




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


_lowerCAmelCase  :      str						=      logging.get_logger(__name__)

_lowerCAmelCase  :      List[Any]						=      {
    "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 	_UpperCamelCase					( A__						):
      UpperCAmelCase_						=						"""xglm"""
      UpperCAmelCase_						=						["""past_key_values"""]

      UpperCAmelCase_						=						{
          """num_attention_heads""": """attention_heads""",
          """hidden_size""": """d_model""",
          """num_hidden_layers""": """num_layers""",
      }
      def __init__(	self      :Union[str, Any]       ,					lowerCamelCase      :Optional[Any]=25_6008       ,					lowerCamelCase      :Union[str, Any]=2048       ,					lowerCamelCase      :int=1024       ,					lowerCamelCase      :int=4096       ,					lowerCamelCase      :Dict=24       ,					lowerCamelCase      :Tuple=16       ,					lowerCamelCase      :List[Any]="gelu"       ,					lowerCamelCase      :Optional[Any]=0.1       ,					lowerCamelCase      :List[Any]=0.1       ,					lowerCamelCase      :int=0.0       ,					lowerCamelCase      :Union[str, Any]=0.0       ,					lowerCamelCase      :Tuple=0.02       ,					lowerCamelCase      :Dict=True       ,					lowerCamelCase      :List[str]=True       ,					lowerCamelCase      :str=2       ,					lowerCamelCase      :Tuple=1       ,					lowerCamelCase      :Optional[Any]=0       ,					lowerCamelCase      :Dict=2       ,					**lowerCamelCase      :Union[str, Any]       ,					)   ->	Optional[int]:
       UpperCAmelCase__							       =  vocab_size
       UpperCAmelCase__							       =  max_position_embeddings
       UpperCAmelCase__							       =  d_model
       UpperCAmelCase__							       =  ffn_dim
       UpperCAmelCase__							       =  num_layers
       UpperCAmelCase__							       =  attention_heads
       UpperCAmelCase__							       =  activation_function
       UpperCAmelCase__							       =  dropout
       UpperCAmelCase__							       =  attention_dropout
       UpperCAmelCase__							       =  activation_dropout
       UpperCAmelCase__							       =  layerdrop
       UpperCAmelCase__							       =  init_std
       UpperCAmelCase__							       =  scale_embedding  # scale factor will be sqrt(d_model) if True
       UpperCAmelCase__							       =  use_cache

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








'''simple docstring'''
from typing import TYPE_CHECKING

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


lowerCamelCase :      int					 =     {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      int					 =     ["GLPNFeatureExtractor"]
     lowerCamelCase :      Optional[int]					 =     ["GLPNImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GLPNForDepthEstimation",
         "GLPNLayer",
         "GLPNModel",
         "GLPNPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_glpn import GLPNFeatureExtractor
          from .image_processing_glpn import GLPNImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_glpn import (
              GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
              GLPNForDepthEstimation,
              GLPNLayer,
              GLPNModel,
              GLPNPreTrainedModel,
          )


else:
     import sys

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