





from typing import TYPE_CHECKING

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


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

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Union[str, Any]         =			["MobileViTFeatureExtractor"]
 __A							:			List[Any]         =			["MobileViTImageProcessor"]

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

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Dict         =			[
     "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

 __A							:			int         =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












from collections import defaultdict
from math import ceil, sqrt





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE = 1_000_000							,     _SCREAMING_SNAKE_CASE = 10				)							-> int:




    """simple docstring"""




    _A     =							defaultdict(_SCREAMING_SNAKE_CASE				)

    for outer_width in range(3							,     (t_limit // 4) + 2				):
        if outer_width * outer_width > t_limit:
            _A     =							max(
                ceil(sqrt(outer_width * outer_width - t_limit				)				)							,     1				)
        else:
            _A     =							1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(_SCREAMING_SNAKE_CASE							,     outer_width - 1							,     2				):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= 10				)


if __name__ == "__main__":
 print(f"{solution() = }")





