




"""simple docstring"""


import warnings

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


A_      =    logging.get_logger(__name__)

A_      =    {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}






class  lowercase(					lowercase_			):







 '''simple docstring'''





 lowercase__			 = "mvp"
 lowercase__			 = ["past_key_values"]
 lowercase__			 = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


 def __init__(    self:		List[str],				a_:		List[str]=50_267,				a_:		List[Any]=1_024,				a_:		List[str]=12,				a_:		Tuple=4_096,				a_:		int=16,				a_:		Dict=12,				a_:		Optional[Any]=4_096,				a_:		Optional[int]=16,				a_:		Any=0.0,				a_:		int=0.0,				a_:		Optional[Any]="gelu",				a_:		Optional[int]=1_024,				a_:		Any=0.1,				a_:		List[str]=0.0,				a_:		Tuple=0.0,				a_:		str=0.02,				a_:		Optional[int]=0.0,				a_:		str=False,				a_:		Optional[int]=True,				a_:		List[str]=1,				a_:		Optional[int]=0,				a_:		str=2,				a_:		Any=True,				a_:		Any=2,				a_:		Tuple=2,				a_:		List[Any]=False,				a_:		List[Any]=100,				a_:		Optional[Any]=800,				**a_:		str,				):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  vocab_size
   _snake_case							:		Union[str, Any]								=  max_position_embeddings
   _snake_case							:		Dict								=  d_model
   _snake_case							:		Optional[int]								=  encoder_ffn_dim
   _snake_case							:		List[Any]								=  encoder_layers
   _snake_case							:		Any								=  encoder_attention_heads
   _snake_case							:		Tuple								=  decoder_ffn_dim
   _snake_case							:		List[Any]								=  decoder_layers
   _snake_case							:		List[Any]								=  decoder_attention_heads
   _snake_case							:		Optional[Any]								=  dropout
   _snake_case							:		List[str]								=  attention_dropout
   _snake_case							:		Any								=  activation_dropout
   _snake_case							:		Union[str, Any]								=  activation_function
   _snake_case							:		List[Any]								=  init_std
   _snake_case							:		Any								=  encoder_layerdrop
   _snake_case							:		Optional[int]								=  decoder_layerdrop
   _snake_case							:		str								=  classifier_dropout
   _snake_case							:		Dict								=  use_cache
   _snake_case							:		Optional[Any]								=  encoder_layers
   _snake_case							:		Tuple								=  scale_embedding  # scale factor will be sqrt(d_model) if True
   _snake_case							:		Dict								=  use_prompt
   _snake_case							:		str								=  prompt_length
   _snake_case							:		Tuple								=  prompt_mid_dim

   super().__init__(
       pad_token_id=a__,				bos_token_id=a__,				eos_token_id=a__,				is_encoder_decoder=a__,				decoder_start_token_id=a__,				forced_eos_token_id=a__,				**a__,				)

   if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""",				a__		):
     _snake_case							:		int								=  self.bos_token_id
     warnings.warn(
         f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
         """The config can simply be saved and uploaded again to be fixed."""		)







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_      =    {
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    ['''MaskFormerFeatureExtractor''']
     A_      =    ['''MaskFormerImageProcessor''']


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    [
         '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MaskFormerForInstanceSegmentation''',
         '''MaskFormerModel''',
         '''MaskFormerPreTrainedModel''',
     ]
     A_      =    [
         '''MaskFormerSwinBackbone''',
         '''MaskFormerSwinModel''',
         '''MaskFormerSwinPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
     from .configuration_maskformer_swin import MaskFormerSwinConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_maskformer import MaskFormerFeatureExtractor
          from .image_processing_maskformer import MaskFormerImageProcessor
     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_maskformer import (
              MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              MaskFormerForInstanceSegmentation,
              MaskFormerModel,
              MaskFormerPreTrainedModel,
          )
          from .modeling_maskformer_swin import (
              MaskFormerSwinBackbone,
              MaskFormerSwinModel,
              MaskFormerSwinPreTrainedModel,
          )


else:
     import sys

     A_      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)


