from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowercase_  : str								=					logging.get_logger(__name__)

lowercase_  : Any								=					{
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   "table-transformer"
    __a						=   ["past_key_values"]
    __a						=   {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }
    def __init__(	self			,			lowerCAmelCase=True			,			lowerCAmelCase=None			,			lowerCAmelCase=3			,			lowerCAmelCase=100			,			lowerCAmelCase=6			,			lowerCAmelCase=2048			,			lowerCAmelCase=8			,			lowerCAmelCase=6			,			lowerCAmelCase=2048			,			lowerCAmelCase=8			,			lowerCAmelCase=0.0			,			lowerCAmelCase=0.0			,			lowerCAmelCase=True			,			lowerCAmelCase="relu"			,			lowerCAmelCase=256			,			lowerCAmelCase=0.1			,			lowerCAmelCase=0.0			,			lowerCAmelCase=0.0			,			lowerCAmelCase=0.02			,			lowerCAmelCase=1.0			,			lowerCAmelCase=False			,			lowerCAmelCase="sine"			,			lowerCAmelCase="resnet50"			,			lowerCAmelCase=True			,			lowerCAmelCase=False			,			lowerCAmelCase=1			,			lowerCAmelCase=5			,			lowerCAmelCase=2			,			lowerCAmelCase=1			,			lowerCAmelCase=1			,			lowerCAmelCase=5			,			lowerCAmelCase=2			,			lowerCAmelCase=0.1			,			**lowerCAmelCase			,			)							->       Union[str, Any]:
     if backbone_config is not None and use_timm_backbone:
      raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )

     if not use_timm_backbone:
      if backbone_config is None:
       logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
       SCREAMING_SNAKE_CASE__: Optional[Any]=   CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
      elif isinstance(lowerCAmelCase			,			lowerCAmelCase ):
       SCREAMING_SNAKE_CASE__: Optional[int]=   backbone_config.get('''model_type''' )
       SCREAMING_SNAKE_CASE__: int=   CONFIG_MAPPING[backbone_model_type]
       SCREAMING_SNAKE_CASE__: Union[str, Any]=   config_class.from_dict(lowerCAmelCase )
      # set timm attributes to None
      SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Union[str, Any]=   None, None, None

     SCREAMING_SNAKE_CASE__: Optional[Any]=   use_timm_backbone
     SCREAMING_SNAKE_CASE__: Optional[Any]=   backbone_config
     SCREAMING_SNAKE_CASE__: Dict=   num_channels
     SCREAMING_SNAKE_CASE__: Optional[Any]=   num_queries
     SCREAMING_SNAKE_CASE__: Tuple=   d_model
     SCREAMING_SNAKE_CASE__: Dict=   encoder_ffn_dim
     SCREAMING_SNAKE_CASE__: Optional[int]=   encoder_layers
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   encoder_attention_heads
     SCREAMING_SNAKE_CASE__: Optional[Any]=   decoder_ffn_dim
     SCREAMING_SNAKE_CASE__: Optional[int]=   decoder_layers
     SCREAMING_SNAKE_CASE__: int=   decoder_attention_heads
     SCREAMING_SNAKE_CASE__: Optional[int]=   dropout
     SCREAMING_SNAKE_CASE__: Any=   attention_dropout
     SCREAMING_SNAKE_CASE__: Tuple=   activation_dropout
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   activation_function
     SCREAMING_SNAKE_CASE__: Optional[Any]=   init_std
     SCREAMING_SNAKE_CASE__: Tuple=   init_xavier_std
     SCREAMING_SNAKE_CASE__: List[str]=   encoder_layerdrop
     SCREAMING_SNAKE_CASE__: Dict=   decoder_layerdrop
     SCREAMING_SNAKE_CASE__: int=   encoder_layers
     SCREAMING_SNAKE_CASE__: Optional[Any]=   auxiliary_loss
     SCREAMING_SNAKE_CASE__: Dict=   position_embedding_type
     SCREAMING_SNAKE_CASE__: List[str]=   backbone
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   use_pretrained_backbone
     SCREAMING_SNAKE_CASE__: Any=   dilation
     # Hungarian matcher
     SCREAMING_SNAKE_CASE__: Any=   class_cost
     SCREAMING_SNAKE_CASE__: Any=   bbox_cost
     SCREAMING_SNAKE_CASE__: Optional[Any]=   giou_cost
     # Loss coefficients
     SCREAMING_SNAKE_CASE__: Optional[Any]=   mask_loss_coefficient
     SCREAMING_SNAKE_CASE__: str=   dice_loss_coefficient
     SCREAMING_SNAKE_CASE__: Optional[int]=   bbox_loss_coefficient
     SCREAMING_SNAKE_CASE__: Tuple=   giou_loss_coefficient
     SCREAMING_SNAKE_CASE__: Optional[Any]=   eos_coefficient
     super().__init__(is_encoder_decoder=lowerCAmelCase			,			**lowerCAmelCase )
    @property
    def     UpperCamelCase_					(	self )							->       int:
     return self.encoder_attention_heads



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






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   version.parse("1.11"     )
    @property
    def     UpperCamelCase_					(	self )							->       Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ('''pixel_mask''', {0: '''batch'''}),
         ] )
    @property
    def     UpperCamelCase_					(	self )							->       float:
     return 1e-5



    @property
    def     UpperCamelCase_					(	self )							->       int:
     return 12






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a_    :  Tuple						  =		{'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_    :  Union[str, Any]						  =		[
         'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
         'UniSpeechForCTC',
         'UniSpeechForPreTraining',
         'UniSpeechForSequenceClassification',
         'UniSpeechModel',
         'UniSpeechPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_unispeech import (
              UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
              UniSpeechForCTC,
              UniSpeechForPreTraining,
              UniSpeechForSequenceClassification,
              UniSpeechModel,
              UniSpeechPreTrainedModel,
          )

else:
     import sys

     a_    :  Optional[Any]						  =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)