


'''simple docstring'''






from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							{
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "umt5"
							lowercase__    :     Optional[Any]					=   ["past_key_values"]







							def __init__(	self      ,					lowerCamelCase_=25_01_12      ,					lowerCamelCase_=5_12      ,					lowerCamelCase_=64      ,					lowerCamelCase_=10_24      ,					lowerCamelCase_=8      ,					lowerCamelCase_=None      ,					lowerCamelCase_=6      ,					lowerCamelCase_=32      ,					lowerCamelCase_=1_28      ,					lowerCamelCase_=0.1      ,					lowerCamelCase_=1e-6      ,					lowerCamelCase_=1.0      ,					lowerCamelCase_="gated-gelu"      ,					lowerCamelCase_=True      ,					lowerCamelCase_=True      ,					lowerCamelCase_="T5Tokenizer"      ,					lowerCamelCase_=True      ,					lowerCamelCase_=0      ,					lowerCamelCase_=1      ,					lowerCamelCase_=0      ,					**lowerCamelCase_      ,					) ->							str:
														super().__init__(
														    is_encoder_decoder=lowerCamelCase_      ,					tokenizer_class=lowerCamelCase_      ,					tie_word_embeddings=lowerCamelCase_      ,					pad_token_id=lowerCamelCase_      ,					eos_token_id=lowerCamelCase_      ,					decoder_start_token_id=lowerCamelCase_      ,					**lowerCamelCase_      ,					)
														lowerCAmelCase__      =			vocab_size
														lowerCAmelCase__      =			d_model
														lowerCAmelCase__      =			d_kv
														lowerCAmelCase__      =			d_ff
														lowerCAmelCase__      =			num_layers
														lowerCAmelCase__      =			(
														    num_decoder_layers if num_decoder_layers is not None else self.num_layers
														)  # default = symmetry
														lowerCAmelCase__      =			num_heads
														lowerCAmelCase__      =			relative_attention_num_buckets
														lowerCAmelCase__      =			relative_attention_max_distance
														lowerCAmelCase__      =			dropout_rate
														lowerCAmelCase__      =			layer_norm_epsilon
														lowerCAmelCase__      =			initializer_factor
														lowerCAmelCase__      =			feed_forward_proj
														lowerCAmelCase__      =			use_cache

														lowerCAmelCase__      =			self.feed_forward_proj.split('''-'''     )
														lowerCAmelCase__      =			act_info[-1]
														lowerCAmelCase__      =			act_info[0] == '''gated'''

														if len(lowerCamelCase_     ) > 1 and act_info[0] != "gated" or len(lowerCamelCase_     ) > 2:
																					raise ValueError(
																					    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
																					    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
																					    '''\'gated-gelu\' or \'relu\''''     )

														if feed_forward_proj == "gated-gelu":
																					lowerCAmelCase__      =			'''gelu_new'''







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														return self.d_model







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														return self.num_heads







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														return self.num_layers




class 	a__       (							a__						):





							'''simple docstring'''






							@property
							# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Mapping[str, Mapping[int, str]]:
														lowerCAmelCase__      =			{
														    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
														    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
														}
														if self.use_past:
																					lowerCAmelCase__      =			'''past_encoder_sequence + sequence'''
																					lowerCAmelCase__      =			{0: '''batch'''}
																					lowerCAmelCase__      =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
														else:
																					lowerCAmelCase__      =			{0: '''batch''', 1: '''decoder_sequence'''}
																					lowerCAmelCase__      =			{0: '''batch''', 1: '''decoder_sequence'''}

														if self.use_past:
																					self.fill_with_past_key_values_(lowerCamelCase_      ,					direction='''inputs'''     )

														return common_inputs







							@property
							# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														return 13







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							float:
														return 5e-4



'''simple docstring'''






from collections import Counter
from timeit import timeit


def       _snake_case     (  A = ""				,					)		->					bool:
							return sum(c % 2 for c in Counter(input_str.replace(''' '''				,					'''''' ).lower() ).values() ) < 2


def       _snake_case     (  A = "" )		->					bool:
							if len(A ) == 0:
														return True
							lowerCAmelCase__      =			input_str.replace(''' '''				,					'''''' ).lower()
							# character_freq_dict: Stores the frequency of every character in the input string
							lowerCAmelCase__      =			{}

							for character in lower_case_input_str:
														lowerCAmelCase__      =			character_freq_dict.get(A				,					0 ) + 1
							lowerCAmelCase__      =			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       _snake_case     (  A = "" )		->					None:
							print('''\nFor string = '''				,					A				,					''':''' )
							print(
							    '''> can_string_be_rearranged_as_palindrome_counter()'''				,					'''\tans ='''				,					can_string_be_rearranged_as_palindrome_counter(A )				,					'''\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(A )				,					'''\ttime ='''				,					timeit(
							        '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''				,					setup='''import __main__ as z'''				,					)				,					'''seconds'''				,					)


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