



'''simple docstring'''






from typing import TYPE_CHECKING

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


A_							: str						 =	{"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnnxConfig"""]}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: str						 =	["""DeiTFeatureExtractor"""]
   A_							: Optional[int]						 =	["""DeiTImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: Any						 =	[
       """DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """DeiTForImageClassification""",
       """DeiTForImageClassificationWithTeacher""",
       """DeiTForMaskedImageModeling""",
       """DeiTModel""",
       """DeiTPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: Any						 =	[
       """TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFDeiTForImageClassification""",
       """TFDeiTForImageClassificationWithTeacher""",
       """TFDeiTForMaskedImageModeling""",
       """TFDeiTModel""",
       """TFDeiTPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_deit import DeiTFeatureExtractor
      from .image_processing_deit import DeiTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deit import (
          DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DeiTForImageClassification,
          DeiTForImageClassificationWithTeacher,
          DeiTForMaskedImageModeling,
          DeiTModel,
          DeiTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deit import (
          TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDeiTForImageClassification,
          TFDeiTForImageClassificationWithTeacher,
          TFDeiTForMaskedImageModeling,
          TFDeiTModel,
          TFDeiTPreTrainedModel,
      )


else:
   import sys

   A_							: Dict						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








'''simple docstring'''






from __future__ import annotations

import typing
from collections import Counter




def     snake_case_					(     lowerCAmelCase_		)->		typing.Counter[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						typing.Counter[int]			     = Counter()
       for base in range(1       ,   max_perimeter + 1		):
              for perpendicular in range(lowerCAmelCase_       ,   max_perimeter + 1		):
                     _UpperCAmelCase  :						List[str]			     = (base * base + perpendicular * perpendicular) ** 0.5
                     if hypotenuse == int(lowerCAmelCase_		):
                            _UpperCAmelCase  :						Optional[Any]			     = int(base + perpendicular + hypotenuse		)
                            if perimeter > max_perimeter:
                                   continue
                            triplets[perimeter] += 1
       return triplets




def     snake_case_					(     lowerCAmelCase_ = 1000		)->		int:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = pythagorean_triple(lowerCAmelCase_		)
       return triplets.most_common(1		)[0][0]


if __name__ == "__main__":
   print(f"""Perimeter {solution()} has maximum solutions""")



