




'''simple docstring'''





import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
  from .testing_utils import (
      floats_tensor,
      load_hf_numpy,
      load_image,
      load_numpy,
      load_pt,
      nightly,
      parse_flag_from_env,
      print_tensor_test,
      require_torch_a,
      require_torch_gpu,
      skip_mps,
      slow,
      torch_all_close,
      torch_device,
  )
  from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


a		:	Union[str, Any]       =      get_logger(__name__)





def UpperCAmelCase    (       UpperCAmelCase_						):
    if version.parse(UpperCAmelCase_						) < version.parse(UpperCAmelCase_						):
        if "dev" in min_version:
            _a				:							int							       =      (
                """This example requires a source install from HuggingFace diffusers (see """
                """`https://huggingface.co/docs/diffusers/installation#install-from-source`),"""
            )
        else:
            _a				:							str							       =      F'''This example requires a minimum version of {min_version},'''
        error_message += F''' but the version found is {__version__}.\n'''
        raise ImportError(UpperCAmelCase_						)








'''simple docstring'''





from __future__ import annotations

import random
import string




class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase = None ):







      '''simple docstring'''


      _a				:							List[Any]							       =      passcode or self.__passcode_creator()
      _a				:							List[str]							       =      self.__make_key_list()
      _a				:							Optional[int]							       =      self.__make_shift_key()



  def __str__(			self ):







      '''simple docstring'''


      return "".join(self.__passcode )



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      for i in range(1		,     len(__lowerCAmelCase )		,     2 ):
          iterlist[i] *= -1
      return iterlist



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      string.ascii_letters + string.digits
      _a				:							str							       =      [random.choice(__lowerCAmelCase ) for _ in range(random.randint(10		,     20 ) )]
      return password



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Dict							       =      (
          string.ascii_letters + string.digits + string.punctuation + """ \t\n"""
      )

      _a				:							str							       =      []

      # creates points known as breakpoints to break the key_list_options at those
      # points and pivot each substring
      _a				:							Dict							       =      sorted(set(self.__passcode ) )
      _a				:							list[str]							       =      []

      # algorithm for creating a new shuffled list, keys_l, out of key_list_options
      for i in key_list_options:
          temp_list.extend(__lowerCAmelCase )

          # checking breakpoints at which to pivot temporary sublist and add it into
          # keys_l
          if i in breakpoints or i == key_list_options[-1]:
              keys_l.extend(temp_list[::-1] )
              temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
      return keys_l



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      sum(self.__neg_pos([ord(__lowerCAmelCase ) for x in self.__passcode] ) )
      return num if num > 0 else len(self.__passcode )



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Tuple							       =      """"""

      # decoding shift like Caesar cipher algorithm implementing negative shift or
      # reverse shift or left shift
      for i in encoded_message:
          _a				:							List[str]							       =      self.__key_list.index(__lowerCAmelCase )
          decoded_message += self.__key_list[
              (position - self.__shift_key) % -len(self.__key_list )
          ]

      return decoded_message



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							List[str]							       =      """"""

      # encoding shift like Caesar cipher algorithm implementing positive shift or
      # forward shift or right shift
      for i in plaintext:
          _a				:							Any							       =      self.__key_list.index(__lowerCAmelCase )
          encoded_message += self.__key_list[
              (position + self.__shift_key) % len(self.__key_list )
          ]

      return encoded_message






def UpperCAmelCase    (       UpperCAmelCase_ = "Hello, this is a modified Caesar cipher"						):
    _a				:							Union[str, Any]							       =      ShuffledShiftCipher()
    return cipa.decrypt(cipa.encrypt(UpperCAmelCase_						)						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()


