





import copy

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


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'deta'
 __UpperCamelCase       =      {
     'hidden_size': 'd_model',
     'num_attention_heads': 'encoder_attention_heads',
 }

 def __init__(  self,		A_=None,		A_=900,		A_=2048,		A_=6,		A_=2048,		A_=8,		A_=6,		A_=1024,		A_=8,		A_=0.0,		A_=True,		A_="relu",		A_=256,		A_=0.1,		A_=0.0,		A_=0.0,		A_=0.02,		A_=1.0,		A_=True,		A_=False,		A_="sine",		A_=5,		A_=4,		A_=4,		A_=True,		A_=300,		A_=True,		A_=True,		A_=1,		A_=5,		A_=2,		A_=1,		A_=1,		A_=5,		A_=2,		A_=0.1,		A_=0.25,		**A_,		)	->       List[Any]:
    if backbone_config is None:
       logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."		)
       UpperCAmelCase__						=CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]		)
    else:
       if isinstance(A_,		A_		):
          UpperCAmelCase__						=backbone_config.pop("model_type"		)
          UpperCAmelCase__						=CONFIG_MAPPING[backbone_model_type]
          UpperCAmelCase__						=config_class.from_dict(A_		)

    UpperCAmelCase__						=backbone_config
    UpperCAmelCase__						=num_queries
    UpperCAmelCase__						=max_position_embeddings
    UpperCAmelCase__						=d_model
    UpperCAmelCase__						=encoder_ffn_dim
    UpperCAmelCase__						=encoder_layers
    UpperCAmelCase__						=encoder_attention_heads
    UpperCAmelCase__						=decoder_ffn_dim
    UpperCAmelCase__						=decoder_layers
    UpperCAmelCase__						=decoder_attention_heads
    UpperCAmelCase__						=dropout
    UpperCAmelCase__						=attention_dropout
    UpperCAmelCase__						=activation_dropout
    UpperCAmelCase__						=activation_function
    UpperCAmelCase__						=init_std
    UpperCAmelCase__						=init_xavier_std
    UpperCAmelCase__						=encoder_layerdrop
    UpperCAmelCase__						=auxiliary_loss
    UpperCAmelCase__						=position_embedding_type
    # deformable attributes
    UpperCAmelCase__						=num_feature_levels
    UpperCAmelCase__						=encoder_n_points
    UpperCAmelCase__						=decoder_n_points
    UpperCAmelCase__						=two_stage
    UpperCAmelCase__						=two_stage_num_proposals
    UpperCAmelCase__						=with_box_refine
    UpperCAmelCase__						=assign_first_stage
    if two_stage is True and with_box_refine is False:
       raise ValueError("If two_stage is True, with_box_refine must be True."		)
    # Hungarian matcher
    UpperCAmelCase__						=class_cost
    UpperCAmelCase__						=bbox_cost
    UpperCAmelCase__						=giou_cost
    # Loss coefficients
    UpperCAmelCase__						=mask_loss_coefficient
    UpperCAmelCase__						=dice_loss_coefficient
    UpperCAmelCase__						=bbox_loss_coefficient
    UpperCAmelCase__						=giou_loss_coefficient
    UpperCAmelCase__						=eos_coefficient
    UpperCAmelCase__						=focal_alpha
    super().__init__(is_encoder_decoder=A_,		**A_		)

 @property
 def  __UpperCAmelCase						(  self		)	->       int:
    return self.encoder_attention_heads

 @property
 def  __UpperCAmelCase						(  self		)	->       int:
    return self.d_model






 def  __UpperCAmelCase						(  self		)	->       Dict:
    UpperCAmelCase__						=copy.deepcopy(self.__dict__		)
    UpperCAmelCase__						=self.backbone_config.to_dict()
    UpperCAmelCase__						=self.__class__.model_type
    return output










from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'roformer'

 def __init__(  self,		A_=5_0000,		A_=None,		A_=768,		A_=12,		A_=12,		A_=3072,		A_="gelu",		A_=0.1,		A_=0.1,		A_=1536,		A_=2,		A_=0.02,		A_=1E-12,		A_=0,		A_=False,		A_=True,		**A_,		)	->       Dict:
    super().__init__(pad_token_id=A_,		**A_		)

    UpperCAmelCase__						=vocab_size
    UpperCAmelCase__						=hidden_size if embedding_size is None else embedding_size
    UpperCAmelCase__						=hidden_size
    UpperCAmelCase__						=num_hidden_layers
    UpperCAmelCase__						=num_attention_heads
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=intermediate_size
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=max_position_embeddings
    UpperCAmelCase__						=type_vocab_size
    UpperCAmelCase__						=initializer_range
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=rotary_value
    UpperCAmelCase__						=use_cache

class     snake_case_		(   a							):






 '''simple docstring'''





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



