



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


UpperCAmelCase__   =       logging.get_logger(__name__)







class 			__lowerCAmelCase    (			snake_case_				,						snake_case_						):
			UpperCamelCase   					=     '''maskformer-swin'''

			UpperCamelCase   					=     {
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}



			def __init__(						self   :  str ,		A   :  Union[str, Any]=2_24 ,		A   :  List[Any]=4 ,		A   :  Union[str, Any]=3 ,		A   :  Optional[Any]=96 ,		A   :  Optional[int]=[2, 2, 6, 2] ,		A   :  str=[3, 6, 12, 24] ,		A   :  Any=7 ,		A   :  Optional[Any]=4.0 ,		A   :  Dict=True ,		A   :  Optional[Any]=0.0 ,		A   :  str=0.0 ,		A   :  List[str]=0.1 ,		A   :  List[str]="gelu" ,		A   :  Dict=False ,		A   :  Dict=0.0_2 ,		A   :  Union[str, Any]=1E-5 ,		A   :  Optional[int]=None ,		A   :  Dict=None ,		**A   :  int ,		)   ->    Any:



						"""simple docstring"""






						super().__init__(**A)

						_UpperCAmelCase		       =   image_size
						_UpperCAmelCase		       =   patch_size
						_UpperCAmelCase		       =   num_channels
						_UpperCAmelCase		       =   embed_dim
						_UpperCAmelCase		       =   depths
						_UpperCAmelCase		       =   len(A)
						_UpperCAmelCase		       =   num_heads
						_UpperCAmelCase		       =   window_size
						_UpperCAmelCase		       =   mlp_ratio
						_UpperCAmelCase		       =   qkv_bias
						_UpperCAmelCase		       =   hidden_dropout_prob
						_UpperCAmelCase		       =   attention_probs_dropout_prob
						_UpperCAmelCase		       =   drop_path_rate
						_UpperCAmelCase		       =   hidden_act
						_UpperCAmelCase		       =   use_absolute_embeddings
						_UpperCAmelCase		       =   layer_norm_eps
						_UpperCAmelCase		       =   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
						_UpperCAmelCase		       =   int(embed_dim * 2 ** (len(A) - 1))
						_UpperCAmelCase		       =   ['stem'] + [F"stage{idx}" for idx in range(1 ,		len(A) + 1)]
						_UpperCAmelCase       ,     _UpperCAmelCase		       =   get_aligned_output_features_output_indices(
						    out_features=A ,		out_indices=A ,		stage_names=self.stage_names)







'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''data2vec-text'''


    def __init__(		self ,		A=3_0522 ,		A=768 ,		A=12 ,		A=12 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=512 ,		A=2 ,		A=0.02 ,		A=1e-12 ,		A=1 ,		A=0 ,		A=2 ,		A="absolute" ,		A=True ,		A=None ,		**A ,		)		->   int:
        super().__init__(pad_token_id=A ,		bos_token_id=A ,		eos_token_id=A ,		**A       )

        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			max_position_embeddings
        _SCREAMING_SNAKE_CASE									=			type_vocab_size
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			position_embedding_type
        _SCREAMING_SNAKE_CASE									=			use_cache
        _SCREAMING_SNAKE_CASE									=			classifier_dropout




class 					a_     (				snake_case_       ):

    '''simple docstring'''


    @property
    def 			snake_case_(		self       )		->   Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            _SCREAMING_SNAKE_CASE									=			{0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _SCREAMING_SNAKE_CASE									=			{0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]       )



