'''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       lowerCAmelCase_							(   )       -> None:


     '''simple docstring'''




     print("Making key files..."     )
     make_key_files("rsa"   ,       10_24     )
     print("Key files generation successful."     )





def       lowerCAmelCase_							(   snake_case_			:					int     )       -> tuple[tuple[int, int], tuple[int, int]]:


     '''simple docstring'''




     print("Generating prime p..."     )
     UpperCAmelCase_			       =  rabinMiller.generate_large_prime(snake_case_     )
     print("Generating prime q..."     )
     UpperCAmelCase_			       =  rabinMiller.generate_large_prime(snake_case_     )
     UpperCAmelCase_			       =  p * q

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

     print("Calculating d that is mod inverse of e..."     )
     UpperCAmelCase_			       =  cryptoMath.find_mod_inverse(snake_case_   ,       (p - 1) * (q - 1)     )

     UpperCAmelCase_			       =  (n, e)
     UpperCAmelCase_			       =  (n, d)
     return (public_key, private_key)





def       lowerCAmelCase_							(   snake_case_			:					str   ,       snake_case_			:					int     )       -> None:


     '''simple docstring'''




     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()

     UpperCAmelCase_       ,					UpperCAmelCase_			       =  generate_key(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()






'''simple docstring'''
from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						42







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		@register_to_config
		def __init__(      self				:			List[str]  ,	UpperCamelCase__				:			int = 16  ,	UpperCamelCase__				:			int = 88  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			int = 1  ,	UpperCamelCase__				:			float = 0.0  ,	UpperCamelCase__				:			int = 32  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			str = "geglu"  ,	UpperCamelCase__				:			bool = True  ,	UpperCamelCase__				:			bool = True  ,	)						->			List[Any]:






				"""simple docstring"""







				super().__init__()
				snake_case  :       Optional[int]     			=						num_attention_heads
				snake_case  :       int     			=						attention_head_dim
				snake_case  :       Dict     			=						num_attention_heads * attention_head_dim

				snake_case  :       Tuple     			=						in_channels

				snake_case  :       Optional[int]     			=						torch.nn.GroupNorm(num_groups=UpperCamelCase__  ,	num_channels=UpperCamelCase__  ,	eps=1e-6  ,	affine=UpperCamelCase__      )
				snake_case  :       List[Any]     			=						nn.Linear(UpperCamelCase__  ,	UpperCamelCase__      )

				# 3. Define transformers blocks
				snake_case  :       Union[str, Any]     			=						nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	dropout=UpperCamelCase__  ,	cross_attention_dim=UpperCamelCase__  ,	activation_fn=UpperCamelCase__  ,	attention_bias=UpperCamelCase__  ,	double_self_attention=UpperCamelCase__  ,	norm_elementwise_affine=UpperCamelCase__  ,	)
				        for d in range(UpperCamelCase__      )
				    ]      )

				snake_case  :       Any     			=						nn.Linear(UpperCamelCase__  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			Any=None  ,	UpperCamelCase__				:			Dict=None  ,	UpperCamelCase__				:			Dict=None  ,	UpperCamelCase__				:			Union[str, Any]=1  ,	UpperCamelCase__				:			Optional[int]=None  ,	UpperCamelCase__				:			bool = True  ,	)						->			Any:






				"""simple docstring"""







				snake_case			,snake_case			,snake_case			,snake_case  :       Tuple     			=						hidden_states.shape
				snake_case  :       List[Any]     			=						batch_frames // num_frames

				snake_case  :       Union[str, Any]     			=						hidden_states

				snake_case  :       Optional[Any]     			=						hidden_states[None, :].reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				snake_case  :       int     			=						hidden_states.permute(0  ,	2  ,	1  ,	3  ,	4      )

				snake_case  :       Tuple     			=						self.norm(UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						hidden_states.permute(0  ,	3  ,	4  ,	2  ,	1      ).reshape(batch_size * height * width  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case  :       Optional[Any]     			=						self.proj_in(UpperCamelCase__      )

				# 2. Blocks
				for block in self.transformer_blocks:
						snake_case  :       Union[str, Any]     			=						block(
						    UpperCamelCase__  ,	encoder_hidden_states=UpperCamelCase__  ,	timestep=UpperCamelCase__  ,	cross_attention_kwargs=UpperCamelCase__  ,	class_labels=UpperCamelCase__  ,	)

				# 3. Output
				snake_case  :       Dict     			=						self.proj_out(UpperCamelCase__      )
				snake_case  :       Tuple     			=						(
				    hidden_states[None, None, :]
				    .reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				    .permute(0  ,	3  ,	4  ,	1  ,	2      )
				    .contiguous()
				)
				snake_case  :       List[Any]     			=						hidden_states.reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case  :       Any     			=						hidden_states + residual

				if not return_dict:
						return (output,)

				return TransformerTemporalModelOutput(sample=UpperCamelCase__      )


