ,							SCREAMING_SNAKE_CASE__       : List[str]=1_0_2_4  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=0.1  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=0.0  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=0.0  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=0.02  ,							SCREAMING_SNAKE_CASE__       : str=0.0  ,							SCREAMING_SNAKE_CASE__       : Any=False  ,							SCREAMING_SNAKE_CASE__       : Any=True  ,							SCREAMING_SNAKE_CASE__       : Dict=1  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=0  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=2  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=True  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=2  ,							SCREAMING_SNAKE_CASE__       : Tuple=2  ,							SCREAMING_SNAKE_CASE__       : int=False  ,							SCREAMING_SNAKE_CASE__       : Dict=1_0_0  ,							SCREAMING_SNAKE_CASE__       : List[Any]=8_0_0  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  ,							)	->    Any:
             a_      :	List[str]							=						vocab_size
             a_      :	int							=						max_position_embeddings
             a_      :	int							=						d_model
             a_      :	int							=						encoder_ffn_dim
             a_      :	List[Any]							=						encoder_layers
             a_      :	int							=						encoder_attention_heads
             a_      :	Dict							=						decoder_ffn_dim
             a_      :	str							=						decoder_layers
             a_      :	Any							=						decoder_attention_heads
             a_      :	Union[str, Any]							=						dropout
             a_      :	Optional[Any]							=						attention_dropout
             a_      :	Union[str, Any]							=						activation_dropout
             a_      :	Optional[Any]							=						activation_function
             a_      :	Any							=						init_std
             a_      :	Optional[int]							=						encoder_layerdrop
             a_      :	List[str]							=						decoder_layerdrop
             a_      :	Tuple							=						classifier_dropout
             a_      :	str							=						use_cache
             a_      :	Tuple							=						encoder_layers
             a_      :	Optional[Any]							=						scale_embedding  # scale factor will be sqrt(d_model) if True
             a_      :	Optional[int]							=						use_prompt
             a_      :	Tuple							=						prompt_length
             a_      :	int							=						prompt_mid_dim

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

             if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	List[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  SCREAMING_SNAKE_CASE_		(    __A				:    Union[str, Any]						,					__A				:    Tuple       )			->	Any:

       """simple docstring"""

       assert isinstance(__A						,					__A       )
       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  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]						,					__A				:    Any						,					__A				:    Tuple       )			->	Tuple:

       """simple docstring"""

       a_      :	Dict							=						tmp_path / 'cache'
       a_      :	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():
              a_      :	Union[str, Any]							=						ParquetDatasetReader(__A						,					cache_dir=__A						,					keep_in_memory=__A       ).read()
       _check_parquet_dataset(__A						,					__A       )







@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  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    Dict						,					__A				:    Union[str, Any]       )			->	Dict:

       """simple docstring"""

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







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

       """simple docstring"""

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







@pytest.mark.parametrize('path_type'						,					[str, list]       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple						,					__A				:    str						,					__A				:    Optional[int]       )			->	Any:

       """simple docstring"""

       if issubclass(__A						,					__A       ):
              a_      :	Tuple							=						parquet_path
       elif issubclass(__A						,					__A       ):
              a_      :	str							=						[parquet_path]
       a_      :	int							=						tmp_path / 'cache'
       a_      :	int							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       a_      :	str							=						ParquetDatasetReader(__A						,					cache_dir=__A       ).read()
       _check_parquet_dataset(__A						,					__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Any						,					__A				:    Dict						,					__A				:    Optional[Any]=("train",)       )			->	Optional[int]:

       """simple docstring"""

       assert isinstance(__A						,					__A       )
       for split in splits:
              a_      :	str							=						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  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    str						,					__A				:    str       )			->	Union[str, Any]:

       """simple docstring"""

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







@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  SCREAMING_SNAKE_CASE_		(    __A				:    Any						,					__A				:    Optional[int]						,					__A				:    Tuple       )			->	List[Any]:

       """simple docstring"""

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







@pytest.mark.parametrize('split'						,					[None, NamedSplit('train'       ), 'train', 'test']       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    List[Any]						,					__A				:    Optional[Any]       )			->	Any:

       """simple docstring"""

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







def  SCREAMING_SNAKE_CASE_		(    __A				:    Any						,					__A				:    Optional[int]       )			->	List[Any]:

       """simple docstring"""

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







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    Any       )			->	Optional[int]:

       """simple docstring"""

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

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

       a_      :	str							=						ParquetDatasetReader(str(tmp_path / 'foo.parquet'       )						,					streaming=__A       ).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  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    List[str]       )			->	List[str]:

       """simple docstring"""

       assert get_writer_batch_size(__A       ) == expected


