


"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A				:   Union[str, Any]		= {
    "configuration_decision_transformer": [
        "DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DecisionTransformerConfig",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A				:   str		= [
        "DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DecisionTransformerGPT2Model",
        "DecisionTransformerGPT2PreTrainedModel",
        "DecisionTransformerModel",
        "DecisionTransformerPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_decision_transformer import (
        DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        DecisionTransformerConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_decision_transformer import (
            DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            DecisionTransformerGPTaModel,
            DecisionTransformerGPTaPreTrainedModel,
            DecisionTransformerModel,
            DecisionTransformerPreTrainedModel,
        )


else:
    import sys

    A				:   Dict		= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


A				:   int		= logging.get_logger(__name__)

A				:   Any		= {
    "layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json",
    "layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json",
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
    import detectrona







class       _lowercase			(							lowercase__):


       """simple docstring"""




       A__ 						=      "layoutlmv2"





       def __init__(       self							:      Union[str, Any] ,				__lowerCamelCase							:      int=30522 ,				__lowerCamelCase							:      List[Any]=768 ,				__lowerCamelCase							:      Any=12 ,				__lowerCamelCase							:      List[Any]=12 ,				__lowerCamelCase							:      Optional[Any]=3072 ,				__lowerCamelCase							:      List[Any]="gelu" ,				__lowerCamelCase							:      Any=0.1 ,				__lowerCamelCase							:      Union[str, Any]=0.1 ,				__lowerCamelCase							:      int=512 ,				__lowerCamelCase							:      str=2 ,				__lowerCamelCase							:      Any=0.0_2 ,				__lowerCamelCase							:      List[Any]=1E-1_2 ,				__lowerCamelCase							:      List[str]=0 ,				__lowerCamelCase							:      Union[str, Any]=1024 ,				__lowerCamelCase							:      Tuple=128 ,				__lowerCamelCase							:      Tuple=32 ,				__lowerCamelCase							:      Union[str, Any]=True ,				__lowerCamelCase							:      List[Any]=256 ,				__lowerCamelCase							:      Dict=64 ,				__lowerCamelCase							:      List[Any]=True ,				__lowerCamelCase							:      Union[str, Any]=[7, 7, 256] ,				__lowerCamelCase							:      str=128 ,				__lowerCamelCase							:      Dict=128 ,				__lowerCamelCase							:      Dict=True ,				__lowerCamelCase							:      int=True ,				__lowerCamelCase							:      str=False ,				__lowerCamelCase							:      Tuple=None ,				**__lowerCamelCase							:      str ,				):


             '''simple docstring'''



             super().__init__(
                 vocab_size=__lowerCamelCase ,				hidden_size=__lowerCamelCase ,				num_hidden_layers=__lowerCamelCase ,				num_attention_heads=__lowerCamelCase ,				intermediate_size=__lowerCamelCase ,				hidden_act=__lowerCamelCase ,				hidden_dropout_prob=__lowerCamelCase ,				attention_probs_dropout_prob=__lowerCamelCase ,				max_position_embeddings=__lowerCamelCase ,				type_vocab_size=__lowerCamelCase ,				initializer_range=__lowerCamelCase ,				layer_norm_eps=__lowerCamelCase ,				pad_token_id=__lowerCamelCase ,				**__lowerCamelCase ,				)
             lowerCamelCase__		:    Dict   =  max_ad_position_embeddings
             lowerCamelCase__		:    int   =  max_rel_pos
             lowerCamelCase__		:    List[Any]   =  rel_pos_bins
             lowerCamelCase__		:    str   =  fast_qkv
             lowerCamelCase__		:    Optional[Any]   =  max_rel_ad_pos
             lowerCamelCase__		:    List[Any]   =  rel_ad_pos_bins
             lowerCamelCase__		:    Optional[Any]   =  convert_sync_batchnorm
             lowerCamelCase__		:    Any   =  image_feature_pool_shape
             lowerCamelCase__		:    List[Any]   =  coordinate_size
             lowerCamelCase__		:    Any   =  shape_size
             lowerCamelCase__		:    List[str]   =  has_relative_attention_bias
             lowerCamelCase__		:    Optional[int]   =  has_spatial_attention_bias
             lowerCamelCase__		:    Optional[Any]   =  has_visual_segment_embedding
             lowerCamelCase__		:    List[str]   =  (
                 detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
             )





       @classmethod
       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             return {
                 "MODEL.MASK_ON": True,
                 "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
                 "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
                 "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
                 "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
                 "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
                 "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
                 "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
                 "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
                 "MODEL.POST_NMS_TOPK_TEST": 1000,
                 "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
                 "MODEL.ROI_HEADS.NUM_CLASSES": 5,
                 "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
                 "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
                 "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
                 "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
                 "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
                 "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
                 "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
                 "MODEL.RESNETS.DEPTH": 101,
                 "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
                 "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
                 "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
                 "MODEL.RESNETS.NUM_GROUPS": 32,
                 "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
                 "MODEL.RESNETS.STRIDE_IN_1X1": False,
             }





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  detectrona.config.get_cfg()
             for k, v in self.detectrona_config_args.items():
                   lowerCamelCase__		:    List[Any]   =  k.split("."	)
                   lowerCamelCase__		:    int   =  detectrona_config
                   for attribute in attributes[:-1]:
                         lowerCamelCase__		:    Any   =  getattr(__lowerCamelCase ,				__lowerCamelCase	)
                   setattr(__lowerCamelCase ,				attributes[-1] ,				__lowerCamelCase	)

             return detectrona_config


