


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


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

SCREAMING_SNAKE_CASE			: str				   =					{
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}






class _lowerCamelCase(		_a  ):
       lowercase_					:      str       					=     """xglm"""
       lowercase_					:      Dict       					=     ["""past_key_values"""]

       lowercase_					:      str       					=     {
           """num_attention_heads""": """attention_heads""",
           """hidden_size""": """d_model""",
           """num_hidden_layers""": """num_layers""",
       }
       def __init__(   self, lowerCamelCase=25_60_08, lowerCamelCase=20_48, lowerCamelCase=10_24, lowerCamelCase=40_96, lowerCamelCase=24, lowerCamelCase=16, lowerCamelCase="gelu", lowerCamelCase=0.1, lowerCamelCase=0.1, lowerCamelCase=0.0, lowerCamelCase=0.0, lowerCamelCase=0.0_2, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=2, lowerCamelCase=1, lowerCamelCase=0, lowerCamelCase=2, **lowerCamelCase, )			->  Tuple:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						vocab_size
           _lowercase			:				Union[str, Any] 						=						max_position_embeddings
           _lowercase			:				Tuple 						=						d_model
           _lowercase			:				Union[str, Any] 						=						ffn_dim
           _lowercase			:				str 						=						num_layers
           _lowercase			:				int 						=						attention_heads
           _lowercase			:				List[str] 						=						activation_function
           _lowercase			:				Tuple 						=						dropout
           _lowercase			:				int 						=						attention_dropout
           _lowercase			:				Optional[int] 						=						activation_dropout
           _lowercase			:				List[str] 						=						layerdrop
           _lowercase			:				Optional[Any] 						=						init_std
           _lowercase			:				Any 						=						scale_embedding  # scale factor will be sqrt(d_model) if True
           _lowercase			:				Union[str, Any] 						=						use_cache

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






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase 							=  {
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          """UperNetForSemanticSegmentation""",
          """UperNetPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_upernet import UperNetConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
      import sys

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

