
'''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__ :		Any					=get_logger(__name__)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Any			)					->							Any:





  """simple docstring"""

  if version.parse(__SCREAMING_SNAKE_CASE			) < version.parse(__SCREAMING_SNAKE_CASE			):
    if "dev" in min_version:
      __A		:  List[str]       				=		(
          """This example requires a source install from HuggingFace diffusers (see """
          """`https://huggingface.co/docs/diffusers/installation#install-from-source`),"""
      )
    else:
      __A		:  Union[str, Any]       				=		F"This example requires a minimum version of {min_version},"
    error_message += F" but the version found is {__version__}.\n"
    raise ImportError(__SCREAMING_SNAKE_CASE			)





'''simple docstring'''
A__ :		Optional[Any]					=[chr(i) for i in range(32, 126)]
A__ :		Tuple					=list(range(len(alphabets)))
A__ :		int					=list(range(len(alphabets)))
A__ :		Optional[Any]					=list(range(len(alphabets)))
A__ :		Any					=list(reversed(range(len(alphabets))))
A__ :		Optional[int]					=[]
A__ :		Tuple					=0







def 							A_  (    )					->							Any:





  """simple docstring"""

  global gear_one_pos
  global gear_two_pos
  global gear_three_pos
  __A		:  Any       				=		gear_one[0]
  gear_one.append(__SCREAMING_SNAKE_CASE			)
  del gear_one[0]
  gear_one_pos += 1
  if gear_one_pos % int(len(__SCREAMING_SNAKE_CASE			)			) == 0:
    __A		:  Any       				=		gear_two[0]
    gear_two.append(__SCREAMING_SNAKE_CASE			)
    del gear_two[0]
    gear_two_pos += 1
    if gear_two_pos % int(len(__SCREAMING_SNAKE_CASE			)			) == 0:
      __A		:  str       				=		gear_three[0]
      gear_three.append(__SCREAMING_SNAKE_CASE			)
      del gear_three[0]
      gear_three_pos += 1







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str			)					->							List[str]:





  """simple docstring"""

  __A		:  Union[str, Any]       				=		alphabets.index(__SCREAMING_SNAKE_CASE			)
  __A		:  Dict       				=		gear_one[target]
  __A		:  int       				=		gear_two[target]
  __A		:  List[str]       				=		gear_three[target]
  __A		:  List[Any]       				=		reflector[target]
  __A		:  List[Any]       				=		gear_three.index(__SCREAMING_SNAKE_CASE			)
  __A		:  Tuple       				=		gear_two.index(__SCREAMING_SNAKE_CASE			)
  __A		:  List[str]       				=		gear_one.index(__SCREAMING_SNAKE_CASE			)
  code.append(alphabets[target]			)
  rotator()


if __name__ == "__main__":
     A__ :		int					=list(input('Type your message:\n'))
     while True:
          try:
               A__ :		Any					=int(input('Please set token:(must be only digits)\n'))
               break
          except Exception as error:
               print(error)
     for _ in range(token):
          rotator()
     for j in decode:
          engine(j)
     print('\n' + ''.join(code))
     print(
         F"\nYour Token is {token} please write it down.\nIf you want to decode "
         'this message again you should input same digits as token!'
     )



