



"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a           =							logging.get_logger(__name__)

__a           =							{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     int		=	MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
					_A						:     Optional[int]		=	"""mobilebert"""






					def __init__(			self:				int		,	snake_case:				str=30_522		,	snake_case:				List[Any]=512		,	snake_case:				Any=24		,	snake_case:				List[str]=4		,	snake_case:				Optional[Any]=512		,	snake_case:				List[str]="relu"		,	snake_case:				Optional[Any]=0.0		,	snake_case:				Tuple=0.1		,	snake_case:				Tuple=512		,	snake_case:				int=2		,	snake_case:				List[Any]=0.0_2		,	snake_case:				List[Any]=1E-12		,	snake_case:				int=0		,	snake_case:				List[str]=128		,	snake_case:				str=True		,	snake_case:				Any=True		,	snake_case:				Optional[int]=128		,	snake_case:				List[Any]=False		,	snake_case:				Optional[int]=True		,	snake_case:				List[Any]=4		,	snake_case:				List[str]="no_norm"		,	snake_case:				List[str]=True		,	snake_case:				Any=None		,	**snake_case:				Tuple		,	)  ->      str:
											super().__init__(pad_token_id=snake_case		,	**snake_case		)

											snake_case_					:Tuple		=     vocab_size
											snake_case_					:int		=     hidden_size
											snake_case_					:Optional[int]		=     num_hidden_layers
											snake_case_					:Optional[int]		=     num_attention_heads
											snake_case_					:Union[str, Any]		=     hidden_act
											snake_case_					:Optional[int]		=     intermediate_size
											snake_case_					:List[str]		=     hidden_dropout_prob
											snake_case_					:List[str]		=     attention_probs_dropout_prob
											snake_case_					:Dict		=     max_position_embeddings
											snake_case_					:int		=     type_vocab_size
											snake_case_					:Optional[Any]		=     initializer_range
											snake_case_					:str		=     layer_norm_eps
											snake_case_					:int		=     embedding_size
											snake_case_					:Any		=     trigram_input
											snake_case_					:List[str]		=     use_bottleneck
											snake_case_					:Any		=     intra_bottleneck_size
											snake_case_					:List[str]		=     use_bottleneck_attention
											snake_case_					:Union[str, Any]		=     key_query_shared_bottleneck
											snake_case_					:Optional[Any]		=     num_feedforward_networks
											snake_case_					:List[Any]		=     normalization_type
											snake_case_					:List[str]		=     classifier_activation

											if self.use_bottleneck:
																	snake_case_					:Optional[int]		=     intra_bottleneck_size
											else:
																	snake_case_					:Optional[int]		=     hidden_size

											snake_case_					:Any		=     classifier_dropout

class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					@property
					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
																	snake_case_					:List[Any]		=     {0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
																	snake_case_					:List[str]		=     {0: """batch""", 1: """sequence"""}
											return OrderedDict(
											    [
											        ("""input_ids""", dynamic_axis),
											        ("""attention_mask""", dynamic_axis),
											        ("""token_type_ids""", dynamic_axis),
											    ]		)

'''simple docstring'''





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


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

UpperCamelCase       :     Union[str, Any]			=    {
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    int				=			"tvlt"





    def __init__(					self     :						List[str]							,							UpperCAmelCase_     :						Any=2_2_4							,							UpperCAmelCase_     :						Optional[Any]=2_0_4_8							,							UpperCAmelCase_     :						Dict=1_2_8							,							UpperCAmelCase_     :						str=[1_6, 1_6]							,							UpperCAmelCase_     :						int=[1_6, 1_6]							,							UpperCAmelCase_     :						int=3							,							UpperCAmelCase_     :						Optional[int]=1							,							UpperCAmelCase_     :						List[str]=8							,							UpperCAmelCase_     :						List[Any]=7_6_8							,							UpperCAmelCase_     :						int=1_2							,							UpperCAmelCase_     :						List[Any]=1_2							,							UpperCAmelCase_     :						Optional[Any]=3_0_7_2							,							UpperCAmelCase_     :						List[str]="gelu"							,							UpperCAmelCase_     :						Tuple=0.0							,							UpperCAmelCase_     :						Optional[Any]=0.0							,							UpperCAmelCase_     :						Tuple=0.02							,							UpperCAmelCase_     :						Any=1e-6							,							UpperCAmelCase_     :						Dict=True							,							UpperCAmelCase_     :						Union[str, Any]=False							,							UpperCAmelCase_     :						Tuple=1_6							,							UpperCAmelCase_     :						str=5_1_2							,							UpperCAmelCase_     :						List[str]=8							,							UpperCAmelCase_     :						Union[str, Any]=2_0_4_8							,							UpperCAmelCase_     :						Tuple=0.75							,							UpperCAmelCase_     :						Optional[Any]=0.15							,							UpperCAmelCase_     :						Union[str, Any]="frame-level"							,							UpperCAmelCase_     :						List[str]=True							,							UpperCAmelCase_     :						Any=True							,							UpperCAmelCase_     :						Optional[int]="classification"							,							**UpperCAmelCase_     :						Any							,							):


         """simple docstring"""







         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					:							str							= image_size
         a					:							List[Any]							= spectrogram_length
         a					:							Union[str, Any]							= frequency_length
         a					:							str							= image_patch_size
         a					:							Dict							= audio_patch_size
         a					:							Dict							= num_image_channels
         a					:							Tuple							= num_audio_channels
         a					:							Optional[int]							= num_frames

         a					:							str							= hidden_size
         a					:							List[Any]							= num_hidden_layers
         a					:							Optional[Any]							= num_attention_heads
         a					:							Any							= intermediate_size
         a					:							List[Any]							= hidden_act
         a					:							int							= hidden_dropout_prob
         a					:							Optional[Any]							= attention_probs_dropout_prob
         a					:							Optional[Any]							= initializer_range
         a					:							List[str]							= layer_norm_eps
         a					:							List[Any]							= qkv_bias
         a					:							Dict							= use_mean_pooling

         a					:							List[Any]							= decoder_num_attention_heads
         a					:							Any							= decoder_hidden_size
         a					:							int							= decoder_num_hidden_layers
         a					:							Tuple							= decoder_intermediate_size
         a					:							Tuple							= pixel_mask_ratio
         a					:							List[Any]							= audio_mask_ratio
         a					:							Optional[Any]							= audio_mask_type

         a					:							Any							= task_matching
         a					:							Optional[Any]							= task_mae
         a					:							List[str]							= loss_type

