
from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput






class        SCREAMING_SNAKE_CASE__    (							nn.Module    ):

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_ = 16     ,	SCREAMING_SNAKE_CASE_ = 88     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = 0.0     ,	SCREAMING_SNAKE_CASE_ = 32     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = "geglu"     ,	SCREAMING_SNAKE_CASE_ = None     ,	)->   int:





													'''simple docstring'''






													super().__init__()
													__UpperCamelCase       =			nn.ModuleList(
													    [
													        TransformeraDModel(
													            num_attention_heads=SCREAMING_SNAKE_CASE_     ,	attention_head_dim=SCREAMING_SNAKE_CASE_     ,	in_channels=SCREAMING_SNAKE_CASE_     ,	num_layers=SCREAMING_SNAKE_CASE_     ,	dropout=SCREAMING_SNAKE_CASE_     ,	norm_num_groups=SCREAMING_SNAKE_CASE_     ,	cross_attention_dim=SCREAMING_SNAKE_CASE_     ,	attention_bias=SCREAMING_SNAKE_CASE_     ,	sample_size=SCREAMING_SNAKE_CASE_     ,	num_vector_embeds=SCREAMING_SNAKE_CASE_     ,	activation_fn=SCREAMING_SNAKE_CASE_     ,	num_embeds_ada_norm=SCREAMING_SNAKE_CASE_     ,	)
													        for _ in range(2			)
													    ]			)

													# Variables that can be set by a pipeline:

													# The ratio of transformer1 to transformer2's output states to be combined during inference
													__UpperCamelCase       =			0.5

													# The shape of `encoder_hidden_states` is expected to be
													# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
													__UpperCamelCase       =			[77, 257]

													# Which transformer to use to encode which condition.
													# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
													__UpperCamelCase       =			[1, 0]



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_ = True     ,	)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			hidden_states

													__UpperCamelCase       =			[]
													__UpperCamelCase       =			0
													# attention_mask is not used yet
													for i in range(2			):
																			# for each of the two transformers, pass the corresponding condition tokens
																			__UpperCamelCase       =			encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
																			__UpperCamelCase       =			self.transformer_index_for_condition[i]
																			__UpperCamelCase       =			self.transformers[transformer_index](
																			    SCREAMING_SNAKE_CASE_     ,	encoder_hidden_states=SCREAMING_SNAKE_CASE_     ,	timestep=SCREAMING_SNAKE_CASE_     ,	cross_attention_kwargs=SCREAMING_SNAKE_CASE_     ,	return_dict=SCREAMING_SNAKE_CASE_     ,	)[0]
																			encoded_states.append(encoded_state - input_states			)
																			tokens_start += self.condition_lengths[i]

													__UpperCamelCase       =			encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
													__UpperCamelCase       =			output_states + input_states

													if not return_dict:
																			return (output_states,)

													return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE_			)






from collections import Counter
from timeit import timeit




def 	A_ (							snake_case							:   str = "" ,      )      ->    bool:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   str = "" )      ->    bool:



						'''simple docstring'''

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

						for character in lower_case_input_str:
												__UpperCamelCase       =			character_freq_dict.get(snake_case ,      0 ) + 1
						__UpperCamelCase       =			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 	A_ (							snake_case							:   str = "" )      ->    None:



						'''simple docstring'''

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


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




