



from typing import TYPE_CHECKING

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


__UpperCAmelCase          =       {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase          =       ['''DPTFeatureExtractor''']
    __UpperCAmelCase          =       ['''DPTImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase          =       [
        '''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''DPTForDepthEstimation''',
        '''DPTForSemanticSegmentation''',
        '''DPTModel''',
        '''DPTPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_dpt import DPTFeatureExtractor
        from .image_processing_dpt import DPTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_dpt import (
            DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DPTForDepthEstimation,
            DPTForSemanticSegmentation,
            DPTModel,
            DPTPreTrainedModel,
        )


else:
    import sys

    __UpperCAmelCase          =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






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


lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[Any]									=				"vit_msn"







			def __init__(     self :				Dict							,   _UpperCamelCase :				Optional[int]=7_6_8							,   _UpperCamelCase :				Optional[Any]=1_2							,   _UpperCamelCase :				Union[str, Any]=1_2							,   _UpperCamelCase :				str=3_0_7_2							,   _UpperCamelCase :				Tuple="gelu"							,   _UpperCamelCase :				Dict=0.0							,   _UpperCamelCase :				Dict=0.0							,   _UpperCamelCase :				List[str]=0.02							,   _UpperCamelCase :				List[Any]=1e-06							,   _UpperCamelCase :				Any=2_2_4							,   _UpperCamelCase :				Optional[Any]=1_6							,   _UpperCamelCase :				Any=3							,   _UpperCamelCase :				str=True							,   **_UpperCamelCase :				Any							,   )      ->int:
									super().__init__(**_UpperCamelCase							)

									snake_case_   =	hidden_size
									snake_case_   =	num_hidden_layers
									snake_case_   =	num_attention_heads
									snake_case_   =	intermediate_size
									snake_case_   =	hidden_act
									snake_case_   =	hidden_dropout_prob
									snake_case_   =	attention_probs_dropout_prob
									snake_case_   =	initializer_range
									snake_case_   =	layer_norm_eps
									snake_case_   =	image_size
									snake_case_   =	patch_size
									snake_case_   =	num_channels
									snake_case_   =	qkv_bias