






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowerCAmelCase_						   =    logging.get_logger(__name__)  # pylint: disable=invalid-name



class        _lowerCAmelCase    (			UpperCAmelCase__							):


  '''simple docstring'''







  def __init__(      self      :					Dict      ,				UpperCamelCase      :					Optional[Any]      ,				UpperCamelCase      :					str=7_68							):


    '''simple docstring'''





    super().__init__(__lowerCAmelCase							)
    _snake_case   :				Dict														=	proj_size

    _snake_case   :				Optional[Any]														=	CLIPVisionModel(__lowerCAmelCase							)
    _snake_case   :				int														=	PaintByExampleMapper(__lowerCAmelCase							)
    _snake_case   :				Optional[int]														=	nn.LayerNorm(config.hidden_size							)
    _snake_case   :				Union[str, Any]														=	nn.Linear(config.hidden_size      ,				self.proj_size							)

    # uncondition for scaling
    _snake_case   :				Optional[Any]														=	nn.Parameter(torch.randn((1, 1, self.proj_size)							)							)







  def UpperCamelCase_ (      self      :					Any      ,				UpperCamelCase      :					int      ,				UpperCamelCase      :					Tuple=False							):


    '''simple docstring'''





    _snake_case   :				str														=	self.model(pixel_values=__lowerCAmelCase							)
    _snake_case   :				Optional[int]														=	clip_output.pooler_output
    _snake_case   :				int														=	self.mapper(latent_states[:, None]							)
    _snake_case   :				Optional[Any]														=	self.final_layer_norm(__lowerCAmelCase							)
    _snake_case   :				Any														=	self.proj_out(__lowerCAmelCase							)
    if return_uncond_vector:
      return latent_states, self.uncond_vector

    return latent_states




class        _lowerCAmelCase    (			nn.Module							):


  '''simple docstring'''







  def __init__(      self      :					List[str]      ,				UpperCamelCase      :					List[str]							):


    '''simple docstring'''





    super().__init__()
    _snake_case   :				Any														=	(config.num_hidden_layers + 1) // 5
    _snake_case   :				Tuple														=	config.hidden_size
    _snake_case   :				List[str]														=	1
    _snake_case   :				Dict														=	nn.ModuleList(
        [
            BasicTransformerBlock(__lowerCAmelCase      ,				__lowerCAmelCase      ,				__lowerCAmelCase      ,				activation_fn='gelu'      ,				attention_bias=__lowerCAmelCase							)
            for _ in range(__lowerCAmelCase							)
        ]							)







  def UpperCamelCase_ (      self      :					Dict      ,				UpperCamelCase      :					Any							):


    '''simple docstring'''





    for block in self.blocks:
      _snake_case   :				Optional[int]														=	block(__lowerCAmelCase							)

    return hidden_states











def     lowerCamelCase_	(      lowerCAmelCase:	int				)->		list:
  _snake_case   :				List[Any]														=	int(lowerCAmelCase				)
  if n_element < 1:
    _snake_case   :				int														=	ValueError('a should be a positive number'				)
    raise my_error

  _snake_case   :				Union[str, Any]														=	[1]
  _snake_case							,					_snake_case							,					_snake_case   :				Any														=	(0, 0, 0)
  _snake_case   :				str														=	1
  while index < n_element:
    while hamming_list[i] * 2 <= hamming_list[-1]:
      i += 1
    while hamming_list[j] * 3 <= hamming_list[-1]:
      j += 1
    while hamming_list[k] * 5 <= hamming_list[-1]:
      k += 1
    hamming_list.append(
        min(hamming_list[i] * 2    ,							hamming_list[j] * 3    ,							hamming_list[k] * 5				)				)
    index += 1
  return hamming_list


if __name__ == "__main__":
      lowerCAmelCase_						   =    input("""Enter the last number (nth term) of the Hamming Number Series: """)
      print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
      lowerCAmelCase_						   =    hamming(int(n))
      print("""-----------------------------------------------------""")
      print(F"""The list with nth numbers is: {hamming_numbers}""")
      print("""-----------------------------------------------------""")




