





from typing import TYPE_CHECKING

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


UpperCAmelCase_       : int							=    {
    """configuration_sam""": [
        """SAM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SamConfig""",
        """SamMaskDecoderConfig""",
        """SamPromptEncoderConfig""",
        """SamVisionConfig""",
    ],
    """processing_sam""": ["""SamProcessor"""],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : Optional[int]							=    [
          """SAM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """SamModel""",
          """SamPreTrainedModel""",
      ]
try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : Optional[int]							=    [
          """TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFSamModel""",
          """TFSamPreTrainedModel""",
      ]
try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : Any							=    ["""SamImageProcessor"""]


if TYPE_CHECKING:
      from .configuration_sam import (
          SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SamConfig,
          SamMaskDecoderConfig,
          SamPromptEncoderConfig,
          SamVisionConfig,
      )
      from .processing_sam import SamProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_sam import SamImageProcessor

else:
      import sys

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







from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_       : Union[str, Any]							=    logging.get_logger(__name__)

UpperCAmelCase_       : int							=    {
    """google/electra-small-generator""": """https://huggingface.co/google/electra-small-generator/resolve/main/config.json""",
    """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/config.json""",
    """google/electra-large-generator""": """https://huggingface.co/google/electra-large-generator/resolve/main/config.json""",
    """google/electra-small-discriminator""": (
        """https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"""
    ),
    """google/electra-base-discriminator""": (
        """https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"""
    ),
    """google/electra-large-discriminator""": (
        """https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"""
    ),
}





class   lowercase__     (				__A	):
  __UpperCamelCase						  =	"""electra"""







  def __init__(						self     ,    _lowercase=30_522     ,    _lowercase=128     ,    _lowercase=256     ,    _lowercase=12     ,    _lowercase=4     ,    _lowercase=1_024     ,    _lowercase="gelu"     ,    _lowercase=0.1     ,    _lowercase=0.1     ,    _lowercase=512     ,    _lowercase=2     ,    _lowercase=0.02     ,    _lowercase=1e-12     ,    _lowercase="first"     ,    _lowercase=True     ,    _lowercase="gelu"     ,    _lowercase=0.1     ,    _lowercase=0     ,    _lowercase="absolute"     ,    _lowercase=True     ,    _lowercase=None     ,    **_lowercase     ,    ):
       super().__init__(pad_token_id=_lowercase     ,    **_lowercase    )

       lowerCAmelCase_       :							Dict			 =		vocab_size
       lowerCAmelCase_       :							Tuple			 =		embedding_size
       lowerCAmelCase_       :							Any			 =		hidden_size
       lowerCAmelCase_       :							Optional[Any]			 =		num_hidden_layers
       lowerCAmelCase_       :							Union[str, Any]			 =		num_attention_heads
       lowerCAmelCase_       :							List[str]			 =		intermediate_size
       lowerCAmelCase_       :							Union[str, Any]			 =		hidden_act
       lowerCAmelCase_       :							Any			 =		hidden_dropout_prob
       lowerCAmelCase_       :							Optional[Any]			 =		attention_probs_dropout_prob
       lowerCAmelCase_       :							int			 =		max_position_embeddings
       lowerCAmelCase_       :							Optional[int]			 =		type_vocab_size
       lowerCAmelCase_       :							Dict			 =		initializer_range
       lowerCAmelCase_       :							Any			 =		layer_norm_eps

       lowerCAmelCase_       :							str			 =		summary_type
       lowerCAmelCase_       :							Optional[int]			 =		summary_use_proj
       lowerCAmelCase_       :							Optional[int]			 =		summary_activation
       lowerCAmelCase_       :							int			 =		summary_last_dropout
       lowerCAmelCase_       :							str			 =		position_embedding_type
       lowerCAmelCase_       :							Optional[int]			 =		use_cache
       lowerCAmelCase_       :							Tuple			 =		classifier_dropout





class   lowercase__     (				__A	):







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