






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     : List[str]   	=      ['MobileViTFeatureExtractor']
 _a     : Optional[Any]   	=      ['MobileViTImageProcessor']

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

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _a     : Optional[int]   	=      [
     '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     : Optional[Any]   	=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








"""simple docstring"""






def      _lowercase							(    __snake_case   ,__snake_case      )		->  Tuple:
			# "extended trapezoidal rule"
			# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
			__lowerCAmelCase :    Optional[Any]				=			(boundary[1] - boundary[0]) / steps
			__lowerCAmelCase :    List[Any]				=			boundary[0]
			__lowerCAmelCase :    Any				=			boundary[1]
			__lowerCAmelCase :    Dict				=			make_points(__snake_case   ,__snake_case   ,__snake_case      )
			__lowerCAmelCase :    List[str]				=			0.0
			y += (h / 2.0) * f(__snake_case      )
			for i in x_i:
						# print(i)
						y += h * f(__snake_case      )
			y += (h / 2.0) * f(__snake_case      )
			return y


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Union[str, Any]:
			__lowerCAmelCase :    int				=			a + h
			while x < (b - h):
						yield x
						__lowerCAmelCase :    List[str]				=			x + h


def      _lowercase							(    __snake_case      )		->  Tuple:  # enter your function here
			__lowerCAmelCase :    int				=			(x - 0) * (x - 0)
			return y


def      _lowercase							(    )		->  int:
			__lowerCAmelCase :    Tuple				=			0.0  # Lower bound of integration
			__lowerCAmelCase :    Union[str, Any]				=			1.0  # Upper bound of integration
			__lowerCAmelCase :    Dict				=			10.0  # define number of steps or resolution
			__lowerCAmelCase :    Optional[Any]				=			[a, b]  # define boundary of integration
			__lowerCAmelCase :    Optional[int]				=			method_a(__snake_case   ,__snake_case      )
			print(F"""y = {y}"""      )


if __name__ == "__main__":
	main()