


from typing import TYPE_CHECKING

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


a			:			Dict												=					{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			int												=					[
	    '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''RobertaPreLayerNormForCausalLM''',
	    '''RobertaPreLayerNormForMaskedLM''',
	    '''RobertaPreLayerNormForMultipleChoice''',
	    '''RobertaPreLayerNormForQuestionAnswering''',
	    '''RobertaPreLayerNormForSequenceClassification''',
	    '''RobertaPreLayerNormForTokenClassification''',
	    '''RobertaPreLayerNormModel''',
	    '''RobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Tuple												=					[
	    '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFRobertaPreLayerNormForCausalLM''',
	    '''TFRobertaPreLayerNormForMaskedLM''',
	    '''TFRobertaPreLayerNormForMultipleChoice''',
	    '''TFRobertaPreLayerNormForQuestionAnswering''',
	    '''TFRobertaPreLayerNormForSequenceClassification''',
	    '''TFRobertaPreLayerNormForTokenClassification''',
	    '''TFRobertaPreLayerNormMainLayer''',
	    '''TFRobertaPreLayerNormModel''',
	    '''TFRobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			str												=					[
	    '''FlaxRobertaPreLayerNormForCausalLM''',
	    '''FlaxRobertaPreLayerNormForMaskedLM''',
	    '''FlaxRobertaPreLayerNormForMultipleChoice''',
	    '''FlaxRobertaPreLayerNormForQuestionAnswering''',
	    '''FlaxRobertaPreLayerNormForSequenceClassification''',
	    '''FlaxRobertaPreLayerNormForTokenClassification''',
	    '''FlaxRobertaPreLayerNormModel''',
	    '''FlaxRobertaPreLayerNormPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	a			:			List[str]												=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






from collections import Counter
from timeit import timeit


def 		lowercase_	(  _UpperCamelCase = ""							,      ):


			'''simple docstring'''







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


def 		lowercase_	(  _UpperCamelCase = ""	):


			'''simple docstring'''







			if len(_UpperCamelCase	) == 0:
						return True
			__lowercase			       = input_str.replace(''' '''							,      ''''''	).lower()
			# character_freq_dict: Stores the frequency of every character in the input string
			__lowercase			       = {}

			for character in lower_case_input_str:
						__lowercase			       = character_freq_dict.get(_UpperCamelCase							,      0	) + 1
			__lowercase			       = 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_	(  _UpperCamelCase = ""	):


			'''simple docstring'''







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


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


