"""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							=						4_2







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_			:       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"""







import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812



def     lowercase__( ):
     print('Making key files...'   )
     make_key_files('rsa'  ,    10_24   )
     print('Key files generation successful.'   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int   ):
     print('Generating prime p...'   )
     lowercase_			:       List[str]									=			rabinMiller.generate_large_prime(__SCREAMING_SNAKE_CASE   )
     print('Generating prime q...'   )
     lowercase_			:       int									=			rabinMiller.generate_large_prime(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Union[str, Any]									=			p * q

     print('Generating e that is relatively prime to (p - 1) * (q - 1)...'   )
     while True:
          lowercase_			:       str									=			random.randrange(2 ** (key_size - 1)  ,    2 ** (key_size)   )
          if cryptoMath.gcd(__SCREAMING_SNAKE_CASE  ,    (p - 1) * (q - 1)   ) == 1:
               break

     print('Calculating d that is mod inverse of e...'   )
     lowercase_			:       List[str]									=			cryptoMath.find_mod_inverse(__SCREAMING_SNAKE_CASE  ,    (p - 1) * (q - 1)   )

     lowercase_			:       Any									=			(n, e)
     lowercase_			:       int									=			(n, d)
     return (public_key, private_key)



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     if os.path.exists(F'''{name}_pubkey.txt'''   ) or os.path.exists(F'''{name}_privkey.txt'''   ):
          print('\nWARNING:'   )
          print(
              F'''"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'''
              'Use a different name or delete these files and re-run this program.'   )
          sys.exit()

     lowercase_							,						lowercase_			:       int									=			generate_key(__SCREAMING_SNAKE_CASE   )
     print(F'''\nWriting public key to file {name}_pubkey.txt...'''   )
     with open(F'''{name}_pubkey.txt'''  ,    'w'   ) as out_file:
          out_file.write(F'''{key_size},{public_key[0]},{public_key[1]}'''   )

     print(F'''Writing private key to file {name}_privkey.txt...'''   )
     with open(F'''{name}_privkey.txt'''  ,    'w'   ) as out_file:
          out_file.write(F'''{key_size},{private_key[0]},{private_key[1]}'''   )


if __name__ == "__main__":
       main()

