




def     _SCREAMING_SNAKE_CASE    (							lowercase			: list[int]			,							lowercase			: str  ):


      '''simple docstring'''



      lowerCamelCase_	  =					int(lowercase  )

      # Initialize Result
      lowerCamelCase_	  =					[]

      # Traverse through all denomination
      for denomination in reversed(lowercase  ):
            # Find denominations
            while int(lowercase  ) >= int(lowercase  ):
                  total_value -= int(lowercase  )
                  answer.append(lowercase  )  # Append the "answers" array

      return answer


# Driver Code
if __name__ == "__main__":
 lowerCamelCase       :	str									=     []
 lowerCamelCase       :	Dict									=     "0"

 if (
     input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
     == "y"
 ):
  lowerCamelCase       :	Optional[int]									=     int(input("Enter the number of denominations you want to add: ").strip())

  for i in range(0, n):
   denominations.append(int(input(F"""Denomination {i}: """).strip()))
  lowerCamelCase       :	Optional[int]									=     input("Enter the change you want to make in Indian Currency: ").strip()
 else:
  # All denominations of Indian Currency if user does not enter
  lowerCamelCase       :	Any									=     [1, 2, 5, 10, 20, 50, 100, 500, 2_000]
  lowerCamelCase       :	Optional[Any]									=     input("Enter the change you want to make: ").strip()

 if int(value) == 0 or int(value) < 0:
  print("The total value cannot be zero or negative.")

 else:
  print(F"""Following is minimal change for {value}: """)
  lowerCamelCase       :	Tuple									=     find_minimum_change(denominations, value)
  # Print result
  for i in range(len(answer)):
   print(answer[i], end=" ")









from typing import TYPE_CHECKING

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


lowerCamelCase       :	Dict									=     {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Optional[int]									=     ["ViTFeatureExtractor"]
 lowerCamelCase       :	Dict									=     ["ViTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	int									=     [
     "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTForImageClassification",
     "ViTForMaskedImageModeling",
     "ViTModel",
     "ViTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Tuple									=     [
     "TFViTForImageClassification",
     "TFViTModel",
     "TFViTPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Dict									=     [
     "FlaxViTForImageClassification",
     "FlaxViTModel",
     "FlaxViTPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_vit import ViTFeatureExtractor
  from .image_processing_vit import ViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit import (
      VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTForImageClassification,
      ViTForMaskedImageModeling,
      ViTModel,
      ViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
 import sys

 lowerCamelCase       :	Any									=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



