


'''simple docstring'''



from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase	:						Tuple       =	{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						int       =	[
	    'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'InformerForPrediction',
	    'InformerModel',
	    'InformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_informer import (
		    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    InformerForPrediction,
		    InformerModel,
		    InformerPreTrainedModel,
		)

else:
	import sys

	__lowercase	:						Optional[int]       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








'''simple docstring'''





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


__a          =		logging.get_logger(__name__)

__a          =		{
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		"glpn"




    def __init__(    self					:     Optional[Any] ,     snake_case_					:     List[str]=3 ,     snake_case_					:     Dict=4 ,     snake_case_					:     List[Any]=[2, 2, 2, 2] ,     snake_case_					:     int=[8, 4, 2, 1] ,     snake_case_					:     List[str]=[32, 64, 160, 256] ,     snake_case_					:     Tuple=[7, 3, 3, 3] ,     snake_case_					:     List[Any]=[4, 2, 2, 2] ,     snake_case_					:     Tuple=[1, 2, 5, 8] ,     snake_case_					:     List[str]=[4, 4, 4, 4] ,     snake_case_					:     Optional[int]="gelu" ,     snake_case_					:     Dict=0.0 ,     snake_case_					:     Union[str, Any]=0.0 ,     snake_case_					:     List[Any]=0.02 ,     snake_case_					:     Tuple=0.1 ,     snake_case_					:     Any=1E-6 ,     snake_case_					:     Dict=64 ,     snake_case_					:     Tuple=10 ,     snake_case_					:     List[Any]=-1 ,     **snake_case_					:     Optional[Any] ,     ):
          super().__init__(**snake_case_						)

          snake_case__  :    Optional[Any]    						=      num_channels
          snake_case__  :    Dict    						=      num_encoder_blocks
          snake_case__  :    Tuple    						=      depths
          snake_case__  :    Union[str, Any]    						=      sr_ratios
          snake_case__  :    Tuple    						=      hidden_sizes
          snake_case__  :    Optional[Any]    						=      patch_sizes
          snake_case__  :    int    						=      strides
          snake_case__  :    List[Any]    						=      mlp_ratios
          snake_case__  :    Optional[int]    						=      num_attention_heads
          snake_case__  :    Dict    						=      hidden_act
          snake_case__  :    int    						=      hidden_dropout_prob
          snake_case__  :    Optional[Any]    						=      attention_probs_dropout_prob
          snake_case__  :    str    						=      initializer_range
          snake_case__  :    List[str]    						=      drop_path_rate
          snake_case__  :    int    						=      layer_norm_eps
          snake_case__  :    Tuple    						=      decoder_hidden_size
          snake_case__  :    List[Any]    						=      max_depth
          snake_case__  :    Dict    						=      head_in_index
