


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__)







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


__a     =			logging.get_logger(__name__)

__a     =			{"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       '''ctrl'''
      UpperCamelCase								=       ['''past_key_values''']
      UpperCamelCase								=       {
          '''max_position_embeddings''': '''n_positions''',
          '''hidden_size''': '''n_embd''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }





      def __init__(		self						:					str					,       __lowerCamelCase						:					Optional[int]=2_4_6_5_3_4					,       __lowerCamelCase						:					Union[str, Any]=2_5_6					,       __lowerCamelCase						:					int=1_2_8_0					,       __lowerCamelCase						:					Optional[Any]=8_1_9_2					,       __lowerCamelCase						:					List[str]=4_8					,       __lowerCamelCase						:					Dict=1_6					,       __lowerCamelCase						:					Union[str, Any]=0.1					,       __lowerCamelCase						:					Union[str, Any]=0.1					,       __lowerCamelCase						:					Union[str, Any]=1e-6					,       __lowerCamelCase						:					str=0.02					,       __lowerCamelCase						:					Union[str, Any]=True					,       **__lowerCamelCase						:					List[str]					,       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     vocab_size
         UpperCAmelCase							=     n_positions
         UpperCAmelCase							=     n_embd
         UpperCAmelCase							=     n_layer
         UpperCAmelCase							=     n_head
         UpperCAmelCase							=     dff
         UpperCAmelCase							=     resid_pdrop
         UpperCAmelCase							=     embd_pdrop
         UpperCAmelCase							=     layer_norm_epsilon
         UpperCAmelCase							=     initializer_range

         UpperCAmelCase							=     use_cache

         super().__init__(**__lowerCamelCase       )



