"""simple docstring"""


from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput






class    a__    (   nn.Module	):


   def __init__(     self,				_UpperCAmelCase = 16,				_UpperCAmelCase = 88,				_UpperCAmelCase = None,				_UpperCAmelCase = 1,				_UpperCAmelCase = 0.0,				_UpperCAmelCase = 32,				_UpperCAmelCase = None,				_UpperCAmelCase = False,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = "geglu",				_UpperCAmelCase = None,				):
        '''simple docstring'''



        super().__init__()
        lowercase__				 =							nn.ModuleList(
            [
                TransformeraDModel(
                    num_attention_heads=_UpperCAmelCase,				attention_head_dim=_UpperCAmelCase,				in_channels=_UpperCAmelCase,				num_layers=_UpperCAmelCase,				dropout=_UpperCAmelCase,				norm_num_groups=_UpperCAmelCase,				cross_attention_dim=_UpperCAmelCase,				attention_bias=_UpperCAmelCase,				sample_size=_UpperCAmelCase,				num_vector_embeds=_UpperCAmelCase,				activation_fn=_UpperCAmelCase,				num_embeds_ada_norm=_UpperCAmelCase,				)
                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
        lowercase__				 =							0.5

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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase = True,				):
        '''simple docstring'''



        lowercase__				 =							hidden_states

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

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

        if not return_dict:
             return (output_states,)

        return TransformeraDModelOutput(sample=_UpperCAmelCase  )
"""simple docstring"""


import itertools
import math







def 					__a   (		A      ):






     '''simple docstring'''



     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5    ,				int(math.sqrt(A      ) + 1      )    ,				6      ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True







def 					__a   (		):






     '''simple docstring'''



     lowercase__				 =							2
     while True:
          if is_prime(A      ):
               yield num
          num += 1







def 					__a   (		A = 1_00_01      ):






     '''simple docstring'''



     return next(itertools.islice(prime_generator()    ,				nth - 1    ,				A      )      )


if __name__ == "__main__":
 print(F'{solution() = }')




