



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       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : List[Any]       =						"vit-hybrid"


   def __init__(    self:     Optional[int]  ,lowerCamelCase_:     str=None  ,lowerCamelCase_:     Union[str, Any]=768  ,lowerCamelCase_:     Dict=12  ,lowerCamelCase_:     List[Any]=12  ,lowerCamelCase_:     str=3072  ,lowerCamelCase_:     List[Any]="gelu"  ,lowerCamelCase_:     Any=0.0  ,lowerCamelCase_:     int=0.0  ,lowerCamelCase_:     int=0.0_2  ,lowerCamelCase_:     Tuple=1e-12  ,lowerCamelCase_:     Optional[int]=224  ,lowerCamelCase_:     Optional[int]=1  ,lowerCamelCase_:     List[Any]=3  ,lowerCamelCase_:     int=[1, 1024, 24, 24]  ,lowerCamelCase_:     Dict=True  ,**lowerCamelCase_:     Tuple  ,)	->      List[Any]:
       super().__init__(**lowerCamelCase_							)

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

       if isinstance(lowerCamelCase_  ,lowerCamelCase_							):
           if "model_type" in backbone_config:
               UpperCAmelCase_     :       Optional[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."""							)
               UpperCAmelCase_     :       List[Any]  		=					BitConfig
           UpperCAmelCase_     :       Any  		=					backbone_config_class(**lowerCamelCase_							)

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






   def       A__    (    self:     Optional[Any]							)	->      Dict[str, any]:
       UpperCAmelCase_     :       Dict  		=					copy.deepcopy(self.__dict__							)
       UpperCAmelCase_     :       Optional[int]  		=					self.backbone_config.to_dict()
       UpperCAmelCase_     :       Any  		=					self.__class__.model_type
       return output





import copy

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


UpperCamelCase_									=   logging.get_logger(__name__)

UpperCamelCase_									=   {}







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : List[str]       =						"fsmt_decoder"


   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     Tuple=0  ,lowerCamelCase_:     List[str]=0							)	->      Any:
       super().__init__()
       UpperCAmelCase_     :       Optional[Any]  		=					vocab_size
       UpperCAmelCase_     :       Union[str, Any]  		=					bos_token_id







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Dict       =						"fsmt"
   A__      : int       =						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


   def __init__(    self:     int  ,lowerCamelCase_:     Optional[Any]=["en", "de"]  ,lowerCamelCase_:     Any=42024  ,lowerCamelCase_:     Any=42024  ,lowerCamelCase_:     List[str]="relu"  ,lowerCamelCase_:     Any=1024  ,lowerCamelCase_:     Optional[int]=200  ,lowerCamelCase_:     str=1024  ,lowerCamelCase_:     Any=4096  ,lowerCamelCase_:     Dict=12  ,lowerCamelCase_:     Tuple=16  ,lowerCamelCase_:     List[Any]=0.0  ,lowerCamelCase_:     str=4096  ,lowerCamelCase_:     Union[str, Any]=12  ,lowerCamelCase_:     Any=16  ,lowerCamelCase_:     List[str]=0.0  ,lowerCamelCase_:     str=0.0  ,lowerCamelCase_:     Optional[int]=0.1  ,lowerCamelCase_:     List[Any]=0.0  ,lowerCamelCase_:     Tuple=0.0_2  ,lowerCamelCase_:     str=2  ,lowerCamelCase_:     List[str]=True  ,lowerCamelCase_:     Dict=True  ,lowerCamelCase_:     Any=False  ,lowerCamelCase_:     Any=5  ,lowerCamelCase_:     Optional[int]=1.0  ,lowerCamelCase_:     List[str]=False  ,lowerCamelCase_:     List[Any]=True  ,lowerCamelCase_:     List[str]=1  ,lowerCamelCase_:     int=0  ,lowerCamelCase_:     Optional[Any]=2  ,lowerCamelCase_:     Any=2  ,**lowerCamelCase_:     Optional[Any]  ,)	->      int:
       UpperCAmelCase_     :       Dict  		=					langs
       UpperCAmelCase_     :       Tuple  		=					src_vocab_size
       UpperCAmelCase_     :       Optional[int]  		=					tgt_vocab_size
       UpperCAmelCase_     :       Optional[Any]  		=					d_model  # encoder_embed_dim and decoder_embed_dim

       UpperCAmelCase_     :       List[str]  		=					encoder_ffn_dim
       UpperCAmelCase_     :       List[Any]  		=					encoder_layers
       UpperCAmelCase_     :       List[Any]  		=					encoder_attention_heads
       UpperCAmelCase_     :       Union[str, Any]  		=					encoder_layerdrop
       UpperCAmelCase_     :       List[Any]  		=					decoder_layerdrop
       UpperCAmelCase_     :       str  		=					decoder_ffn_dim
       UpperCAmelCase_     :       Optional[int]  		=					decoder_layers
       UpperCAmelCase_     :       int  		=					decoder_attention_heads
       UpperCAmelCase_     :       List[Any]  		=					max_position_embeddings
       UpperCAmelCase_     :       Dict  		=					init_std  # Normal(0, this parameter)
       UpperCAmelCase_     :       Dict  		=					activation_function

       UpperCAmelCase_     :       List[str]  		=					DecoderConfig(vocab_size=lowerCamelCase_  ,bos_token_id=lowerCamelCase_							)
       if "decoder" in common_kwargs:
           del common_kwargs["decoder"]

       UpperCAmelCase_     :       Any  		=					scale_embedding  # scale factor will be sqrt(d_model) if True

       # 3 Types of Dropout
       UpperCAmelCase_     :       int  		=					attention_dropout
       UpperCAmelCase_     :       int  		=					activation_dropout
       UpperCAmelCase_     :       Any  		=					dropout

       UpperCAmelCase_     :       Any  		=					use_cache
       super().__init__(
           pad_token_id=lowerCamelCase_  ,bos_token_id=lowerCamelCase_  ,eos_token_id=lowerCamelCase_  ,decoder_start_token_id=lowerCamelCase_  ,is_encoder_decoder=lowerCamelCase_  ,tie_word_embeddings=lowerCamelCase_  ,forced_eos_token_id=lowerCamelCase_  ,max_length=lowerCamelCase_  ,num_beams=lowerCamelCase_  ,length_penalty=lowerCamelCase_  ,early_stopping=lowerCamelCase_  ,**lowerCamelCase_  ,)






   def       A__    (    self:     List[str]							)	->      Union[str, Any]:
       UpperCAmelCase_     :       str  		=					copy.deepcopy(self.__dict__							)
       UpperCAmelCase_     :       int  		=					self.decoder.to_dict()
       UpperCAmelCase_     :       Dict  		=					self.__class__.model_type
       return output
