





'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     def __init__(   self							:			Optional[Any]   ,       *__snake_case							:			int   ,       **__snake_case							:			Optional[int]						)->	None:
        warnings.warn(
            """The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
            """ Please use ChineseCLIPImageProcessor instead."""   ,       __snake_case   ,       )
        super().__init__(*__snake_case   ,       **__snake_case						)








'''simple docstring'''




import logging

from transformers.configuration_utils import PretrainedConfig


_SCREAMING_SNAKE_CASE							     =			logging.getLogger(__name__)
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "masked_bert"



     def __init__(   self							:			Any   ,       __snake_case							:			Optional[Any]=3_05_22   ,       __snake_case							:			List[Any]=7_68   ,       __snake_case							:			int=12   ,       __snake_case							:			List[str]=12   ,       __snake_case							:			int=30_72   ,       __snake_case							:			Optional[int]="gelu"   ,       __snake_case							:			Any=0.1   ,       __snake_case							:			Tuple=0.1   ,       __snake_case							:			List[str]=5_12   ,       __snake_case							:			Tuple=2   ,       __snake_case							:			Tuple=0.02   ,       __snake_case							:			Tuple=1e-12   ,       __snake_case							:			Union[str, Any]=0   ,       __snake_case							:			int="topK"   ,       __snake_case							:			int="constant"   ,       __snake_case							:			List[str]=0.0   ,       **__snake_case							:			Any   ,       )->	Union[str, Any]:
        super().__init__(pad_token_id=__snake_case   ,       **__snake_case						)

        snake_case 						=   vocab_size
        snake_case 						=   hidden_size
        snake_case 						=   num_hidden_layers
        snake_case 						=   num_attention_heads
        snake_case 						=   hidden_act
        snake_case 						=   intermediate_size
        snake_case 						=   hidden_dropout_prob
        snake_case 						=   attention_probs_dropout_prob
        snake_case 						=   max_position_embeddings
        snake_case 						=   type_vocab_size
        snake_case 						=   initializer_range
        snake_case 						=   layer_norm_eps
        snake_case 						=   pruning_method
        snake_case 						=   mask_init
        snake_case 						=   mask_scale

