"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a				:		Tuple	   =       logging.get_logger(__name__)

_a				:		Any	   =       {
    'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/config.json',
    'distilbert-base-uncased-distilled-squad': (
        'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/config.json',
    'distilbert-base-cased-distilled-squad': (
        'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json',
    'distilbert-base-multilingual-cased': (
        'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'
    ),
    'distilbert-base-uncased-finetuned-sst-2-english': (
        'https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'
    ),
}
class 						__A							(			__lowerCAmelCase		):
     _UpperCamelCase							:      int		 =		'''distilbert'''
     _UpperCamelCase							:      Optional[Any]		 =		{
         '''hidden_size''': '''dim''',
         '''num_attention_heads''': '''n_heads''',
         '''num_hidden_layers''': '''n_layers''',
     }






     def __init__(		self    ,					a__=30522    ,					a__=512    ,					a__=False    ,					a__=6    ,					a__=12    ,					a__=768    ,					a__=4 * 768    ,					a__=0.1    ,					a__=0.1    ,					a__="gelu"    ,					a__=0.0_2    ,					a__=0.1    ,					a__=0.2    ,					a__=0    ,					**a__    ,					):
       _lowerCAmelCase						:	Union[str, Any]        =					vocab_size
       _lowerCAmelCase						:	int        =					max_position_embeddings
       _lowerCAmelCase						:	List[str]        =					sinusoidal_pos_embds
       _lowerCAmelCase						:	Optional[Any]        =					n_layers
       _lowerCAmelCase						:	List[str]        =					n_heads
       _lowerCAmelCase						:	List[str]        =					dim
       _lowerCAmelCase						:	List[str]        =					hidden_dim
       _lowerCAmelCase						:	Union[str, Any]        =					dropout
       _lowerCAmelCase						:	Optional[Any]        =					attention_dropout
       _lowerCAmelCase						:	int        =					activation
       _lowerCAmelCase						:	str        =					initializer_range
       _lowerCAmelCase						:	List[Any]        =					qa_dropout
       _lowerCAmelCase						:	List[str]        =					seq_classif_dropout
       super().__init__(**lowerCAmelCase_    ,					pad_token_id=lowerCAmelCase_  )







class 						__A							(			__lowerCAmelCase		):






     @property
     def    __A  (		self  ):
       if self.task == "multiple-choice":
         _lowerCAmelCase						:	Optional[Any]        =					{0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
         _lowerCAmelCase						:	List[Any]        =					{0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]  )





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_a				:		List[Any]	   =       logging.get_logger(__name__)
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Tuple		 =		"maskformer-swin"

     _UpperCamelCase							:      Union[str, Any]		 =		{
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }






     def __init__(		self    ,					a__=224    ,					a__=4    ,					a__=3    ,					a__=96    ,					a__=[2, 2, 6, 2]    ,					a__=[3, 6, 12, 24]    ,					a__=7    ,					a__=4.0    ,					a__=True    ,					a__=0.0    ,					a__=0.0    ,					a__=0.1    ,					a__="gelu"    ,					a__=False    ,					a__=0.0_2    ,					a__=1e-5    ,					a__=None    ,					a__=None    ,					**a__    ,					):
       super().__init__(**a__  )

       _lowerCAmelCase						:	Dict        =					image_size
       _lowerCAmelCase						:	List[str]        =					patch_size
       _lowerCAmelCase						:	Any        =					num_channels
       _lowerCAmelCase						:	int        =					embed_dim
       _lowerCAmelCase						:	Optional[Any]        =					depths
       _lowerCAmelCase						:	List[str]        =					len(a__  )
       _lowerCAmelCase						:	List[Any]        =					num_heads
       _lowerCAmelCase						:	Tuple        =					window_size
       _lowerCAmelCase						:	List[Any]        =					mlp_ratio
       _lowerCAmelCase						:	Optional[Any]        =					qkv_bias
       _lowerCAmelCase						:	int        =					hidden_dropout_prob
       _lowerCAmelCase						:	Union[str, Any]        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Any        =					drop_path_rate
       _lowerCAmelCase						:	Optional[Any]        =					hidden_act
       _lowerCAmelCase						:	Tuple        =					use_absolute_embeddings
       _lowerCAmelCase						:	str        =					layer_norm_eps
       _lowerCAmelCase						:	Any        =					initializer_range
       # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       _lowerCAmelCase						:	Union[str, Any]        =					int(embed_dim * 2 ** (len(a__  ) - 1)  )
       _lowerCAmelCase						:	int        =					["""stem"""] + [F"stage{idx}" for idx in range(1    ,					len(a__  ) + 1  )]
       _lowerCAmelCase      ,     _lowerCAmelCase						:	int        =					get_aligned_output_features_output_indices(
           out_features=a__    ,					out_indices=a__    ,					stage_names=self.stage_names  )




