
'''simple docstring'''





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


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}

class     a (					__magic_name__     ):
				_snake_case		     =			'''cpmant'''




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					int = 3_07_20,     SCREAMING_SNAKE_CASE_						:					int = 40_96,     SCREAMING_SNAKE_CASE_						:					int = 32,     SCREAMING_SNAKE_CASE_						:					int = 1_28,     SCREAMING_SNAKE_CASE_						:					int = 1_02_40,     SCREAMING_SNAKE_CASE_						:					int = 48,     SCREAMING_SNAKE_CASE_						:					int = 0.0,     SCREAMING_SNAKE_CASE_						:					int = 5_12,     SCREAMING_SNAKE_CASE_						:					int = 20_48,     SCREAMING_SNAKE_CASE_						:					int = 1e-6,     SCREAMING_SNAKE_CASE_						:					float = 1.0,     SCREAMING_SNAKE_CASE_						:					int = 32,     SCREAMING_SNAKE_CASE_						:					int = 32,     SCREAMING_SNAKE_CASE_						:					int = 32,     SCREAMING_SNAKE_CASE_						:					bool = True,     **SCREAMING_SNAKE_CASE_						:					Optional[int],     ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[Any]			=     prompt_types
										snake_case							:  List[str]			=     prompt_length
										snake_case							:  Dict			=     segment_types
										snake_case							:  str			=     hidden_size
										snake_case							:  int			=     num_attention_heads
										snake_case							:  List[str]			=     dim_head
										snake_case							:  str			=     dim_ff
										snake_case							:  Union[str, Any]			=     num_hidden_layers
										snake_case							:  Any			=     position_bias_num_buckets
										snake_case							:  Any			=     position_bias_max_distance
										snake_case							:  List[Any]			=     dropout_p
										snake_case							:  Optional[Any]			=     eps
										snake_case							:  int			=     use_cache
										snake_case							:  Any			=     vocab_size
										snake_case							:  List[str]			=     init_std



'''simple docstring'''





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}

class     a (					__magic_name__     ):
				_snake_case		     =			'''vit-hybrid'''




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Tuple=7_68,     SCREAMING_SNAKE_CASE_						:					List[str]=12,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=12,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=30_72,     SCREAMING_SNAKE_CASE_						:					List[Any]="gelu",     SCREAMING_SNAKE_CASE_						:					Any=0.0,     SCREAMING_SNAKE_CASE_						:					Dict=0.0,     SCREAMING_SNAKE_CASE_						:					List[Any]=0.02,     SCREAMING_SNAKE_CASE_						:					List[Any]=1e-12,     SCREAMING_SNAKE_CASE_						:					int=2_24,     SCREAMING_SNAKE_CASE_						:					int=1,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=3,     SCREAMING_SNAKE_CASE_						:					Any=[1, 10_24, 24, 24],     SCREAMING_SNAKE_CASE_						:					List[str]=True,     **SCREAMING_SNAKE_CASE_						:					List[str],     ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )

										if backbone_config is None:
																logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''   )
																snake_case							:  int			=     {
																    '''global_padding''': '''same''',
																    '''layer_type''': '''bottleneck''',
																    '''depths''': [3, 4, 9],
																    '''out_features''': ['''stage3'''],
																    '''embedding_dynamic_padding''': True,
																}

										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																if "model_type" in backbone_config:
																						snake_case							:  Any			=     CONFIG_MAPPING[backbone_config['''model_type''']]
																else:
																						logger.info(
																						    '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''   )
																						snake_case							:  Any			=     BitConfig
																snake_case							:  Optional[Any]			=     backbone_config_class(**SCREAMING_SNAKE_CASE_   )

										snake_case							:  str			=     backbone_featmap_shape
										snake_case							:  Union[str, Any]			=     backbone_config
										snake_case							:  List[str]			=     hidden_size
										snake_case							:  List[Any]			=     num_hidden_layers
										snake_case							:  Dict			=     num_attention_heads
										snake_case							:  List[str]			=     intermediate_size
										snake_case							:  Optional[int]			=     hidden_act
										snake_case							:  List[str]			=     hidden_dropout_prob
										snake_case							:  Tuple			=     attention_probs_dropout_prob
										snake_case							:  Optional[int]			=     initializer_range
										snake_case							:  Optional[int]			=     layer_norm_eps
										snake_case							:  List[Any]			=     image_size
										snake_case							:  Any			=     patch_size
										snake_case							:  str			=     num_channels
										snake_case							:  List[str]			=     qkv_bias




				def 						__snake_case				(				self						:					Any   ):
										snake_case							:  Tuple			=     copy.deepcopy(self.__dict__   )
										snake_case							:  List[Any]			=     self.backbone_config.to_dict()
										snake_case							:  Optional[Any]			=     self.__class__.model_type
										return output

