


from collections import Counter
from timeit import timeit
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__ = ""      ,	):
 return sum(c % 2 for c in Counter(input_str.replace(' '      ,	''						).lower()						).values()						) < 2
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__ = ""						):
 if len(SCREAMING_SNAKE_CASE__						) == 0:
  return True
 __lowerCamelCase		:							Any		 =						input_str.replace(' '      ,	''						).lower()
 # character_freq_dict: Stores the frequency of every character in the input string
 __lowerCamelCase		:							dict[str, int]		 =						{}

 for character in lower_case_input_str:
  __lowerCamelCase		:							int		 =						character_freq_dict.get(SCREAMING_SNAKE_CASE__      ,	0						) + 1
 __lowerCamelCase		:							Union[str, Any]		 =						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 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__ = ""						):
 print('\nFor string = '      ,	SCREAMING_SNAKE_CASE__      ,	':'						)
 print(
     '> can_string_be_rearranged_as_palindrome_counter()'      ,	'\tans ='      ,	can_string_be_rearranged_as_palindrome_counter(SCREAMING_SNAKE_CASE__						)      ,	'\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(SCREAMING_SNAKE_CASE__						)      ,	'\ttime ='      ,	timeit(
         'z.can_string_be_rearranged_as_palindrome(z.check_str)'      ,	setup='import __main__ as z'      ,	)      ,	'seconds'      ,	)


if __name__ == "__main__":
       lowercase_	   =     input(
           'Enter string to determine if it can be rearranged as a palindrome or not: '
       ).strip()
       benchmark(check_str)
       lowercase_	   =     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_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase_	   =     {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     ['ConvNextFeatureExtractor']
       lowercase_	   =     ['ConvNextImageProcessor']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'ConvNextForImageClassification',
           'ConvNextModel',
           'ConvNextPreTrainedModel',
           'ConvNextBackbone',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'TFConvNextForImageClassification',
           'TFConvNextModel',
           'TFConvNextPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_convnext import ConvNextFeatureExtractor
              from .image_processing_convnext import ConvNextImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_convnext import (
                  CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ConvNextBackbone,
                  ConvNextForImageClassification,
                  ConvNextModel,
                  ConvNextPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
       import sys

       lowercase_	   =     _LazyModule(__name__, globals()['__file__'], _import_structure)

