


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


lowerCAmelCase           =		logging.get_logger(__name__)

lowerCAmelCase           =		{
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}





class       A    (   lowercase_ ):
							UpperCamelCase_   :							Any       ='''xglm'''
							UpperCamelCase_   :							Any       =['''past_key_values''']

							UpperCamelCase_   :							Any       ={
							    '''num_attention_heads''': '''attention_heads''',
							    '''hidden_size''': '''d_model''',
							    '''num_hidden_layers''': '''num_layers''',
							}


							def __init__(self      ,				lowerCAmelCase=2_5_6_0_0_8      ,				lowerCAmelCase=2_0_4_8      ,				lowerCAmelCase=1_0_2_4      ,				lowerCAmelCase=4_0_9_6      ,				lowerCAmelCase=2_4      ,				lowerCAmelCase=1_6      ,				lowerCAmelCase="gelu"      ,				lowerCAmelCase=0.1      ,				lowerCAmelCase=0.1      ,				lowerCAmelCase=0.0      ,				lowerCAmelCase=0.0      ,				lowerCAmelCase=0.02      ,				lowerCAmelCase=True      ,				lowerCAmelCase=True      ,				lowerCAmelCase=2      ,				lowerCAmelCase=1      ,				lowerCAmelCase=0      ,				lowerCAmelCase=2      ,				**lowerCAmelCase      ,				):
									__lowercase=			vocab_size
									__lowercase=			max_position_embeddings
									__lowercase=			d_model
									__lowercase=			ffn_dim
									__lowercase=			num_layers
									__lowercase=			attention_heads
									__lowercase=			activation_function
									__lowercase=			dropout
									__lowercase=			attention_dropout
									__lowercase=			activation_dropout
									__lowercase=			layerdrop
									__lowercase=			init_std
									__lowercase=			scale_embedding  # scale factor will be sqrt(d_model) if True
									__lowercase=			use_cache

									super().__init__(
									    pad_token_id=__UpperCamelCase      ,				bos_token_id=__UpperCamelCase      ,				eos_token_id=__UpperCamelCase      ,				decoder_start_token_id=__UpperCamelCase      ,				**__UpperCamelCase      ,				)













"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case     :		Optional[int]       						= logging.get_logger(__name__)

_snake_case     :		Optional[int]       						= {
    'google/vivit-b-16x2-kinetics400': (
        'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							'''vivit'''


					def __init__(					self			:Optional[Any]     ,       __UpperCamelCase			:Dict=2_24     ,       __UpperCamelCase			:int=32     ,       __UpperCamelCase			:Union[str, Any]=[2, 16, 16]     ,       __UpperCamelCase			:Optional[Any]=3     ,       __UpperCamelCase			:Optional[Any]=7_68     ,       __UpperCamelCase			:Any=12     ,       __UpperCamelCase			:List[str]=12     ,       __UpperCamelCase			:List[str]=30_72     ,       __UpperCamelCase			:Any="gelu_fast"     ,       __UpperCamelCase			:List[Any]=0.0     ,       __UpperCamelCase			:str=0.0     ,       __UpperCamelCase			:Dict=0.02     ,       __UpperCamelCase			:Optional[Any]=1e-06     ,       __UpperCamelCase			:Dict=True     ,       **__UpperCamelCase			:Tuple     ,       ):
												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  intermediate_size
												A		=  hidden_act
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  initializer_range
												A		=  layer_norm_eps

												A		=  image_size
												A		=  num_frames
												A		=  tubelet_size
												A		=  num_channels
												A		=  qkv_bias

												super().__init__(**__UpperCamelCase		)


