


'''simple docstring'''





import copy
import os
from typing import Union

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


UpperCAmelCase_   :				Optional[Any]						      = logging.get_logger(__name__)

UpperCAmelCase_   :				List[Any]						      = {
    '''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json''',
    '''BridgeTower/bridgetower-base-itm-mlm''': (
        '''https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'''
    ),
}
class 				SCREAMING_SNAKE_CASE__						(       _UpperCAmelCase    ):
      snake_case__       :  str       =		'bridgetower_vision_model'
      def __init__( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=7_6_8  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=1_2  ,							SCREAMING_SNAKE_CASE__       : Dict=3  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=1_6  ,							SCREAMING_SNAKE_CASE__       : str=2_8_8  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=1  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=1E-05  ,							SCREAMING_SNAKE_CASE__       : Dict=False  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=True  ,							SCREAMING_SNAKE_CASE__       : Tuple=False  ,							**SCREAMING_SNAKE_CASE__       : Tuple  ,							)	->    int:
             super().__init__(**__UpperCamelCase  )
             a_      :	str							=						hidden_size
             a_      :	Optional[Any]							=						num_hidden_layers
             a_      :	Optional[int]							=						num_channels
             a_      :	Any							=						patch_size
             a_      :	Optional[Any]							=						image_size
             a_      :	Union[str, Any]							=						initializer_factor
             a_      :	Any							=						layer_norm_eps
             a_      :	List[Any]							=						stop_gradient
             a_      :	str							=						share_layernorm
             a_      :	List[Any]							=						remove_last_layer

      @classmethod
      def  SCREAMING_SNAKE_CASE							( cls       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Union[str, os.PathLike]  ,							**SCREAMING_SNAKE_CASE__       : int  )	->    "PretrainedConfig":
             a_							,	a_      :	Optional[Any]							=						cls.get_config_dict(__UpperCamelCase  ,							**__UpperCamelCase  )

             if config_dict.get('model_type'  ) == "bridgetower":
                    a_      :	int							=						config_dict['text_config']

             if "model_type" in config_dict and hasattr(cls  ,							'model_type'  ) and config_dict["model_type"] != cls.model_type:
                    logger.warning(
                        F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                        F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""  )

             return cls.from_dict(__UpperCamelCase  ,							**__UpperCamelCase  )
class 				SCREAMING_SNAKE_CASE__						(       _UpperCAmelCase    ):
      snake_case__       :  List[Any]       =		'bridgetower_text_model'
      def __init__( self       : str  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=5_0_2_6_5  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=7_6_8  ,							SCREAMING_SNAKE_CASE__       : Any=1_2  ,							SCREAMING_SNAKE_CASE__       : Tuple=1_2  ,							SCREAMING_SNAKE_CASE__       : int=1  ,							SCREAMING_SNAKE_CASE__       : List[str]=3_0_7_2  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]="gelu"  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=0.1  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=0.1  ,							SCREAMING_SNAKE_CASE__       : List[str]=5_1_4  ,							SCREAMING_SNAKE_CASE__       : Dict=1  ,							SCREAMING_SNAKE_CASE__       : Any=1E-05  ,							SCREAMING_SNAKE_CASE__       : List[str]=1  ,							SCREAMING_SNAKE_CASE__       : List[Any]=0  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=2  ,							SCREAMING_SNAKE_CASE__       : str="absolute"  ,							SCREAMING_SNAKE_CASE__       : List[str]=True  ,							**SCREAMING_SNAKE_CASE__       : Dict  ,							)	->    Union[str, Any]:
             super().__init__(**__UpperCamelCase  )

             a_      :	Optional[int]							=						vocab_size
             a_      :	Dict							=						hidden_size
             a_      :	Optional[int]							=						num_hidden_layers
             a_      :	Tuple							=						num_attention_heads
             a_      :	List[Any]							=						hidden_act
             a_      :	List[str]							=						initializer_factor
             a_      :	Dict							=						intermediate_size
             a_      :	Union[str, Any]							=						hidden_dropout_prob
             a_      :	List[Any]							=						attention_probs_dropout_prob
             a_      :	str							=						max_position_embeddings
             a_      :	List[Any]							=						type_vocab_size
             a_      :	Dict							=						layer_norm_eps
             a_      :	Optional[int]							=						position_embedding_type
             a_      :	Optional[int]							=						use_cache
             a_      :	List[str]							=						pad_token_id
             a_      :	Tuple							=						bos_token_id
             a_      :	int							=						eos_token_id

      @classmethod
      def  SCREAMING_SNAKE_CASE							( cls       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Union[str, os.PathLike]  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  )	->    "PretrainedConfig":
             a_							,	a_      :	List[Any]							=						cls.get_config_dict(__UpperCamelCase  ,							**__UpperCamelCase  )

             if config_dict.get('model_type'  ) == "bridgetower":
                    a_      :	Optional[Any]							=						config_dict['text_config']

             if "model_type" in config_dict and hasattr(cls  ,							'model_type'  ) and config_dict["model_type"] != cls.model_type:
                    logger.warning(
                        F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                        F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""  )

             return cls.from_dict(__UpperCamelCase  ,							**__UpperCamelCase  )
class 				SCREAMING_SNAKE_CASE__						(       _UpperCAmelCase    ):
      snake_case__       :  Dict       =		'bridgetower'
      def __init__( self       : Any  ,							SCREAMING_SNAKE_CASE__       : Tuple=True  ,							SCREAMING_SNAKE_CASE__       : Any="gelu"  ,							SCREAMING_SNAKE_CASE__       : str=7_6_8  ,							SCREAMING_SNAKE_CASE__       : Dict=1  ,							SCREAMING_SNAKE_CASE__       : List[Any]=1E-05  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=False  ,							SCREAMING_SNAKE_CASE__       : str="add"  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=1_2  ,							SCREAMING_SNAKE_CASE__       : Tuple=6  ,							SCREAMING_SNAKE_CASE__       : Any=False  ,							SCREAMING_SNAKE_CASE__       : str=False  ,							SCREAMING_SNAKE_CASE__       : Tuple=None  ,							SCREAMING_SNAKE_CASE__       : Any=None  ,							**SCREAMING_SNAKE_CASE__       : Optional[int]  ,							)	->    List[Any]:
             a_      :	List[str]							=						kwargs.pop('text_config_dict'  ,							__UpperCamelCase  )
             a_      :	int							=						kwargs.pop('vision_config_dict'  ,							__UpperCamelCase  )

             super().__init__(**__UpperCamelCase  )
             a_      :	Tuple							=						share_cross_modal_transformer_layers
             a_      :	int							=						hidden_act
             a_      :	str							=						hidden_size
             a_      :	Dict							=						initializer_factor
             a_      :	Optional[int]							=						layer_norm_eps
             a_      :	List[Any]							=						share_link_tower_layers
             a_      :	Union[str, Any]							=						link_tower_type
             a_      :	int							=						num_attention_heads
             a_      :	str							=						num_hidden_layers
             a_      :	Dict							=						tie_word_embeddings
             a_      :	Dict							=						init_layernorm_from_vision_encoder

             if text_config is None:
                    a_      :	List[str]							=						{}
                    logger.info('`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'  )

             if vision_config is None:
                    a_      :	Union[str, Any]							=						{}
                    logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'  )

             a_      :	Optional[Any]							=						BridgeTowerTextConfig(**__UpperCamelCase  )
             a_      :	Optional[Any]							=						BridgeTowerVisionConfig(**__UpperCamelCase  )
      @classmethod
      def  SCREAMING_SNAKE_CASE							( cls       : List[str]  ,							SCREAMING_SNAKE_CASE__       : BridgeTowerTextConfig  ,							SCREAMING_SNAKE_CASE__       : BridgeTowerVisionConfig  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  )	->    Optional[Any]:
             return cls(text_config=text_config.to_dict()  ,							vision_config=vision_config.to_dict()  ,							**__UpperCamelCase  )

      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    List[Any]:
             a_      :	List[str]							=						copy.deepcopy(self.__dict__  )
             a_      :	str							=						self.text_config.to_dict()
             a_      :	Tuple							=						self.vision_config.to_dict()
             a_      :	Tuple							=						self.__class__.model_type
             return output






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


