"""simple docstring"""





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





def        a_  (   _lowerCAmelCase						:				Union[Dict, str, None] = None  ):





       '''simple docstring'''

       lowercase__    :	List[Any]     					=			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(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__  ):
              ua += "; " + "; ".join(f"""{k}/{v}""" for k, v in user_agent.items()  )
       elif isinstance(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__  ):
              ua += "; " + user_agent
       return ua





def        a_  (   _lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				Optional[str] = None     ,				_lowerCAmelCase						:				Optional[str] = None  ):





       '''simple docstring'''

       if token is None:
              lowercase__    :	Optional[Any]     					=			HfFolder.get_token()
       if organization is None:
              lowercase__    :	Tuple     					=			whoami(SCREAMING_SNAKE_CASE__  )["""name"""]
              return f"""{username}/{model_id}"""
       else:
              return f"""{organization}/{model_id}"""





def        a_  (   _lowerCAmelCase						:				List[str]     ,				_lowerCAmelCase						:				str  ):





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

       lowercase__    :	Optional[int]     					=			args.hub_token if hasattr(SCREAMING_SNAKE_CASE__     ,				'hub_token'  ) else None
       lowercase__    :	Union[str, Any]     					=			get_full_repo_name(SCREAMING_SNAKE_CASE__     ,				token=SCREAMING_SNAKE_CASE__  )

       lowercase__    :	Optional[Any]     					=			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=SCREAMING_SNAKE_CASE__     ,				model_name=SCREAMING_SNAKE_CASE__     ,				repo_name=SCREAMING_SNAKE_CASE__     ,				dataset_name=args.dataset_name if hasattr(SCREAMING_SNAKE_CASE__     ,				'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(SCREAMING_SNAKE_CASE__     ,				'gradient_accumulation_steps'  ) else None
           )     ,				adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE__     ,				'adam_beta1'  ) else None     ,				adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE__     ,				'adam_beta2'  ) else None     ,				adam_weight_decay=args.adam_weight_decay if hasattr(SCREAMING_SNAKE_CASE__     ,				'adam_weight_decay'  ) else None     ,				adam_epsilon=args.adam_epsilon if hasattr(SCREAMING_SNAKE_CASE__     ,				'adam_epsilon'  ) else None     ,				lr_scheduler=args.lr_scheduler if hasattr(SCREAMING_SNAKE_CASE__     ,				'lr_scheduler'  ) else None     ,				lr_warmup_steps=args.lr_warmup_steps if hasattr(SCREAMING_SNAKE_CASE__     ,				'lr_warmup_steps'  ) else None     ,				ema_inv_gamma=args.ema_inv_gamma if hasattr(SCREAMING_SNAKE_CASE__     ,				'ema_inv_gamma'  ) else None     ,				ema_power=args.ema_power if hasattr(SCREAMING_SNAKE_CASE__     ,				'ema_power'  ) else None     ,				ema_max_decay=args.ema_max_decay if hasattr(SCREAMING_SNAKE_CASE__     ,				'ema_max_decay'  ) else None     ,				mixed_precision=args.mixed_precision     ,				)

       lowercase__    :	Tuple     					=			os.path.join(args.output_dir     ,				'README.md'  )
       model_card.save(SCREAMING_SNAKE_CASE__  )





def        a_  (   _lowerCAmelCase						:				Optional[str]     ,				_lowerCAmelCase						:				Optional[str] = None  ):





       '''simple docstring'''

       if resolved_file is None or commit_hash is not None:
              return commit_hash
       lowercase__    :	Optional[int]     					=			str(Path(SCREAMING_SNAKE_CASE__  ).as_posix()  )
       lowercase__    :	List[str]     					=			re.search(R'snapshots/([^/]+)/'     ,				SCREAMING_SNAKE_CASE__  )
       if search is None:
              return None
       lowercase__    :	Union[str, Any]     					=			search.groups()[0]
       return commit_hash if REGEX_COMMIT_HASH.match(SCREAMING_SNAKE_CASE__  ) 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     :		Optional[Any]											=					os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
_UpperCamelCase     :		str											=					os.path.join(hf_cache_home, "diffusers")





def        a_  (   _lowerCAmelCase						:				Optional[str] = None     ,				_lowerCAmelCase						:				Optional[str] = None  ):





       '''simple docstring'''

       if new_cache_dir is None:
              lowercase__    :	Tuple     					=			DIFFUSERS_CACHE
       if old_cache_dir is None:
              lowercase__    :	List[Any]     					=			old_diffusers_cache

       lowercase__    :	List[str]     					=			Path(SCREAMING_SNAKE_CASE__  ).expanduser()
       lowercase__    :	Union[str, Any]     					=			Path(SCREAMING_SNAKE_CASE__  ).expanduser()
       for old_blob_path in old_cache_dir.glob('**/blobs/*'  ):
              if old_blob_path.is_file() and not old_blob_path.is_symlink():
                     lowercase__    :	Union[str, Any]     					=			new_cache_dir / old_blob_path.relative_to(SCREAMING_SNAKE_CASE__  )
                     new_blob_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__     ,				exist_ok=SCREAMING_SNAKE_CASE__  )
                     os.replace(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__  )
                     try:
                            os.symlink(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__  )
                     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     :		Optional[int]											=					os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
       _UpperCamelCase     :		Union[str, Any]											=					0
else:
       with open(cache_version_file) as f:
              try:
                     _UpperCamelCase     :		Optional[Any]											=					int(f.read())
              except ValueError:
                     _UpperCamelCase     :		Any											=					0

if cache_version < 1:
       _UpperCamelCase     :		int											=					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     :		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        a_  (   _lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				Optional[str] = None  ):





       '''simple docstring'''

       if variant is not None:
              lowercase__    :	int     					=			weights_name.split('.'  )
              lowercase__    :	Dict     					=			splits[:-1] + [variant] + splits[-1:]
              lowercase__    :	int     					=			""".""".join(SCREAMING_SNAKE_CASE__  )

       return weights_name






def        a_  (   _lowerCAmelCase						:				Union[str, Any]     ,				*,
    _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Tuple     ,				_lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				List[str]     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Tuple     ,				_lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				int=None     ,				):





       '''simple docstring'''

       lowercase__    :	int     					=			str(SCREAMING_SNAKE_CASE__  )
       if os.path.isfile(SCREAMING_SNAKE_CASE__  ):
              return pretrained_model_name_or_path
       elif os.path.isdir(SCREAMING_SNAKE_CASE__  ):
              if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__  )  ):
                     # Load from a PyTorch checkpoint
                     lowercase__    :	str     					=			os.path.join(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__  )
                     return model_file
              elif subfolder is not None and os.path.isfile(
                  os.path.join(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__  )  ):
                     lowercase__    :	List[str]     					=			os.path.join(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__  )
                     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(SCREAMING_SNAKE_CASE__  ).base_version  ) >= version.parse('0.20.0'  )
              ):
                     try:
                            lowercase__    :	Tuple     					=			hf_hub_download(
                                SCREAMING_SNAKE_CASE__     ,				filename=_add_variant(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__  )     ,				cache_dir=SCREAMING_SNAKE_CASE__     ,				force_download=SCREAMING_SNAKE_CASE__     ,				proxies=SCREAMING_SNAKE_CASE__     ,				resume_download=SCREAMING_SNAKE_CASE__     ,				local_files_only=SCREAMING_SNAKE_CASE__     ,				use_auth_token=SCREAMING_SNAKE_CASE__     ,				user_agent=SCREAMING_SNAKE_CASE__     ,				subfolder=SCREAMING_SNAKE_CASE__     ,				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."""     ,				SCREAMING_SNAKE_CASE__     ,				)
                            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(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__  )} 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(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__  )}\' so that the correct variant file can be added."""     ,				SCREAMING_SNAKE_CASE__     ,				)
              try:
                     # 2. Load model file as usual
                     lowercase__    :	List[str]     					=			hf_hub_download(
                         SCREAMING_SNAKE_CASE__     ,				filename=SCREAMING_SNAKE_CASE__     ,				cache_dir=SCREAMING_SNAKE_CASE__     ,				force_download=SCREAMING_SNAKE_CASE__     ,				proxies=SCREAMING_SNAKE_CASE__     ,				resume_download=SCREAMING_SNAKE_CASE__     ,				local_files_only=SCREAMING_SNAKE_CASE__     ,				use_auth_token=SCREAMING_SNAKE_CASE__     ,				user_agent=SCREAMING_SNAKE_CASE__     ,				subfolder=SCREAMING_SNAKE_CASE__     ,				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}"""  )
"""simple docstring"""





import math





def        a_  (   _lowerCAmelCase						:				int = 100  ):





       '''simple docstring'''

       lowercase__    :	Union[str, Any]     					=			sum(i * i for i in range(1     ,				n + 1  )  )
       lowercase__    :	str     					=			int(math.pow(sum(range(1     ,				n + 1  )  )     ,				2  )  )
       return square_of_sum - sum_of_squares


if __name__ == "__main__":
       print(f'''{solution() = }''')

