





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a        =						{
    "configuration_clipseg": [
        "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPSegConfig",
        "CLIPSegTextConfig",
        "CLIPSegVisionConfig",
    ],
    "processing_clipseg": ["CLIPSegProcessor"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a        =						[
        "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
        "CLIPSegModel",
        "CLIPSegPreTrainedModel",
        "CLIPSegTextModel",
        "CLIPSegVisionModel",
        "CLIPSegForImageSegmentation",
    ]

if TYPE_CHECKING:
    from .configuration_clipseg import (
        CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
        CLIPSegConfig,
        CLIPSegTextConfig,
        CLIPSegVisionConfig,
    )
    from .processing_clipseg import CLIPSegProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_clipseg import (
            CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
            CLIPSegForImageSegmentation,
            CLIPSegModel,
            CLIPSegPreTrainedModel,
            CLIPSegTextModel,
            CLIPSegVisionModel,
        )

else:
    import sys

    a        =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


a        =						logging.get_logger(__name__)

a        =						{
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}







class __a (   _snake_case			):
   __UpperCamelCase   : Tuple	      =	'camembert'





   def __init__(					self				:			int				,lowerCamelCase				:			List[Any]=3_0522				,lowerCamelCase				:			List[Any]=768				,lowerCamelCase				:			str=12				,lowerCamelCase				:			List[str]=12				,lowerCamelCase				:			Optional[Any]=3072				,lowerCamelCase				:			Tuple="gelu"				,lowerCamelCase				:			List[str]=0.1				,lowerCamelCase				:			Tuple=0.1				,lowerCamelCase				:			Union[str, Any]=512				,lowerCamelCase				:			Dict=2				,lowerCamelCase				:			Tuple=0.02				,lowerCamelCase				:			List[Any]=1E-1_2				,lowerCamelCase				:			Union[str, Any]=1				,lowerCamelCase				:			Optional[Any]=0				,lowerCamelCase				:			List[Any]=2				,lowerCamelCase				:			List[str]="absolute"				,lowerCamelCase				:			int=True				,lowerCamelCase				:			Any=None				,**lowerCamelCase				:			Optional[Any]				,):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE      							=       vocab_size
      __SCREAMING_SNAKE_CASE      							=       hidden_size
      __SCREAMING_SNAKE_CASE      							=       num_hidden_layers
      __SCREAMING_SNAKE_CASE      							=       num_attention_heads
      __SCREAMING_SNAKE_CASE      							=       hidden_act
      __SCREAMING_SNAKE_CASE      							=       intermediate_size
      __SCREAMING_SNAKE_CASE      							=       hidden_dropout_prob
      __SCREAMING_SNAKE_CASE      							=       attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE      							=       max_position_embeddings
      __SCREAMING_SNAKE_CASE      							=       type_vocab_size
      __SCREAMING_SNAKE_CASE      							=       initializer_range
      __SCREAMING_SNAKE_CASE      							=       layer_norm_eps
      __SCREAMING_SNAKE_CASE      							=       position_embedding_type
      __SCREAMING_SNAKE_CASE      							=       use_cache
      __SCREAMING_SNAKE_CASE      							=       classifier_dropout







class __a (   _snake_case			):





   @property
   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

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





