






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__				   =		{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__				   =		["MobileViTFeatureExtractor"]
     UpperCAmelCase__				   =		["MobileViTImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__				   =		[
         "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MobileViTForImageClassification",
         "MobileViTForSemanticSegmentation",
         "MobileViTModel",
         "MobileViTPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__				   =		[
         "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFMobileViTForImageClassification",
         "TFMobileViTForSemanticSegmentation",
         "TFMobileViTModel",
         "TFMobileViTPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_mobilevit import MobileViTFeatureExtractor
          from .image_processing_mobilevit import MobileViTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilevit import (
              MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileViTForImageClassification,
              MobileViTForSemanticSegmentation,
              MobileViTModel,
              MobileViTPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mobilevit import (
              TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMobileViTForImageClassification,
              TFMobileViTForSemanticSegmentation,
              TFMobileViTModel,
              TFMobileViTPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase__				   =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










from math import factorial

UpperCAmelCase__				   =		{str(digit): factorial(digit) for digit in range(10)}
def 						_a    (      a :int )							->			int:
 if not isinstance(a	,							a ):
  raise TypeError('''Parameter number must be int''' )

 if number < 0:
  raise ValueError('''Parameter number must be greater than or equal to 0''' )

 # Converts number in string to iterate on its digits and adds its factorial.
 return sum(DIGIT_FACTORIAL[digit] for digit in str(a ) )
def 						_a    (      a :int = 60	,							a :int = 1_000_000 )							->			int:

 if not isinstance(a	,							a ) or not isinstance(a	,							a ):
  raise TypeError('''Parameters chain_length and number_limit must be int''' )

 if chain_length <= 0 or number_limit <= 0:
  raise ValueError(
      '''Parameters chain_length and number_limit must be greater than 0''' )

 # the counter for the chains with the exact desired length
 a		  =      0
 # the cached sizes of the previous chains
 a		  =      {}

 for start_chain_element in range(1	,							a ):
  # The temporary set will contain the elements of the chain
  a		  =      set()
  a		  =      0

  # Stop computing the chain when you find a cached size, a repeating item or the
  # length is greater then the desired one.
  a		  =      start_chain_element
  while (
      chain_element not in chain_sets_lengths
      and chain_element not in chain_set
      and chain_set_length <= chain_length
  ):
   chain_set.add(a )
   chain_set_length += 1
   a		  =      digit_factorial_sum(a )

  if chain_element in chain_sets_lengths:
   chain_set_length += chain_sets_lengths[chain_element]

  a		  =      chain_set_length

  # If chain contains the exact amount of elements increase the counter
  if chain_set_length == chain_length:
   chains_counter += 1

 return chains_counter


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(f"""{solution()}""")


