


'''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


A__	:      Union[str, Any]				=						logging.get_logger(__name__)

A__	:      List[Any]				=						{
    """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      UpperCAmelCase_						(_UpperCAmelCase						):





    """simple docstring"""






    lowerCamelCase						:      Any							  =    'vit-hybrid'







    def __init__(							self    ,  SCREAMING_SNAKE_CASE_=None    ,  SCREAMING_SNAKE_CASE_=7_68    ,  SCREAMING_SNAKE_CASE_=12    ,  SCREAMING_SNAKE_CASE_=12    ,  SCREAMING_SNAKE_CASE_=30_72    ,  SCREAMING_SNAKE_CASE_="gelu"    ,  SCREAMING_SNAKE_CASE_=0.0    ,  SCREAMING_SNAKE_CASE_=0.0    ,  SCREAMING_SNAKE_CASE_=0.0_2    ,  SCREAMING_SNAKE_CASE_=1E-12    ,  SCREAMING_SNAKE_CASE_=2_24    ,  SCREAMING_SNAKE_CASE_=1    ,  SCREAMING_SNAKE_CASE_=3    ,  SCREAMING_SNAKE_CASE_=[1, 10_24, 24, 24]    ,  SCREAMING_SNAKE_CASE_=True    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  Union[str, Any]:
         super().__init__(**SCREAMING_SNAKE_CASE_			)

         if backbone_config is None:
              logger.info('`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'			)
              __lowerCamelCase     :			Optional[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:
                   __lowerCamelCase     :			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.'			)
                   __lowerCamelCase     :			List[str]              =	BitConfig
              __lowerCamelCase     :			Dict              =	backbone_config_class(**SCREAMING_SNAKE_CASE_			)

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







    def    lowercase_       (							self			)       ->  Dict[str, any]:
         __lowerCamelCase     :			List[Any]              =	copy.deepcopy(self.__dict__			)
         __lowerCamelCase     :			Union[str, Any]              =	self.backbone_config.to_dict()
         __lowerCamelCase     :			Tuple              =	self.__class__.model_type
         return output





'''simple docstring'''



from typing import Callable, Optional, Union

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


A__	:      Any				=						logging.get_logger(__name__)

A__	:      str				=						{
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}





class      UpperCAmelCase_						(_UpperCAmelCase						):





    """simple docstring"""






    lowerCamelCase						:      Dict							  =    'prophetnet'
    lowerCamelCase						:      List[str]							  =    ['past_key_values']
    lowerCamelCase						:      List[str]							  =    {
        'num_attention_heads': 'num_encoder_attention_heads',
    }







    def __init__(							self    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = "gelu"    ,  SCREAMING_SNAKE_CASE_ = 3_05_22    ,  SCREAMING_SNAKE_CASE_ = 10_24    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 5_12    ,  SCREAMING_SNAKE_CASE_ = 0.0_2    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 2    ,  SCREAMING_SNAKE_CASE_ = 32    ,  SCREAMING_SNAKE_CASE_ = 1_28    ,  SCREAMING_SNAKE_CASE_ = False    ,  SCREAMING_SNAKE_CASE_ = 0.0    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 1    ,  SCREAMING_SNAKE_CASE_ = 2    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  List[Any]:
         __lowerCamelCase     :			List[Any]              =	vocab_size
         __lowerCamelCase     :			Union[str, Any]              =	hidden_size
         __lowerCamelCase     :			Tuple              =	encoder_ffn_dim
         __lowerCamelCase     :			Any              =	num_encoder_layers
         __lowerCamelCase     :			int              =	num_encoder_attention_heads
         __lowerCamelCase     :			Optional[Any]              =	decoder_ffn_dim
         __lowerCamelCase     :			str              =	num_decoder_layers
         __lowerCamelCase     :			int              =	num_decoder_attention_heads
         __lowerCamelCase     :			Union[str, Any]              =	max_position_embeddings
         __lowerCamelCase     :			Optional[Any]              =	init_std  # Normal(0, this parameter)
         __lowerCamelCase     :			Union[str, Any]              =	activation_function

         # parameters for prophetnet
         __lowerCamelCase     :			int              =	ngram
         __lowerCamelCase     :			Optional[int]              =	num_buckets
         __lowerCamelCase     :			str              =	relative_max_distance
         __lowerCamelCase     :			Optional[Any]              =	disable_ngram_loss
         __lowerCamelCase     :			str              =	eps

         # 3 Types of Dropout
         __lowerCamelCase     :			Optional[Any]              =	attention_dropout
         __lowerCamelCase     :			str              =	activation_dropout
         __lowerCamelCase     :			Optional[Any]              =	dropout

         __lowerCamelCase     :			Optional[Any]              =	use_cache

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE_    ,  bos_token_id=SCREAMING_SNAKE_CASE_    ,  eos_token_id=SCREAMING_SNAKE_CASE_    ,  is_encoder_decoder=SCREAMING_SNAKE_CASE_    ,  add_cross_attention=SCREAMING_SNAKE_CASE_    ,  decoder_start_token_id=SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_    ,  )







    @property
    def    lowercase_       (							self			)       ->  int:
         return self.num_encoder_layers + self.num_decoder_layers







    @num_hidden_layers.setter
    def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Union[str, Any]:
         raise NotImplementedError(
             'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
             ' `num_decoder_layers`.'			)






