






'''simple docstring'''


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE_				      =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				      =							{
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   _A						=   'oneformer'
   _A						=   {'hidden_size': 'hidden_dim'}



   def __init__( self   ,   _A = None   ,   _A = 255   ,   _A = 150   ,   _A = 0.1   ,   _A = 2.0   ,   _A = 5.0   ,   _A = 5.0   ,   _A = 0.5   ,   _A = 0.07   ,   _A = 12544   ,   _A = 3.0   ,   _A = 0.75   ,   _A = 0.02   ,   _A = 1.0   ,   _A = 1E-0_5   ,   _A = False   ,   _A = True   ,   _A = True   ,   _A = [4, 8, 16, 32]   ,   _A = 77   ,   _A = 256   ,   _A = 77   ,   _A = 6   ,   _A = 49408   ,   _A = 2   ,   _A = 16   ,   _A = 256   ,   _A = 256   ,   _A = 256   ,   _A = 1024   ,   _A = "GN"   ,   _A = 6   ,   _A = 10   ,   _A = True   ,   _A = 8   ,   _A = 0.1   ,   _A = 2048   ,   _A = False   ,   _A = False   ,   _A = 2   ,   _A = 4   ,   **_A   ,   )    ->   Tuple:
    if backbone_config is None:
     logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'			)
     __a    :	Any									=						CONFIG_MAPPING['swin'](
         image_size=224   ,   in_channels=3   ,   patch_size=4   ,   embed_dim=96   ,   depths=[2, 2, 6, 2]   ,   num_heads=[3, 6, 12, 24]   ,   window_size=7   ,   drop_path_rate=0.3   ,   use_absolute_embeddings=A_   ,   out_features=['stage1', 'stage2', 'stage3', 'stage4']   ,   )
    elif isinstance(A_   ,   A_			):
     __a    :	Tuple									=						backbone_config.get('model_type'			)
     __a    :	Optional[Any]									=						CONFIG_MAPPING[backbone_model_type]
     __a    :	List[str]									=						config_class.from_dict(A_			)

    __a    :	List[Any]									=						backbone_config

    __a    :	List[str]									=						ignore_value
    __a    :	Union[str, Any]									=						num_queries
    __a    :	Any									=						no_object_weight
    __a    :	Optional[int]									=						class_weight
    __a    :	Dict									=						mask_weight
    __a    :	Tuple									=						dice_weight
    __a    :	Union[str, Any]									=						contrastive_weight
    __a    :	Union[str, Any]									=						contrastive_temperature
    __a    :	int									=						train_num_points
    __a    :	List[str]									=						oversample_ratio
    __a    :	Union[str, Any]									=						importance_sample_ratio
    __a    :	Any									=						init_std
    __a    :	List[Any]									=						init_xavier_std
    __a    :	Tuple									=						layer_norm_eps
    __a    :	Tuple									=						is_training
    __a    :	Optional[Any]									=						use_auxiliary_loss
    __a    :	str									=						output_auxiliary_logits
    __a    :	Tuple									=						strides
    __a    :	str									=						task_seq_len
    __a    :	Dict									=						text_encoder_width
    __a    :	List[str]									=						text_encoder_context_length
    __a    :	Optional[Any]									=						text_encoder_num_layers
    __a    :	List[str]									=						text_encoder_vocab_size
    __a    :	Optional[int]									=						text_encoder_proj_layers
    __a    :	Optional[int]									=						text_encoder_n_ctx
    __a    :	List[Any]									=						conv_dim
    __a    :	Optional[int]									=						mask_dim
    __a    :	str									=						hidden_dim
    __a    :	Dict									=						encoder_feedforward_dim
    __a    :	Optional[Any]									=						norm
    __a    :	Any									=						encoder_layers
    __a    :	Optional[int]									=						decoder_layers
    __a    :	Optional[int]									=						use_task_norm
    __a    :	Optional[int]									=						num_attention_heads
    __a    :	Union[str, Any]									=						dropout
    __a    :	Union[str, Any]									=						dim_feedforward
    __a    :	Optional[int]									=						pre_norm
    __a    :	List[Any]									=						enforce_input_proj
    __a    :	str									=						query_dec_layers
    __a    :	Tuple									=						common_stride
    __a    :	List[Any]									=						decoder_layers

    super().__init__(**A_			)



   def    __magic_name__    ( self			)    ->   str:
    __a    :	Optional[Any]									=						copy.deepcopy(self.__dict__			)
    __a    :	str									=						self.backbone_config.to_dict()
    __a    :	Dict									=						self.__class__.model_type
    return output












'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_				      =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				      =							{
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   _A						=   SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
   _A						=   'squeezebert'



   def __init__( self   ,   _A=30522   ,   _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-1_2   ,   _A=0   ,   _A=768   ,   _A=4   ,   _A=4   ,   _A=4   ,   _A=1   ,   _A=4   ,   _A=4   ,   **_A   ,   )    ->   List[Any]:
    super().__init__(pad_token_id=_A   ,   **_A			)

    __a    :	Optional[int]									=						vocab_size
    __a    :	Optional[int]									=						hidden_size
    __a    :	int									=						num_hidden_layers
    __a    :	int									=						num_attention_heads
    __a    :	Tuple									=						hidden_act
    __a    :	Optional[Any]									=						intermediate_size
    __a    :	Any									=						hidden_dropout_prob
    __a    :	List[Any]									=						attention_probs_dropout_prob
    __a    :	List[str]									=						max_position_embeddings
    __a    :	Tuple									=						type_vocab_size
    __a    :	Tuple									=						initializer_range
    __a    :	List[str]									=						layer_norm_eps
    __a    :	str									=						embedding_size
    __a    :	Any									=						q_groups
    __a    :	Any									=						k_groups
    __a    :	Union[str, Any]									=						v_groups
    __a    :	List[str]									=						post_attention_groups
    __a    :	Dict									=						intermediate_groups
    __a    :	Optional[Any]									=						output_groups




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   @property
   def    __magic_name__    ( self			)    ->   Mapping[str, Mapping[int, str]]:
    if self.task == "multiple-choice":
     __a    :	Tuple									=						{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
     __a    :	List[Any]									=						{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
            ('token_type_ids', dynamic_axis),
        ]			)




