




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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class   UpperCAmelCase    (      A ):
   __SCREAMING_SNAKE_CASE		:				torch.FloatTensor
   __SCREAMING_SNAKE_CASE		:				torch.FloatTensor


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase=0.999       ,  UpperCamelCase="cosine"       ,  )      ->		Any:





  """simple docstring"""





  if alpha_transform_type == "cosine":

    def alpha_bar_fn(UpperCamelCase				):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(UpperCamelCase				):
      return math.exp(t * -12.0				)

  else:
    raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'				)

  _lowercase:			Dict      =     []
  for i in range(UpperCamelCase				):
    _lowercase:			Optional[int]      =     i / num_diffusion_timesteps
    _lowercase:			Tuple      =     (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(UpperCamelCase				) / alpha_bar_fn(UpperCamelCase				)       ,  UpperCamelCase				)				)
  return torch.tensor(UpperCamelCase       ,  dtype=torch.floataa				)





class   UpperCAmelCase    (      A ,       A ):
   __SCREAMING_SNAKE_CASE		:				Dict      					=       1






   @register_to_config
   def __init__(						self :							List[Any]    ,       UpperCamelCase_ :							int = 10_00    ,       UpperCamelCase_ :							float = 0.0_001    ,       UpperCamelCase_ :							float = 0.02    ,       UpperCamelCase_ :							str = "linear"    ,       UpperCamelCase_ :							float = 0.0    ,       UpperCamelCase_ :							Optional[np.ndarray] = None    ,       UpperCamelCase_ :							bool = True    ,       )->      List[str]:
     if trained_betas is not None:
       _lowercase:			Optional[Any]      =     torch.from_numpy(UpperCamelCase_  )
     elif beta_schedule == "linear":
       _lowercase:			Dict      =     torch.linspace(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       dtype=torch.floataa  )
     elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       _lowercase:			Any      =     (
           torch.linspace(beta_start**0.5    ,       beta_end**0.5    ,       UpperCamelCase_    ,       dtype=torch.floataa  ) ** 2
       )
     elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       _lowercase:			str      =     betas_for_alpha_bar(UpperCamelCase_  )
     elif beta_schedule == "sigmoid":
       # GeoDiff sigmoid schedule
       _lowercase:			List[Any]      =     torch.linspace(-6    ,       6    ,       UpperCamelCase_  )
       _lowercase:			Union[str, Any]      =     torch.sigmoid(UpperCamelCase_  ) * (beta_end - beta_start) + beta_start
     else:
       raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'  )

     _lowercase:			str      =     1.0 - self.betas
     _lowercase:			Optional[Any]      =     torch.cumprod(self.alphas    ,       dim=0  )
     _lowercase:			List[str]      =     torch.tensor(1.0  )

     _lowercase:			Tuple      =     torch.tensor(1.0  )

     # standard deviation of the initial noise distribution
     _lowercase:			Optional[Any]      =     1.0

     # setable values
     _lowercase:			Dict      =     None
     _lowercase:			Optional[Any]      =     torch.from_numpy(np.arange(0    ,       UpperCamelCase_  )[::-1].copy()  )

     _lowercase:			List[Any]      =     eta






   def 			_a    (						self :							int    ,       UpperCamelCase_ :							torch.FloatTensor    ,       UpperCamelCase_ :							Optional[int] = None  )->      torch.FloatTensor:
     return sample






   def 			_a    (						self :							List[str]    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							int = 10    ,       UpperCamelCase_ :							int = 10    ,       UpperCamelCase_ :							Union[str, torch.device] = None    ,       )->      Dict:
     _lowercase:			int      =     min(self.config.num_train_timesteps    ,       UpperCamelCase_  )
     _lowercase:			List[Any]      =     num_inference_steps

     _lowercase:			List[Any]      =     []

     _lowercase:			Optional[Any]      =     {}
     for j in range(0    ,       num_inference_steps - jump_length    ,       UpperCamelCase_  ):
       _lowercase:			Any      =     jump_n_sample - 1

     _lowercase:			int      =     num_inference_steps
     while t >= 1:
       _lowercase:			Union[str, Any]      =     t - 1
       timesteps.append(UpperCamelCase_  )

       if jumps.get(UpperCamelCase_    ,       0  ) > 0:
         _lowercase:			Tuple      =     jumps[t] - 1
         for _ in range(UpperCamelCase_  ):
           _lowercase:			List[str]      =     t + 1
           timesteps.append(UpperCamelCase_  )

     _lowercase:			Optional[Any]      =     np.array(UpperCamelCase_  ) * (self.config.num_train_timesteps // self.num_inference_steps)
     _lowercase:			str      =     torch.from_numpy(UpperCamelCase_  ).to(UpperCamelCase_  )






   def 			_a    (						self :							Dict    ,       UpperCamelCase_ :							Dict  )->      List[Any]:
     _lowercase:			int      =     t - self.config.num_train_timesteps // self.num_inference_steps

     _lowercase:			Union[str, Any]      =     self.alphas_cumprod[t]
     _lowercase:			int      =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
     _lowercase:			Dict      =     1 - alpha_prod_t
     _lowercase:			Any      =     1 - alpha_prod_t_prev

     # For t > 0, compute predicted variance βt (see formula (6) and (7) from
     # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
     # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
     # variance to pred_sample
     # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
     # without eta.
     # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
     _lowercase:			Dict      =     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

     return variance






   def 			_a    (						self :							Any    ,       UpperCamelCase_ :							torch.FloatTensor    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							torch.FloatTensor    ,       UpperCamelCase_ :							torch.FloatTensor    ,       UpperCamelCase_ :							torch.FloatTensor    ,       UpperCamelCase_ :							Optional[torch.Generator] = None    ,       UpperCamelCase_ :							bool = True    ,       )->      Union[RePaintSchedulerOutput, Tuple]:
     _lowercase:			Union[str, Any]      =     timestep
     _lowercase:			List[Any]      =     timestep - self.config.num_train_timesteps // self.num_inference_steps

     # 1. compute alphas, betas
     _lowercase:			List[str]      =     self.alphas_cumprod[t]
     _lowercase:			List[Any]      =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
     _lowercase:			Dict      =     1 - alpha_prod_t

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     _lowercase:			Optional[Any]      =     (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

     # 3. Clip "predicted x_0"
     if self.config.clip_sample:
       _lowercase:			Dict      =     torch.clamp(UpperCamelCase_    ,       -1    ,       1  )

     # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
     # substitute formula (7) in the algorithm coming from DDPM paper
     # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
     # DDIM schedule gives the same results as DDPM with eta = 1.0
     # Noise is being reused in 7. and 8., but no impact on quality has
     # been observed.

     # 5. Add noise
     _lowercase:			Dict      =     model_output.device
     _lowercase:			List[Any]      =     randn_tensor(model_output.shape    ,       generator=UpperCamelCase_    ,       device=UpperCamelCase_    ,       dtype=model_output.dtype  )
     _lowercase:			Optional[Any]      =     self.eta * self._get_variance(UpperCamelCase_  ) ** 0.5

     _lowercase:			Tuple      =     0
     if t > 0 and self.eta > 0:
       _lowercase:			List[Any]      =     std_dev_t * noise

     # 6. compute "direction pointing to x_t" of formula (12)
     # from https://arxiv.org/pdf/2010.02502.pdf
     _lowercase:			Tuple      =     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

     # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     _lowercase:			str      =     alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

     # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
     _lowercase:			str      =     (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

     # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
     _lowercase:			int      =     mask * prev_known_part + (1.0 - mask) * prev_unknown_part

     if not return_dict:
       return (
           pred_prev_sample,
           pred_original_sample,
       )

     return RePaintSchedulerOutput(prev_sample=UpperCamelCase_    ,       pred_original_sample=UpperCamelCase_  )






   def 			_a    (						self :							Union[str, Any]    ,       UpperCamelCase_ :							List[str]    ,       UpperCamelCase_ :							List[str]    ,       UpperCamelCase_ :							Tuple=None  )->      Tuple:
     _lowercase:			Union[str, Any]      =     self.config.num_train_timesteps // self.num_inference_steps

     for i in range(UpperCamelCase_  ):
       _lowercase:			str      =     self.betas[timestep + i]
       if sample.device.type == "mps":
         # randn does not work reproducibly on mps
         _lowercase:			Any      =     randn_tensor(sample.shape    ,       dtype=sample.dtype    ,       generator=UpperCamelCase_  )
         _lowercase:			Tuple      =     noise.to(sample.device  )
       else:
         _lowercase:			List[Any]      =     randn_tensor(sample.shape    ,       generator=UpperCamelCase_    ,       device=sample.device    ,       dtype=sample.dtype  )

       # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
       _lowercase:			Any      =     (1 - beta) ** 0.5 * sample + beta**0.5 * noise

     return sample






   def 			_a    (						self :							List[Any]    ,       UpperCamelCase_ :							torch.FloatTensor    ,       UpperCamelCase_ :							torch.FloatTensor    ,       UpperCamelCase_ :							torch.IntTensor    ,       )->      torch.FloatTensor:
     raise NotImplementedError('Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'  )






   def __len__(						self :							Tuple  )->      Union[str, Any]:
     return self.config.num_train_timesteps






import os
import sys

from . import rsa_key_generator as rkg

UpperCAmelCase__    :							str       		=     128
UpperCAmelCase__    :							int       		=     256


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE				)      ->		list[int]:





  """simple docstring"""





  _lowercase:			List[str]      =     message.encode('ascii'				)

  _lowercase:			str      =     []
  for block_start in range(0       ,  len(UpperCamelCase				)       ,  UpperCamelCase				):
    _lowercase:			Optional[int]      =     0
    for i in range(UpperCamelCase       ,  min(block_start + block_size       ,  len(UpperCamelCase				)				)				):
      block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
    block_ints.append(UpperCamelCase				)
  return block_ints


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE				)      ->		str:





  """simple docstring"""





  _lowercase:			list[str]      =     []
  for block_int in block_ints:
    _lowercase:			list[str]      =     []
    for i in range(block_size - 1       ,  -1       ,  -1				):
      if len(UpperCamelCase				) + i < message_length:
        _lowercase:			Optional[Any]      =     block_int // (BYTE_SIZE**i)
        _lowercase:			Dict      =     block_int % (BYTE_SIZE**i)
        block_message.insert(0       ,  chr(UpperCamelCase				)				)
    message.extend(UpperCamelCase				)
  return "".join(UpperCamelCase				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE				)      ->		list[int]:





  """simple docstring"""





  _lowercase:			Optional[int]      =     []
  _lowercase  ,       _lowercase:			str      =     key

  for block in get_blocks_from_text(UpperCamelCase       ,  UpperCamelCase				):
    encrypted_blocks.append(pow(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)				)
  return encrypted_blocks


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE       ,  )      ->		str:





  """simple docstring"""





  _lowercase:			Optional[int]      =     []
  _lowercase  ,       _lowercase:			str      =     key
  for block in encrypted_blocks:
    decrypted_blocks.append(pow(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)				)
  return get_text_from_blocks(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		tuple[int, int, int]:





  """simple docstring"""





  with open(UpperCamelCase				) as fo:
    _lowercase:			Any      =     fo.read()
  _lowercase  ,       _lowercase  ,       _lowercase:			Optional[Any]      =     content.split(','				)
  return (int(UpperCamelCase				), int(UpperCamelCase				), int(UpperCamelCase				))


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE       ,  )      ->		str:





  """simple docstring"""





  _lowercase  ,       _lowercase  ,       _lowercase:			int      =     read_key_file(UpperCamelCase				)
  if key_size < block_size * 8:
    sys.exit(
        'ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '
        'requires the block size to be equal to or greater than the key size. '
        'Either decrease the block size or use different keys.'.format(
            block_size * 8       ,  UpperCamelCase				)				)

  _lowercase:			int      =     [str(UpperCamelCase				) for i in encrypt_message(UpperCamelCase       ,  (n, e)       ,  UpperCamelCase				)]

  _lowercase:			List[Any]      =     ','.join(UpperCamelCase				)
  _lowercase:			Tuple      =     F'{len(UpperCamelCase				)}_{block_size}_{encrypted_content}'
  with open(UpperCamelCase       ,  'w'				) as fo:
    fo.write(UpperCamelCase				)
  return encrypted_content


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  _lowercase  ,       _lowercase  ,       _lowercase:			Tuple      =     read_key_file(UpperCamelCase				)
  with open(UpperCamelCase				) as fo:
    _lowercase:			Optional[Any]      =     fo.read()
  _lowercase  ,       _lowercase  ,       _lowercase:			Union[str, Any]      =     content.split('_'				)
  _lowercase:			List[Any]      =     int(UpperCamelCase				)
  _lowercase:			Dict      =     int(UpperCamelCase				)

  if key_size < block_size * 8:
    sys.exit(
        'ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '
        'requires the block size to be equal to or greater than the key size. '
        'Did you specify the correct key file and encrypted file?'.format(
            block_size * 8       ,  UpperCamelCase				)				)

  _lowercase:			Union[str, Any]      =     []
  for block in encrypted_message.split(','				):
    encrypted_blocks.append(int(UpperCamelCase				)				)

  return decrypt_message(UpperCamelCase       ,  UpperCamelCase       ,  (n, d)       ,  UpperCamelCase				)


def 		_lowerCamelCase					(       )      ->		None:





  """simple docstring"""





  _lowercase:			Tuple      =     'encrypted_file.txt'
  _lowercase:			Optional[Any]      =     input(r'Encrypt\Decrypt [e\d]: '				)

  if response.lower().startswith('e'				):
    _lowercase:			Optional[Any]      =     'encrypt'
  elif response.lower().startswith('d'				):
    _lowercase:			Union[str, Any]      =     'decrypt'

  if mode == "encrypt":
    if not os.path.exists('rsa_pubkey.txt'				):
      rkg.make_key_files('rsa'       ,  1_024				)

    _lowercase:			Union[str, Any]      =     input('\nEnter message: '				)
    _lowercase:			Tuple      =     'rsa_pubkey.txt'
    print(F'Encrypting and writing to {filename}...'				)
    _lowercase:			Optional[Any]      =     encrypt_and_write_to_file(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)

    print('\nEncrypted text:'				)
    print(UpperCamelCase				)

  elif mode == "decrypt":
    _lowercase:			Optional[Any]      =     'rsa_privkey.txt'
    print(F'Reading from {filename} and decrypting...'				)
    _lowercase:			int      =     read_from_file_and_decrypt(UpperCamelCase       ,  UpperCamelCase				)
    print('writing decryption to rsa_decryption.txt...'				)
    with open('rsa_decryption.txt'       ,  'w'				) as dec:
      dec.write(UpperCamelCase				)

    print('\nDecryption:'				)
    print(UpperCamelCase				)


if __name__ == "__main__":
   main()
