"""simple docstring"""







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block







@dataclass
class  UpperCamelCase	(    lowercase_						):
   lowercase							=						42







class  UpperCamelCase	(    lowercase_		,   lowercase_						):
   @register_to_config
   def __init__(    self  ,__UpperCamelCase = 6_5536  ,__UpperCamelCase = None  ,__UpperCamelCase = 2  ,__UpperCamelCase = 2  ,__UpperCamelCase = 0  ,__UpperCamelCase = "fourier"  ,__UpperCamelCase = True  ,__UpperCamelCase = False  ,__UpperCamelCase = 0.0  ,__UpperCamelCase = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")  ,__UpperCamelCase = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")  ,__UpperCamelCase = "UNetMidBlock1D"  ,__UpperCamelCase = None  ,__UpperCamelCase = (32, 32, 64)  ,__UpperCamelCase = None  ,__UpperCamelCase = 8  ,__UpperCamelCase = 1  ,__UpperCamelCase = False  ,)			->   List[Any]:


        '''simple docstring'''


        super().__init__()
        lowercase_			:       List[str]									=			sample_size

        # time
        if time_embedding_type == "fourier":
             lowercase_			:       Any									=			GaussianFourierProjection(
                 embedding_size=8  ,set_W_to_weight=__UpperCamelCase  ,log=__UpperCamelCase  ,flip_sin_to_cos=__UpperCamelCase    )
             lowercase_			:       Optional[Any]									=			2 * block_out_channels[0]
        elif time_embedding_type == "positional":
             lowercase_			:       Union[str, Any]									=			Timesteps(
                 block_out_channels[0]  ,flip_sin_to_cos=__UpperCamelCase  ,downscale_freq_shift=__UpperCamelCase    )
             lowercase_			:       Dict									=			block_out_channels[0]

        if use_timestep_embedding:
             lowercase_			:       Dict									=			block_out_channels[0] * 4
             lowercase_			:       Union[str, Any]									=			TimestepEmbedding(
                 in_channels=__UpperCamelCase  ,time_embed_dim=__UpperCamelCase  ,act_fn=__UpperCamelCase  ,out_dim=block_out_channels[0]  ,)

        lowercase_			:       Optional[Any]									=			nn.ModuleList([]    )
        lowercase_			:       Dict									=			None
        lowercase_			:       Optional[int]									=			nn.ModuleList([]    )
        lowercase_			:       int									=			None

        # down
        lowercase_			:       Optional[int]									=			in_channels
        for i, down_block_type in enumerate(__UpperCamelCase    ):
             lowercase_			:       Tuple									=			output_channel
             lowercase_			:       Optional[Any]									=			block_out_channels[i]

             if i == 0:
                  input_channel += extra_in_channels

             lowercase_			:       int									=			i == len(__UpperCamelCase    ) - 1

             lowercase_			:       List[str]									=			get_down_block(
                 __UpperCamelCase  ,num_layers=__UpperCamelCase  ,in_channels=__UpperCamelCase  ,out_channels=__UpperCamelCase  ,temb_channels=block_out_channels[0]  ,add_downsample=not is_final_block or downsample_each_block  ,)
             self.down_blocks.append(__UpperCamelCase    )

        # mid
        lowercase_			:       int									=			get_mid_block(
            __UpperCamelCase  ,in_channels=block_out_channels[-1]  ,mid_channels=block_out_channels[-1]  ,out_channels=block_out_channels[-1]  ,embed_dim=block_out_channels[0]  ,num_layers=__UpperCamelCase  ,add_downsample=__UpperCamelCase  ,)

        # up
        lowercase_			:       List[Any]									=			list(reversed(__UpperCamelCase    )    )
        lowercase_			:       Optional[Any]									=			reversed_block_out_channels[0]
        if out_block_type is None:
             lowercase_			:       List[str]									=			out_channels
        else:
             lowercase_			:       List[Any]									=			block_out_channels[0]

        for i, up_block_type in enumerate(__UpperCamelCase    ):
             lowercase_			:       List[Any]									=			output_channel
             lowercase_			:       Optional[Any]									=			(
                 reversed_block_out_channels[i + 1] if i < len(__UpperCamelCase    ) - 1 else final_upsample_channels
             )

             lowercase_			:       str									=			i == len(__UpperCamelCase    ) - 1

             lowercase_			:       int									=			get_up_block(
                 __UpperCamelCase  ,num_layers=__UpperCamelCase  ,in_channels=__UpperCamelCase  ,out_channels=__UpperCamelCase  ,temb_channels=block_out_channels[0]  ,add_upsample=not is_final_block  ,)
             self.up_blocks.append(__UpperCamelCase    )
             lowercase_			:       Union[str, Any]									=			output_channel

        # out
        lowercase_			:       Any									=			norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4  ,32    )
        lowercase_			:       Dict									=			get_out_block(
            out_block_type=__UpperCamelCase  ,num_groups_out=__UpperCamelCase  ,embed_dim=block_out_channels[0]  ,out_channels=__UpperCamelCase  ,act_fn=__UpperCamelCase  ,fc_dim=block_out_channels[-1] // 4  ,)







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase = True  ,)			->   Union[UNetaDOutput, Tuple]:


        '''simple docstring'''


        lowercase_			:       str									=			timestep
        if not torch.is_tensor(__UpperCamelCase    ):
             lowercase_			:       Optional[int]									=			torch.tensor([timesteps]  ,dtype=torch.long  ,device=sample.device    )
        elif torch.is_tensor(__UpperCamelCase    ) and len(timesteps.shape    ) == 0:
             lowercase_			:       Any									=			timesteps[None].to(sample.device    )

        lowercase_			:       int									=			self.time_proj(__UpperCamelCase    )
        if self.config.use_timestep_embedding:
             lowercase_			:       Tuple									=			self.time_mlp(__UpperCamelCase    )
        else:
             lowercase_			:       str									=			timestep_embed[..., None]
             lowercase_			:       Optional[int]									=			timestep_embed.repeat([1, 1, sample.shape[2]]    ).to(sample.dtype    )
             lowercase_			:       List[Any]									=			timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])    )

        # 2. down
        lowercase_			:       List[Any]									=			()
        for downsample_block in self.down_blocks:
             lowercase_							,						lowercase_			:       str									=			downsample_block(hidden_states=__UpperCamelCase  ,temb=__UpperCamelCase    )
             down_block_res_samples += res_samples

        # 3. mid
        if self.mid_block:
             lowercase_			:       List[Any]									=			self.mid_block(__UpperCamelCase  ,__UpperCamelCase    )

        # 4. up
        for i, upsample_block in enumerate(self.up_blocks    ):
             lowercase_			:       Optional[int]									=			down_block_res_samples[-1:]
             lowercase_			:       Union[str, Any]									=			down_block_res_samples[:-1]
             lowercase_			:       Tuple									=			upsample_block(__UpperCamelCase  ,res_hidden_states_tuple=__UpperCamelCase  ,temb=__UpperCamelCase    )

        # 5. post-process
        if self.out_block:
             lowercase_			:       Optional[int]									=			self.out_block(__UpperCamelCase  ,__UpperCamelCase    )

        if not return_dict:
             return (sample,)

        return UNetaDOutput(sample=__UpperCamelCase    )


"""simple docstring"""







from math import sqrt



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int   ):

     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(sqrt(__SCREAMING_SNAKE_CASE   ) + 1   )  ,    6   ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int = 1_00_01   ):
     lowercase_			:       str									=			0
     lowercase_			:       Optional[Any]									=			1
     while count != nth and number < 3:
          number += 1
          if is_prime(__SCREAMING_SNAKE_CASE   ):
               count += 1
     while count != nth:
          number += 2
          if is_prime(__SCREAMING_SNAKE_CASE   ):
               count += 1
     return number


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

