


'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

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


a_       :     int 							=     logging.get_logger(__name__)

a_       :     List[Any] 							=     {
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}




class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						'''rembert'''
 def __init__(			self   ,					__magic_name__=25_03_00   ,					__magic_name__=11_52   ,					__magic_name__=32   ,					__magic_name__=18   ,					__magic_name__=2_56   ,					__magic_name__=16_64   ,					__magic_name__=46_08   ,					__magic_name__="gelu"   ,					__magic_name__=0.0   ,					__magic_name__=0.0   ,					__magic_name__=0.1   ,					__magic_name__=5_12   ,					__magic_name__=2   ,					__magic_name__=0.0_2   ,					__magic_name__=1e-12   ,					__magic_name__=True   ,					__magic_name__=0   ,					__magic_name__=3_12   ,					__magic_name__=3_13   ,					**__magic_name__   ,					)       ->				int:
      super().__init__(pad_token_id=__magic_name__   ,					bos_token_id=__magic_name__   ,					eos_token_id=__magic_name__   ,					**__magic_name__    )

      _a         =				vocab_size
      _a         =				input_embedding_size
      _a         =				output_embedding_size
      _a         =				max_position_embeddings
      _a         =				hidden_size
      _a         =				num_hidden_layers
      _a         =				num_attention_heads
      _a         =				intermediate_size
      _a         =				hidden_act
      _a         =				hidden_dropout_prob
      _a         =				attention_probs_dropout_prob
      _a         =				classifier_dropout_prob
      _a         =				initializer_range
      _a         =				type_vocab_size
      _a         =				layer_norm_eps
      _a         =				use_cache
      _a         =				False





class 			a  (  _SCREAMING_SNAKE_CASE							):
 @property
 def 				__UpperCAmelCase		(			self    )       ->				str:







      if self.task == "multiple-choice":
           _a         =				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
           _a         =				{0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('input_ids', dynamic_axis),
              ('attention_mask', dynamic_axis),
              ('token_type_ids', dynamic_axis),
          ]    )




 @property
 def 				__UpperCAmelCase		(			self    )       ->				str:
      return 1e-4









"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase_						:					Tuple      				=			{"""configuration_detr""": ["""DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetrConfig""", """DetrOnnxConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Tuple      				=			["""DetrFeatureExtractor"""]
    UpperCamelCase_						:					int      				=			["""DetrImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Dict      				=			[
        """DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """DetrForObjectDetection""",
        """DetrForSegmentation""",
        """DetrModel""",
        """DetrPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_detr import DetrFeatureExtractor
        from .image_processing_detr import DetrImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_detr import (
            DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
            DetrForObjectDetection,
            DetrForSegmentation,
            DetrModel,
            DetrPreTrainedModel,
        )

else:
    import sys

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


