



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


__snake_case						:List[Any]						=					logging.get_logger(__name__)

__snake_case						:Optional[Any]						=					{
    '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   Tuple						 =						'''lxmert'''
       UpperCamelCase__  :   Any						 =						{}



       def __init__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=30_522					,		__SCREAMING_SNAKE_CASE							:					Tuple=768					,		__SCREAMING_SNAKE_CASE							:					int=12					,		__SCREAMING_SNAKE_CASE							:					List[Any]=9_500					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=1_600					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=400					,		__SCREAMING_SNAKE_CASE							:					int=3_072					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]="gelu"					,		__SCREAMING_SNAKE_CASE							:					List[Any]=0.1					,		__SCREAMING_SNAKE_CASE							:					Dict=0.1					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=512					,		__SCREAMING_SNAKE_CASE							:					Any=2					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=0.02					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=1E-12					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=9					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=5					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=5					,		__SCREAMING_SNAKE_CASE							:					Any=2_048					,		__SCREAMING_SNAKE_CASE							:					int=4					,		__SCREAMING_SNAKE_CASE							:					Tuple=6.67					,		__SCREAMING_SNAKE_CASE							:					str=True					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=True					,		__SCREAMING_SNAKE_CASE							:					Any=True					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=True					,		__SCREAMING_SNAKE_CASE							:					str=True					,		__SCREAMING_SNAKE_CASE							:					List[str]=True					,		__SCREAMING_SNAKE_CASE							:					List[Any]=True					,		**__SCREAMING_SNAKE_CASE							:					str					,		):


           '''simple docstring'''

           __a							=    vocab_size
           __a							=    hidden_size
           __a							=    num_attention_heads
           __a							=    hidden_act
           __a							=    intermediate_size
           __a							=    hidden_dropout_prob
           __a							=    attention_probs_dropout_prob
           __a							=    max_position_embeddings
           __a							=    type_vocab_size
           __a							=    initializer_range
           __a							=    layer_norm_eps
           __a							=    num_qa_labels
           __a							=    num_object_labels
           __a							=    num_attr_labels
           __a							=    l_layers
           __a							=    x_layers
           __a							=    r_layers
           __a							=    visual_feat_dim
           __a							=    visual_pos_dim
           __a							=    visual_loss_normalizer
           __a							=    task_matched
           __a							=    task_mask_lm
           __a							=    task_obj_predict
           __a							=    task_qa
           __a							=    visual_obj_loss
           __a							=    visual_attr_loss
           __a							=    visual_feat_loss
           __a							=    {'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
           super().__init__(**__SCREAMING_SNAKE_CASE)










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


__snake_case						:Dict						=					logging.get_logger(__name__)

__snake_case						:List[Any]						=					{
    '''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
        '''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
    ),
}

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   List[str]						 =						'''gptsan-japanese'''
       UpperCamelCase__  :   Dict						 =						[
           '''past_key_values''',
       ]
       UpperCamelCase__  :   Dict						 =						{
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''num_heads''',
           '''num_hidden_layers''': '''num_layers''',
       }



       def __init__(		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=36_000					,		__SCREAMING_SNAKE_CASE							:					Tuple=1_280					,		__SCREAMING_SNAKE_CASE							:					List[Any]=1_024					,		__SCREAMING_SNAKE_CASE							:					List[Any]=8_192					,		__SCREAMING_SNAKE_CASE							:					str=4_096					,		__SCREAMING_SNAKE_CASE							:					Any=128					,		__SCREAMING_SNAKE_CASE							:					int=10					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=0					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=16					,		__SCREAMING_SNAKE_CASE							:					List[Any]=16					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=128					,		__SCREAMING_SNAKE_CASE							:					Tuple=0.0					,		__SCREAMING_SNAKE_CASE							:					List[Any]=1E-5					,		__SCREAMING_SNAKE_CASE							:					str=False					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=0.0					,		__SCREAMING_SNAKE_CASE							:					List[str]="float32"					,		__SCREAMING_SNAKE_CASE							:					List[str]=False					,		__SCREAMING_SNAKE_CASE							:					Any=False					,		__SCREAMING_SNAKE_CASE							:					Any=False					,		__SCREAMING_SNAKE_CASE							:					int=0.0_02					,		__SCREAMING_SNAKE_CASE							:					Any=False					,		__SCREAMING_SNAKE_CASE							:					Tuple=True					,		__SCREAMING_SNAKE_CASE							:					int=35_998					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=35_995					,		__SCREAMING_SNAKE_CASE							:					List[str]=35_999					,		**__SCREAMING_SNAKE_CASE							:					List[str]					,		):


           '''simple docstring'''

           __a							=    vocab_size
           __a							=    max_position_embeddings
           __a							=    d_model
           __a							=    d_ff
           __a							=    d_ext
           __a							=    d_spout
           __a							=    num_switch_layers
           __a							=    num_ext_layers
           __a							=    num_switch_layers + num_ext_layers
           __a							=    num_heads
           __a							=    num_experts
           __a							=    expert_capacity
           __a							=    dropout_rate
           __a							=    layer_norm_epsilon
           __a							=    router_bias
           __a							=    router_jitter_noise
           __a							=    router_dtype
           __a							=    router_ignore_padding_tokens
           __a							=    output_hidden_states
           __a							=    output_attentions
           __a							=    initializer_factor
           __a							=    output_router_logits
           __a							=    use_cache

           super().__init__(
               separator_token_id=__SCREAMING_SNAKE_CASE					,		pad_token_id=__SCREAMING_SNAKE_CASE					,		eos_token_id=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)





