"""simple docstring"""
import glob
import os
from typing import Dict, List, Union

import torch

from diffusers.utils import is_safetensors_available


if is_safetensors_available():
 import safetensors.torch

from huggingface_hub import snapshot_download

from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME




class   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self)      ->     List[Any]:





        '''simple docstring'''





        self.register_to_config()
        super().__init__()



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        if dicta == dicta:
          return True
        else:
          a__				,       a__:	List[str]     				=      self._remove_meta_keys(lowercase)
          a__				,       a__:	List[str]     				=      self._remove_meta_keys(lowercase)
          if configa == configa:
            print(f'Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.')
            return True
        return False



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Optional[Any]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      []
        a__:	Optional[int]     				=      config_dict.copy()
        for key in config_dict.keys():
          if key.startswith('_'):
            temp_dict.pop(lowercase)
            meta_keys.append(lowercase)
        return (temp_dict, meta_keys)



      @torch.no_grad()
      def     lowerCamelCase_  (		self       ,     lowercase       ,     **lowercase)      ->     int:





        '''simple docstring'''





        a__:	List[Any]     				=      kwargs.pop('cache_dir'       ,     lowercase)
        a__:	Union[str, Any]     				=      kwargs.pop('resume_download'       ,     lowercase)
        a__:	Any     				=      kwargs.pop('force_download'       ,     lowercase)
        a__:	str     				=      kwargs.pop('proxies'       ,     lowercase)
        a__:	Any     				=      kwargs.pop('local_files_only'       ,     lowercase)
        a__:	int     				=      kwargs.pop('use_auth_token'       ,     lowercase)
        a__:	Any     				=      kwargs.pop('revision'       ,     lowercase)
        a__:	Tuple     				=      kwargs.pop('torch_dtype'       ,     lowercase)
        a__:	Any     				=      kwargs.pop('device_map'       ,     lowercase)

        a__:	Dict     				=      kwargs.pop('alpha'       ,     0.5)
        a__:	Optional[int]     				=      kwargs.pop('interp'       ,     lowercase)

        print('Received list'       ,     lowercase)
        print(f'Combining with alpha={alpha}, interpolation mode={interp}')

        a__:	Optional[Any]     				=      len(lowercase)
        # Ignore result from model_index_json comparision of the two checkpoints
        a__:	Tuple     				=      kwargs.pop('force'       ,     lowercase)

        # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
        if checkpoint_count > 3 or checkpoint_count < 2:
          raise ValueError(
              'Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being'
              ' passed.')

        print('Received the right number of checkpoints')
        # chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
        # chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None

        # Validate that the checkpoints can be merged
        # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
        a__:	Optional[Any]     				=      []
        for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
          a__:	List[str]     				=      DiffusionPipeline.load_config(
              lowercase       ,     cache_dir=lowercase       ,     resume_download=lowercase       ,     force_download=lowercase       ,     proxies=lowercase       ,     local_files_only=lowercase       ,     use_auth_token=lowercase       ,     revision=lowercase       ,     )
          config_dicts.append(lowercase)

        a__:	Optional[int]     				=      True
        for idx in range(1       ,     len(lowercase)):
          comparison_result &= self._compare_model_configs(config_dicts[idx - 1]       ,     config_dicts[idx])
          if not force and comparison_result is False:
            raise ValueError('Incompatible checkpoints. Please check model_index.json for the models.')
            print(config_dicts[0]       ,     config_dicts[1])
        print('Compatible model_index.json files found')
        # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
        a__:	List[str]     				=      []
        for pretrained_model_name_or_path, config_dict in zip(lowercase       ,     lowercase):
          a__:	Tuple     				=      [k for k in config_dict.keys() if not k.startswith('_')]
          a__:	int     				=      [os.path.join(lowercase       ,     '*') for k in folder_names]
          allow_patterns += [
              WEIGHTS_NAME,
              SCHEDULER_CONFIG_NAME,
              CONFIG_NAME,
              ONNX_WEIGHTS_NAME,
              DiffusionPipeline.config_name,
          ]
          a__:	Tuple     				=      config_dict.get('_class_name')
          a__:	List[Any]     				=      {'diffusers': __version__, 'pipeline_class': requested_pipeline_class}

          a__:	Optional[Any]     				=      (
              pretrained_model_name_or_path
              if os.path.isdir(lowercase)
              else snapshot_download(
                  lowercase       ,     cache_dir=lowercase       ,     resume_download=lowercase       ,     proxies=lowercase       ,     local_files_only=lowercase       ,     use_auth_token=lowercase       ,     revision=lowercase       ,     allow_patterns=lowercase       ,     user_agent=lowercase       ,     )
          )
          print('Cached Folder'       ,     lowercase)
          cached_folders.append(lowercase)

        # Step 3:-
        # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
        a__:	Tuple     				=      DiffusionPipeline.from_pretrained(
            cached_folders[0]       ,     torch_dtype=lowercase       ,     device_map=lowercase)
        final_pipe.to(self.device)

        a__:	Tuple     				=      None
        if len(lowercase) > 2:
          a__:	Optional[int]     				=      os.path.join(cached_folders[2])

        if interp == "sigmoid":
          a__:	Optional[int]     				=      CheckpointMergerPipeline.sigmoid
        elif interp == "inv_sigmoid":
          a__:	Optional[int]     				=      CheckpointMergerPipeline.inv_sigmoid
        elif interp == "add_diff":
          a__:	int     				=      CheckpointMergerPipeline.add_difference
        else:
          a__:	Dict     				=      CheckpointMergerPipeline.weighted_sum

        # Find each module's state dict.
        for attr in final_pipe.config.keys():
          if not attr.startswith('_'):
            a__:	List[str]     				=      os.path.join(cached_folders[1]       ,     lowercase)
            if os.path.exists(lowercase):
              a__:	int     				=      [
                  *glob.glob(os.path.join(lowercase       ,     '*.safetensors')),
                  *glob.glob(os.path.join(lowercase       ,     '*.bin')),
              ]
              a__:	int     				=      files[0] if len(lowercase) > 0 else None
            if len(lowercase) < 3:
              a__:	Union[str, Any]     				=      None
            else:
              a__:	List[str]     				=      os.path.join(cached_folders[2]       ,     lowercase)
              if os.path.exists(lowercase):
                a__:	int     				=      [
                    *glob.glob(os.path.join(lowercase       ,     '*.safetensors')),
                    *glob.glob(os.path.join(lowercase       ,     '*.bin')),
                ]
                a__:	Optional[Any]     				=      files[0] if len(lowercase) > 0 else None
                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
            if checkpoint_path_a is None and checkpoint_path_a is None:
              print(f'Skipping {attr}: not present in 2nd or 3d model')
              continue
            try:
              a__:	Optional[int]     				=      getattr(lowercase       ,     lowercase)
              if isinstance(lowercase       ,     lowercase):  # ignore requires_safety_checker boolean
                continue
              a__:	List[str]     				=      getattr(lowercase       ,     'state_dict')
              a__:	Optional[Any]     				=      theta_a()

              a__:	int     				=      getattr(lowercase       ,     'load_state_dict')
              a__:	str     				=      (
                  safetensors.torch.load_file(lowercase)
                  if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'))
                  else torch.load(lowercase       ,     map_location='cpu')
              )
              a__:	str     				=      None
              if checkpoint_path_a:
                a__:	Optional[Any]     				=      (
                    safetensors.torch.load_file(lowercase)
                    if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'))
                    else torch.load(lowercase       ,     map_location='cpu')
                )

              if not theta_a.keys() == theta_a.keys():
                print(f'Skipping {attr}: key mismatch')
                continue
              if theta_a and not theta_a.keys() == theta_a.keys():
                print(f'Skipping {attr}:y mismatch')
            except Exception as e:
              print(f'Skipping {attr} do to an unexpected error: {str(lowercase)}')
              continue
            print(f'MERGING {attr}')

            for key in theta_a.keys():
              if theta_a:
                a__:	Optional[int]     				=      theta_func(theta_a[key]       ,     theta_a[key]       ,     theta_a[key]       ,     lowercase)
              else:
                a__:	List[Any]     				=      theta_func(theta_a[key]       ,     theta_a[key]       ,     lowercase       ,     lowercase)

            del theta_a
            del theta_a
            update_theta_a(lowercase)

            del theta_a
        return final_pipe



      @staticmethod
      def     lowerCamelCase_  (		lowercase       ,     lowercase       ,     lowercase       ,     lowercase)      ->     str:





        '''simple docstring'''





        return ((1 - alpha) * thetaa) + (alpha * thetaa)



      @staticmethod
      def     lowerCamelCase_  (		lowercase       ,     lowercase       ,     lowercase       ,     lowercase)      ->     str:





        '''simple docstring'''





        a__:	Any     				=      alpha * alpha * (3 - (2 * alpha))
        return thetaa + ((thetaa - thetaa) * alpha)



      @staticmethod
      def     lowerCamelCase_  (		lowercase       ,     lowercase       ,     lowercase       ,     lowercase)      ->     Optional[Any]:





        '''simple docstring'''





        import math

        a__:	Any     				=      0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
        return thetaa + ((thetaa - thetaa) * alpha)



      @staticmethod
      def     lowerCamelCase_  (		lowercase       ,     lowercase       ,     lowercase       ,     lowercase)      ->     List[Any]:





        '''simple docstring'''





        return thetaa + (thetaa - thetaa) * (1.0 - alpha)



"""simple docstring"""
import random
import sys

from . import cryptomath_module as cryptomath

lowercase__   			=      (
    r' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`'
    r'abcdefghijklmnopqrstuvwxyz{|}~'
)







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->None:
  if mode == "encrypt":
    if key_a == 1:
      sys.exit(
          'The affine cipher becomes weak when key '
          'A is set to 1. Choose different key'  )
    if key_b == 0:
      sys.exit(
          'The affine cipher becomes weak when key '
          'B is set to 0. Choose different key'  )
  if key_a < 0 or key_b < 0 or key_b > len(_SCREAMING_SNAKE_CASE  ) - 1:
    sys.exit(
        'Key A must be greater than 0 and key B must '
        F'be between 0 and {len(_SCREAMING_SNAKE_CASE  ) - 1}.'  )
  if cryptomath.gcd(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  ) != 1:
    sys.exit(
        F'Key A {key_a} and the symbol set size {len(_SCREAMING_SNAKE_CASE  )} '
        'are not relatively prime. Choose a different key.'  )







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->str:
  a__				,       a__:	Optional[Any]     				=      divmod(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  )
  check_keys(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	'encrypt'  )
  a__:	Dict     				=      ''
  for symbol in message:
    if symbol in SYMBOLS:
      a__:	int     				=      SYMBOLS.find(_SCREAMING_SNAKE_CASE  )
      cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(_SCREAMING_SNAKE_CASE  )]
    else:
      cipher_text += symbol
  return cipher_text







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->str:
  a__				,       a__:	List[str]     				=      divmod(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  )
  check_keys(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	'decrypt'  )
  a__:	List[Any]     				=      ''
  a__:	str     				=      cryptomath.find_mod_inverse(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  )
  for symbol in message:
    if symbol in SYMBOLS:
      a__:	List[str]     				=      SYMBOLS.find(_SCREAMING_SNAKE_CASE  )
      plain_text += SYMBOLS[
          (sym_index - key_b) * mod_inverse_of_key_a % len(_SCREAMING_SNAKE_CASE  )
      ]
    else:
      plain_text += symbol
  return plain_text







def 		__a  (      )      ->int:
  while True:
    a__:	int     				=      random.randint(2						,	len(_SCREAMING_SNAKE_CASE  )  )
    a__:	List[Any]     				=      random.randint(2						,	len(_SCREAMING_SNAKE_CASE  )  )
    if cryptomath.gcd(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  ) == 1 and key_b % len(_SCREAMING_SNAKE_CASE  ) != 0:
      return key_b * len(_SCREAMING_SNAKE_CASE  ) + key_b







def 		__a  (      )      ->None:
  a__:	Union[str, Any]     				=      input('Enter message: '  ).strip()
  a__:	List[Any]     				=      int(input('Enter key [2000 - 9000]: '  ).strip()  )
  a__:	List[Any]     				=      input('Encrypt/Decrypt [E/D]: '  ).strip().lower()

  if mode.startswith('e'  ):
    a__:	int     				=      'encrypt'
    a__:	List[str]     				=      encrypt_message(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  elif mode.startswith('d'  ):
    a__:	Dict     				=      'decrypt'
    a__:	Any     				=      decrypt_message(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  print(F'\n{mode.title()}ed text: \n{translated}'  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # main()


