






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


SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__				     =			{
    '''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class 			_UpperCamelCase(       __lowerCamelCase		):
		__SCREAMING_SNAKE_CASE  :       int							=							'''glpn'''

		def __init__(					self		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    Dict=3    ,       SCREAMING_SNAKE_CASE__		:    List[Any]=4    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]=[2, 2, 2, 2]    ,       SCREAMING_SNAKE_CASE__		:    List[Any]=[8, 4, 2, 1]    ,       SCREAMING_SNAKE_CASE__		:    str=[3_2, 6_4, 1_6_0, 2_5_6]    ,       SCREAMING_SNAKE_CASE__		:    Dict=[7, 3, 3, 3]    ,       SCREAMING_SNAKE_CASE__		:    Tuple=[4, 2, 2, 2]    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]=[1, 2, 5, 8]    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]=[4, 4, 4, 4]    ,       SCREAMING_SNAKE_CASE__		:    List[Any]="gelu"    ,       SCREAMING_SNAKE_CASE__		:    List[Any]=0.0    ,       SCREAMING_SNAKE_CASE__		:    Any=0.0    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]=0.02    ,       SCREAMING_SNAKE_CASE__		:    Any=0.1    ,       SCREAMING_SNAKE_CASE__		:    List[Any]=1e-6    ,       SCREAMING_SNAKE_CASE__		:    int=6_4    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]=1_0    ,       SCREAMING_SNAKE_CASE__		:    Any=-1    ,       **SCREAMING_SNAKE_CASE__		:    Optional[Any]    ,       ):



					'''simple docstring'''



					super().__init__(**SCREAMING_SNAKE_CASE__    )

					__a						:  List[str]									=     num_channels
					__a						:  Dict									=     num_encoder_blocks
					__a						:  List[str]									=     depths
					__a						:  Optional[int]									=     sr_ratios
					__a						:  int									=     hidden_sizes
					__a						:  str									=     patch_sizes
					__a						:  Union[str, Any]									=     strides
					__a						:  str									=     mlp_ratios
					__a						:  Optional[int]									=     num_attention_heads
					__a						:  List[Any]									=     hidden_act
					__a						:  Any									=     hidden_dropout_prob
					__a						:  List[Any]									=     attention_probs_dropout_prob
					__a						:  str									=     initializer_range
					__a						:  int									=     drop_path_rate
					__a						:  int									=     layer_norm_eps
					__a						:  Optional[Any]									=     decoder_hidden_size
					__a						:  Dict									=     max_depth
					__a						:  Union[str, Any]									=     head_in_index








'''simple docstring'''







from ...configuration_utils import PretrainedConfig


class        A__   (   _snake_case      ):
    lowercase						       =		"bert-generation"



    def __init__(       self		,					UpperCamelCase__=50358		,					UpperCamelCase__=1024		,					UpperCamelCase__=24		,					UpperCamelCase__=16		,					UpperCamelCase__=4096		,					UpperCamelCase__="gelu"		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=0.02		,					UpperCamelCase__=1e-1_2		,					UpperCamelCase__=0		,					UpperCamelCase__=2		,					UpperCamelCase__=1		,					UpperCamelCase__="absolute"		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   Any:





           '''simple docstring'''
           super().__init__(pad_token_id=UpperCamelCase__		,					bos_token_id=UpperCamelCase__		,					eos_token_id=UpperCamelCase__		,					**UpperCamelCase__							)

           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   num_hidden_layers
           A_						       =   num_attention_heads
           A_						       =   hidden_act
           A_						       =   intermediate_size
           A_						       =   hidden_dropout_prob
           A_						       =   attention_probs_dropout_prob
           A_						       =   max_position_embeddings
           A_						       =   initializer_range
           A_						       =   layer_norm_eps
           A_						       =   position_embedding_type
           A_						       =   use_cache
