


"""simple docstring"""





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


snake_case__		:     Dict    				=				logging.get_logger(__name__)

snake_case__		:     str    				=				{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}



class 		snake_case_(       __lowercase			):
					__UpperCamelCase  =					'''gpt_neox'''






					def __init__(	self  :					Union[str, Any]					,				UpperCamelCase_  :					Union[str, Any]=5_0_4_3_2					,				UpperCamelCase_  :					List[Any]=6_1_4_4					,				UpperCamelCase_  :					int=4_4					,				UpperCamelCase_  :					int=6_4					,				UpperCamelCase_  :					Optional[Any]=2_4_5_7_6					,				UpperCamelCase_  :					Any="gelu"					,				UpperCamelCase_  :					Tuple=0.25					,				UpperCamelCase_  :					Union[str, Any]=1_0_0_0_0					,				UpperCamelCase_  :					Tuple=0.0					,				UpperCamelCase_  :					Any=0.0					,				UpperCamelCase_  :					int=0.1					,				UpperCamelCase_  :					List[str]=2_0_4_8					,				UpperCamelCase_  :					Dict=0.02					,				UpperCamelCase_  :					Optional[Any]=1E-5					,				UpperCamelCase_  :					Tuple=True					,				UpperCamelCase_  :					List[Any]=0					,				UpperCamelCase_  :					Optional[int]=2					,				UpperCamelCase_  :					Optional[int]=False					,				UpperCamelCase_  :					List[Any]=True					,				UpperCamelCase_  :					Any=None					,				**UpperCamelCase_  :					Any					,				):
											super().__init__(bos_token_id=_A					,				eos_token_id=_A					,				**_A				)
											lowerCAmelCase					:	Any       =   vocab_size
											lowerCAmelCase					:	List[Any]       =   max_position_embeddings
											lowerCAmelCase					:	Tuple       =   hidden_size
											lowerCAmelCase					:	str       =   num_hidden_layers
											lowerCAmelCase					:	List[str]       =   num_attention_heads
											lowerCAmelCase					:	List[str]       =   intermediate_size
											lowerCAmelCase					:	Optional[Any]       =   hidden_act
											lowerCAmelCase					:	Union[str, Any]       =   rotary_pct
											lowerCAmelCase					:	List[str]       =   rotary_emb_base
											lowerCAmelCase					:	Optional[Any]       =   attention_dropout
											lowerCAmelCase					:	Any       =   hidden_dropout
											lowerCAmelCase					:	List[Any]       =   classifier_dropout
											lowerCAmelCase					:	int       =   initializer_range
											lowerCAmelCase					:	str       =   layer_norm_eps
											lowerCAmelCase					:	Optional[Any]       =   use_cache
											lowerCAmelCase					:	Optional[int]       =   tie_word_embeddings
											lowerCAmelCase					:	Union[str, Any]       =   use_parallel_residual
											lowerCAmelCase					:	List[Any]       =   rope_scaling
											self._rope_scaling_validation()

											if self.hidden_size % self.num_attention_heads != 0:
																	raise ValueError(
																	    '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''				)






					def      lowerCamelCase__     (	self  :					str				):
											if self.rope_scaling is None:
																	return

											if not isinstance(self.rope_scaling					,				_A				) or len(self.rope_scaling				) != 2:
																	raise ValueError(
																	    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
																	    F'''got {self.rope_scaling}'''				)
											lowerCAmelCase					:	Any       =   self.rope_scaling.get('''type'''					,				_A				)
											lowerCAmelCase					:	List[Any]       =   self.rope_scaling.get('''factor'''					,				_A				)
											if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
																	raise ValueError(
																	    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''				)
											if rope_scaling_factor is None or not isinstance(_A					,				_A				) or rope_scaling_factor <= 1.0:
																	raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''				)






"""simple docstring"""





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


snake_case__		:     Tuple    				=				logging.get_logger(__name__)

snake_case__		:     int    				=				{
    '''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 		snake_case_(       a__			):
     __UpperCamelCase  =					'''levit'''






     def __init__(	self  :					str					,				UpperCamelCase_  :					Union[str, Any]=2_2_4					,				UpperCamelCase_  :					Union[str, Any]=3					,				UpperCamelCase_  :					Union[str, Any]=3					,				UpperCamelCase_  :					int=2					,				UpperCamelCase_  :					Union[str, Any]=1					,				UpperCamelCase_  :					Tuple=1_6					,				UpperCamelCase_  :					Dict=[1_2_8, 2_5_6, 3_8_4]					,				UpperCamelCase_  :					Optional[Any]=[4, 8, 1_2]					,				UpperCamelCase_  :					Dict=[4, 4, 4]					,				UpperCamelCase_  :					Any=[1_6, 1_6, 1_6]					,				UpperCamelCase_  :					str=0					,				UpperCamelCase_  :					int=[2, 2, 2]					,				UpperCamelCase_  :					Optional[Any]=[2, 2, 2]					,				UpperCamelCase_  :					str=0.02					,				**UpperCamelCase_  :					List[str]					,				):
           super().__init__(**UpperCamelCase_				)
           lowerCAmelCase					:	Tuple       =   image_size
           lowerCAmelCase					:	int       =   num_channels
           lowerCAmelCase					:	Optional[int]       =   kernel_size
           lowerCAmelCase					:	Dict       =   stride
           lowerCAmelCase					:	List[Any]       =   padding
           lowerCAmelCase					:	Dict       =   hidden_sizes
           lowerCAmelCase					:	List[str]       =   num_attention_heads
           lowerCAmelCase					:	Tuple       =   depths
           lowerCAmelCase					:	Dict       =   key_dim
           lowerCAmelCase					:	Union[str, Any]       =   drop_path_rate
           lowerCAmelCase					:	List[Any]       =   patch_size
           lowerCAmelCase					:	Tuple       =   attention_ratio
           lowerCAmelCase					:	Optional[int]       =   mlp_ratio
           lowerCAmelCase					:	Union[str, Any]       =   initializer_range
           lowerCAmelCase					:	List[str]       =   [
               ['''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 		snake_case_(       a__			):
     __UpperCamelCase  =					version.parse('''1.11'''			)






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






     @property
     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           return 1E-4


