



"""simple docstring"""
from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				str     						=					CustomTokenizer
  pass








"""simple docstring"""
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__)



