


from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE			: Optional[Any]				   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			: str				   =					{
    "studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
    "studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json",
}






class _lowerCamelCase(		_a  ):
       lowercase_					:      int       					=     """luke"""
       def __init__(   self, lowerCamelCase=5_02_67, lowerCamelCase=50_00_00, lowerCamelCase=7_68, lowerCamelCase=2_56, lowerCamelCase=12, lowerCamelCase=12, lowerCamelCase=30_72, lowerCamelCase="gelu", lowerCamelCase=0.1, lowerCamelCase=0.1, lowerCamelCase=5_12, lowerCamelCase=2, lowerCamelCase=0.0_2, lowerCamelCase=1E-12, lowerCamelCase=True, lowerCamelCase=None, lowerCamelCase=1, lowerCamelCase=0, lowerCamelCase=2, **lowerCamelCase, )			->  List[str]:




           """simple docstring"""

           super().__init__(pad_token_id=lowerCamelCase, bos_token_id=lowerCamelCase, eos_token_id=lowerCamelCase, **lowerCamelCase)

           _lowercase			:				List[Any] 						=						vocab_size
           _lowercase			:				Union[str, Any] 						=						entity_vocab_size
           _lowercase			:				List[str] 						=						hidden_size
           _lowercase			:				Optional[Any] 						=						entity_emb_size
           _lowercase			:				int 						=						num_hidden_layers
           _lowercase			:				Tuple 						=						num_attention_heads
           _lowercase			:				Dict 						=						hidden_act
           _lowercase			:				Optional[Any] 						=						intermediate_size
           _lowercase			:				List[str] 						=						hidden_dropout_prob
           _lowercase			:				Tuple 						=						attention_probs_dropout_prob
           _lowercase			:				Dict 						=						max_position_embeddings
           _lowercase			:				int 						=						type_vocab_size
           _lowercase			:				Union[str, Any] 						=						initializer_range
           _lowercase			:				Optional[int] 						=						layer_norm_eps
           _lowercase			:				Optional[int] 						=						use_entity_aware_attention
           _lowercase			:				Any 						=						classifier_dropout






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE			: Optional[Any]				   =					{
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					["ConditionalDetrFeatureExtractor"]
 SCREAMING_SNAKE_CASE			: Optional[Any]				   =					["ConditionalDetrImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: Any				   =					[
     "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ConditionalDetrForObjectDetection",
     "ConditionalDetrForSegmentation",
     "ConditionalDetrModel",
     "ConditionalDetrPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_conditional_detr import (
     CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConditionalDetrConfig,
     ConditionalDetrOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
  from .image_processing_conditional_detr import ConditionalDetrImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConditionalDetrForObjectDetection,
      ConditionalDetrForSegmentation,
      ConditionalDetrModel,
      ConditionalDetrPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE			: str				   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


