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__)







import math




def    snake_case_     (  snake_case			,							snake_case     )  ->				float:
       return math.pow(snake_case			,							2     ) - a




def    snake_case_     (  snake_case     )  ->				float:
       return 2 * x




def    snake_case_     (  snake_case     )  ->				float:
       lowercase__:   Dict  			=   2.0

       while start <= a:
              lowercase__:   str  			=   math.pow(snake_case			,							2     )

       return start




def    snake_case_     (  snake_case			,							snake_case = 99_99			,							snake_case = 0.0_0_0_0_0_0_0_0_0_0_0_0_0_1     )  ->				float:

       if a < 0:
              raise ValueError('math domain error'     )

       lowercase__:   Tuple  			=   get_initial_point(snake_case     )

       for _ in range(snake_case     ):
              lowercase__:   List[Any]  			=   value
              lowercase__:   Any  			=   value - fx(snake_case			,							snake_case     ) / fx_derivative(snake_case     )
              if abs(prev_value - value     ) < tolerance:
                     return value

       return value


if __name__ == "__main__":
 from doctest import testmod

 testmod()


