


from collections import Counter
from timeit import timeit

def       lowercase_		(		_A			:    str = ""    ,					):

      """simple docstring"""




      return sum(c % 2 for c in Counter(input_str.replace(" "    ,					""						).lower()						).values()						) < 2

def       lowercase_		(		_A			:    str = ""						):

      """simple docstring"""




      if len(__lowerCAmelCase						) == 0:
            return True
      lowerCamelCase__		:				Tuple    =  input_str.replace(" "    ,					""						).lower()
      # character_freq_dict: Stores the frequency of every character in the input string
      lowerCamelCase__		:				List[Any]    =  {}

      for character in lower_case_input_str:
            lowerCamelCase__		:				Tuple    =  character_freq_dict.get(__lowerCAmelCase    ,					0						) + 1
      lowerCamelCase__		:				Optional[int]    =  0

      for character_count in character_freq_dict.values():
            if character_count % 2:
                  odd_char += 1
      if odd_char > 1:
            return False
      return True

def       lowercase_		(		_A			:    str = ""						):

      """simple docstring"""




      print("\nFor string = "    ,					__lowerCAmelCase    ,					":"						)
      print(
          "> can_string_be_rearranged_as_palindrome_counter()"    ,					"\tans ="    ,					can_string_be_rearranged_as_palindrome_counter(__lowerCAmelCase						)    ,					"\ttime ="    ,					timeit(
              "z.can_string_be_rearranged_as_palindrome_counter(z.check_str)"    ,					setup="import __main__ as z"    ,					)    ,					"seconds"    ,					)
      print(
          "> can_string_be_rearranged_as_palindrome()"    ,					"\tans ="    ,					can_string_be_rearranged_as_palindrome(__lowerCAmelCase						)    ,					"\ttime ="    ,					timeit(
              "z.can_string_be_rearranged_as_palindrome(z.check_str)"    ,					setup="import __main__ as z"    ,					)    ,					"seconds"    ,					)


if __name__ == "__main__":
    A      :    str		=					input(
        "Enter string to determine if it can be rearranged as a palindrome or not: "
    ).strip()
    benchmark(check_str)
    A      :    str		=					can_string_be_rearranged_as_palindrome_counter(check_str)
    print(f'{check_str} can {"" if status else "not "}be rearranged as a palindrome')




from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__   :			List[Any]												=    {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ResNetForImageClassification",
					    "ResNetModel",
					    "ResNetPreTrainedModel",
					    "ResNetBackbone",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			List[Any]												=    [
					    "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFResNetForImageClassification",
					    "TFResNetModel",
					    "TFResNetPreTrainedModel",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    [
					    "FlaxResNetForImageClassification",
					    "FlaxResNetModel",
					    "FlaxResNetPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_resnet import (
										    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ResNetBackbone,
										    ResNetForImageClassification,
										    ResNetModel,
										    ResNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_resnet import (
										    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFResNetForImageClassification,
										    TFResNetModel,
										    TFResNetPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
					import sys

					SCREAMING_SNAKE_CASE__   :			Any												=    _LazyModule(__name__, globals()["__file__"], _import_structure)



