
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


a											=							logging.get_logger(__name__)

a											=							{
    'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}


class 	UpperCAmelCase_ (__SCREAMING_SNAKE_CASE							):
	"""simple docstring"""

	lowerCamelCase     :       Tuple            =			'levit'







	def __init__(     self:     Dict							,   _UpperCAmelCase:     Tuple=224							,   _UpperCAmelCase:     List[str]=3							,   _UpperCAmelCase:     List[str]=3							,   _UpperCAmelCase:     List[Any]=2							,   _UpperCAmelCase:     Tuple=1							,   _UpperCAmelCase:     Tuple=16							,   _UpperCAmelCase:     List[str]=[128, 256, 384]							,   _UpperCAmelCase:     Dict=[4, 8, 12]							,   _UpperCAmelCase:     Optional[Any]=[4, 4, 4]							,   _UpperCAmelCase:     Optional[Any]=[16, 16, 16]							,   _UpperCAmelCase:     Dict=0							,   _UpperCAmelCase:     Union[str, Any]=[2, 2, 2]							,   _UpperCAmelCase:     Optional[int]=[2, 2, 2]							,   _UpperCAmelCase:     List[Any]=0.0_2							,   **_UpperCAmelCase:     int							,   ):
								super().__init__(**_SCREAMING_SNAKE_CASE			)
								_lowerCAmelCase :int      	=						image_size
								_lowerCAmelCase :Dict      	=						num_channels
								_lowerCAmelCase :int      	=						kernel_size
								_lowerCAmelCase :Dict      	=						stride
								_lowerCAmelCase :Optional[Any]      	=						padding
								_lowerCAmelCase :str      	=						hidden_sizes
								_lowerCAmelCase :Any      	=						num_attention_heads
								_lowerCAmelCase :int      	=						depths
								_lowerCAmelCase :int      	=						key_dim
								_lowerCAmelCase :int      	=						drop_path_rate
								_lowerCAmelCase :Optional[int]      	=						patch_size
								_lowerCAmelCase :Any      	=						attention_ratio
								_lowerCAmelCase :List[str]      	=						mlp_ratio
								_lowerCAmelCase :int      	=						initializer_range
								_lowerCAmelCase :Optional[int]      	=						[
								    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
								    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
								]





class 	UpperCAmelCase_ (__SCREAMING_SNAKE_CASE							):
	"""simple docstring"""

	lowerCamelCase     :       Tuple            =			version.parse('1.11'							)







	@property
	def       SCREAMING_SNAKE_CASE__     (     self:     List[str]			):
								return OrderedDict(
								    [
								        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
								    ]			)







	@property
	def       SCREAMING_SNAKE_CASE__     (     self:     Any			):
								return 1e-4




"""simple docstring"""






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


__snake_case				:  Tuple =       logging.get_logger(__name__)

__snake_case				:  Any =       {
    '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__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'xglm'
	SCREAMING_SNAKE_CASE        =      ['past_key_values']

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

	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						List[Any]=25_6008			,     _SCREAMING_SNAKE_CASE:						Dict=2048			,     _SCREAMING_SNAKE_CASE:						int=1024			,     _SCREAMING_SNAKE_CASE:						Dict=4096			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=24			,     _SCREAMING_SNAKE_CASE:						int=16			,     _SCREAMING_SNAKE_CASE:						List[str]="gelu"			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=0.1			,     _SCREAMING_SNAKE_CASE:						int=0.1			,     _SCREAMING_SNAKE_CASE:						List[Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Any=0.02			,     _SCREAMING_SNAKE_CASE:						Any=True			,     _SCREAMING_SNAKE_CASE:						Tuple=True			,     _SCREAMING_SNAKE_CASE:						Any=2			,     _SCREAMING_SNAKE_CASE:						str=1			,     _SCREAMING_SNAKE_CASE:						Dict=0			,     _SCREAMING_SNAKE_CASE:						Dict=2			,     **_SCREAMING_SNAKE_CASE:						List[Any]			,     )							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			vocab_size
				__lowerCAmelCase :    int				=			max_position_embeddings
				__lowerCAmelCase :    Optional[Any]				=			d_model
				__lowerCAmelCase :    List[Any]				=			ffn_dim
				__lowerCAmelCase :    int				=			num_layers
				__lowerCAmelCase :    Any				=			attention_heads
				__lowerCAmelCase :    int				=			activation_function
				__lowerCAmelCase :    List[Any]				=			dropout
				__lowerCAmelCase :    Optional[int]				=			attention_dropout
				__lowerCAmelCase :    Optional[int]				=			activation_dropout
				__lowerCAmelCase :    Optional[int]				=			layerdrop
				__lowerCAmelCase :    Optional[int]				=			init_std
				__lowerCAmelCase :    Tuple				=			scale_embedding  # scale factor will be sqrt(d_model) if True
				__lowerCAmelCase :    Dict				=			use_cache

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