


import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


UpperCAmelCase_   :				Dict						      = get_logger(__name__)


UpperCAmelCase_   :				Any						      = Path(__file__).parent / """model_card_template.md"""
UpperCAmelCase_   :				int						      = uuida().hex
UpperCAmelCase_   :				List[str]						      = os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
UpperCAmelCase_   :				List[Any]						      = os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
UpperCAmelCase_   :				Tuple						      = HUGGINGFACE_CO_RESOLVE_ENDPOINT + """/api/telemetry/"""







def  SCREAMING_SNAKE_CASE_		(    __A				:    Any = None       )			->	str:

       """simple docstring"""

       a_      :	List[str]							=						F"""diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"""
       if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
              return ua + "; telemetry/off"
       if is_torch_available():
              ua += F"""; torch/{_torch_version}"""
       if is_flax_available():
              ua += F"""; jax/{_jax_version}"""
              ua += F"""; flax/{_flax_version}"""
       if is_onnx_available():
              ua += F"""; onnxruntime/{_onnxruntime_version}"""
       # CI will set this value to True
       if os.environ.get('DIFFUSERS_IS_CI'						,					''       ).upper() in ENV_VARS_TRUE_VALUES:
              ua += "; is_ci/true"
       if isinstance(__A						,					__A       ):
              ua += "; " + "; ".join(F"""{k}/{v}""" for k, v in user_agent.items()       )
       elif isinstance(__A						,					__A       ):
              ua += "; " + user_agent
       return ua







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    Optional[int] = None						,					__A				:    Tuple = None       )			->	Union[str, Any]:

       """simple docstring"""

       if token is None:
              a_      :	List[Any]							=						HfFolder.get_token()
       if organization is None:
              a_      :	int							=						whoami(__A       )['name']
              return F"""{username}/{model_id}"""
       else:
              return F"""{organization}/{model_id}"""







def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    List[str]       )			->	Any:

       """simple docstring"""

       if not is_jinja_available():
              raise ValueError(
                  'Modelcard rendering is based on Jinja templates.'
                  ' Please make sure to have `jinja` installed before using `create_model_card`.'
                  ' To install it, please run `pip install Jinja2`.'       )

       if hasattr(__A						,					'local_rank'       ) and args.local_rank not in [-1, 0]:
              return

       a_      :	Optional[int]							=						args.hub_token if hasattr(__A						,					'hub_token'       ) else None
       a_      :	str							=						get_full_repo_name(__A						,					token=__A       )

       a_      :	List[str]							=						ModelCard.from_template(
           card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
               language='en'						,					license='apache-2.0'						,					library_name='diffusers'						,					tags=[]						,					datasets=args.dataset_name						,					metrics=[]						,					)						,					template_path=__A						,					model_name=__A						,					repo_name=__A						,					dataset_name=args.dataset_name if hasattr(__A						,					'dataset_name'       ) else None						,					learning_rate=args.learning_rate						,					train_batch_size=args.train_batch_size						,					eval_batch_size=args.eval_batch_size						,					gradient_accumulation_steps=(
               args.gradient_accumulation_steps if hasattr(__A						,					'gradient_accumulation_steps'       ) else None
           )						,					adam_betaa=args.adam_betaa if hasattr(__A						,					'adam_beta1'       ) else None						,					adam_betaa=args.adam_betaa if hasattr(__A						,					'adam_beta2'       ) else None						,					adam_weight_decay=args.adam_weight_decay if hasattr(__A						,					'adam_weight_decay'       ) else None						,					adam_epsilon=args.adam_epsilon if hasattr(__A						,					'adam_epsilon'       ) else None						,					lr_scheduler=args.lr_scheduler if hasattr(__A						,					'lr_scheduler'       ) else None						,					lr_warmup_steps=args.lr_warmup_steps if hasattr(__A						,					'lr_warmup_steps'       ) else None						,					ema_inv_gamma=args.ema_inv_gamma if hasattr(__A						,					'ema_inv_gamma'       ) else None						,					ema_power=args.ema_power if hasattr(__A						,					'ema_power'       ) else None						,					ema_max_decay=args.ema_max_decay if hasattr(__A						,					'ema_max_decay'       ) else None						,					mixed_precision=args.mixed_precision						,					)

       a_      :	Optional[int]							=						os.path.join(args.output_dir						,					'README.md'       )
       model_card.save(__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]						,					__A				:    Dict = None       )			->	str:

       """simple docstring"""

       if resolved_file is None or commit_hash is not None:
              return commit_hash
       a_      :	int							=						str(Path(__A       ).as_posix()       )
       a_      :	Any							=						re.search(R'snapshots/([^/]+)/'						,					__A       )
       if search is None:
              return None
       a_      :	Dict							=						search.groups()[0]
       return commit_hash if REGEX_COMMIT_HASH.match(__A       ) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
UpperCAmelCase_   :				List[str]						      = os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
UpperCAmelCase_   :				Optional[Any]						      = os.path.join(hf_cache_home, 'diffusers')







def  SCREAMING_SNAKE_CASE_		(    __A				:    int = None						,					__A				:    Optional[Any] = None       )			->	None:

       """simple docstring"""

       if new_cache_dir is None:
              a_      :	Optional[Any]							=						DIFFUSERS_CACHE
       if old_cache_dir is None:
              a_      :	Tuple							=						old_diffusers_cache

       a_      :	Any							=						Path(__A       ).expanduser()
       a_      :	Any							=						Path(__A       ).expanduser()
       for old_blob_path in old_cache_dir.glob('**/blobs/*'       ):
              if old_blob_path.is_file() and not old_blob_path.is_symlink():
                     a_      :	Optional[Any]							=						new_cache_dir / old_blob_path.relative_to(__A       )
                     new_blob_path.parent.mkdir(parents=__A						,					exist_ok=__A       )
                     os.replace(__A						,					__A       )
                     try:
                            os.symlink(__A						,					__A       )
                     except OSError:
                            logger.warning(
                                'Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.'       )
       # At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


UpperCAmelCase_   :				str						      = os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
   UpperCAmelCase_   :				Any						      = 0
else:
   with open(cache_version_file) as f:
      try:
         UpperCAmelCase_   :				Optional[int]						      = int(f.read())
      except ValueError:
         UpperCAmelCase_   :				Optional[Any]						      = 0

if cache_version < 1:
   UpperCAmelCase_   :				Union[str, Any]						      = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
   if old_cache_is_not_empty:
      logger.warning(
          'The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '
          'existing cached models. This is a one-time operation, you can interrupt it or run it '
          'later by calling `diffusers.utils.hub_utils.move_cache()`.'
      )
      try:
         move_cache()
      except Exception as e:
         UpperCAmelCase_   :				Optional[Any]						      = """\n""".join(traceback.format_tb(e.__traceback__))
         logger.error(
             F'There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '
             'file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '
             'message and we will do our best to help.'
         )

if cache_version < 1:
   try:
      os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
      with open(cache_version_file, 'w') as f:
         f.write('1')
   except Exception:
      logger.warning(
          F'There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '
          'the directory exists and can be written to.'
      )







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    Any = None       )			->	str:

       """simple docstring"""

       if variant is not None:
              a_      :	Dict							=						weights_name.split('.'       )
              a_      :	int							=						splits[:-1] + [variant] + splits[-1:]
              a_      :	List[Any]							=						'.'.join(__A       )

       return weights_name







def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					*,
    __A				:    str						,					__A				:    Union[str, Any]						,					__A				:    str						,					__A				:    List[Any]						,					__A				:    List[str]						,					__A				:    Dict						,					__A				:    Optional[int]						,					__A				:    Any						,					__A				:    Tuple						,					__A				:    Any						,					__A				:    Tuple=None						,					)			->	Optional[Any]:

       """simple docstring"""

       a_      :	Optional[Any]							=						str(__A       )
       if os.path.isfile(__A       ):
              return pretrained_model_name_or_path
       elif os.path.isdir(__A       ):
              if os.path.isfile(os.path.join(__A						,					__A       )       ):
                     # Load from a PyTorch checkpoint
                     a_      :	Any							=						os.path.join(__A						,					__A       )
                     return model_file
              elif subfolder is not None and os.path.isfile(
                  os.path.join(__A						,					__A						,					__A       )       ):
                     a_      :	Any							=						os.path.join(__A						,					__A						,					__A       )
                     return model_file
              else:
                     raise EnvironmentError(
                         F"""Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."""       )
       else:
              # 1. First check if deprecated way of loading from branches is used
              if (
                  revision in DEPRECATED_REVISION_ARGS
                  and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
                  and version.parse(version.parse(__A       ).base_version       ) >= version.parse('0.20.0'       )
              ):
                     try:
                            a_      :	List[Any]							=						hf_hub_download(
                                __A						,					filename=_add_variant(__A						,					__A       )						,					cache_dir=__A						,					force_download=__A						,					proxies=__A						,					resume_download=__A						,					local_files_only=__A						,					use_auth_token=__A						,					user_agent=__A						,					subfolder=__A						,					revision=revision or commit_hash						,					)
                            warnings.warn(
                                F"""Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead."""						,					__A						,					)
                            return model_file
                     except:  # noqa: E722
                            warnings.warn(
                                F"""You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(__A						,					__A       )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(__A						,					__A       )}\' so that the correct variant file can be added."""						,					__A						,					)
              try:
                     # 2. Load model file as usual
                     a_      :	List[Any]							=						hf_hub_download(
                         __A						,					filename=__A						,					cache_dir=__A						,					force_download=__A						,					proxies=__A						,					resume_download=__A						,					local_files_only=__A						,					use_auth_token=__A						,					user_agent=__A						,					subfolder=__A						,					revision=revision or commit_hash						,					)
                     return model_file

              except RepositoryNotFoundError:
                     raise EnvironmentError(
                         F"""{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier """
                         'listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '
                         'token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '
                         'login`.'       )
              except RevisionNotFoundError:
                     raise EnvironmentError(
                         F"""{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for """
                         'this model name. Check the model page at '
                         F"""\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions."""       )
              except EntryNotFoundError:
                     raise EnvironmentError(
                         F"""{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."""       )
              except HTTPError as err:
                     raise EnvironmentError(
                         F"""There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"""       )
              except ValueError:
                     raise EnvironmentError(
                         F"""We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it"""
                         F""" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"""
                         F""" directory containing a file named {weights_name} or"""
                         ' \nCheckout your internet connection or see how to run the library in'
                         ' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.'       )
              except EnvironmentError:
                     raise EnvironmentError(
                         F"""Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from """
                         '\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '
                         F"""Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory """
                         F"""containing a file named {weights_name}"""       )






import random
class 				SCREAMING_SNAKE_CASE__						:
      @staticmethod
      def  SCREAMING_SNAKE_CASE							( SCREAMING_SNAKE_CASE__       : str  )	->    tuple[list[int], list[int]]:
             a_      :	int							=						[ord(SCREAMING_SNAKE_CASE__  ) for i in text]
             a_      :	Any							=						[]
             a_      :	Optional[int]							=						[]
             for i in plain:
                    a_      :	Tuple							=						random.randint(1  ,							3_0_0  )
                    a_      :	Optional[int]							=						(i + k) * k
                    cipher.append(SCREAMING_SNAKE_CASE__  )
                    key.append(SCREAMING_SNAKE_CASE__  )
             return cipher, key

      @staticmethod
      def  SCREAMING_SNAKE_CASE							( SCREAMING_SNAKE_CASE__       : list[int]  ,							SCREAMING_SNAKE_CASE__       : list[int]  )	->    str:
             a_      :	List[Any]							=						[]
             for i in range(len(SCREAMING_SNAKE_CASE__  )  ):
                    a_      :	str							=						int((cipher[i] - (key[i]) ** 2) / key[i]  )
                    plain.append(chr(SCREAMING_SNAKE_CASE__  )  )
             return "".join(SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
   UpperCAmelCase_      ,     UpperCAmelCase_   :				Any						      = Onepad().encrypt('Hello')
   print(c, k)
   print(Onepad().decrypt(c, k))


