





"""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


A	     =					get_logger(__name__)


A	     =					Path(__file__).parent / '''model_card_template.md'''
A	     =					uuida().hex
A	     =					os.getenv('''HF_HUB_OFFLINE''', '''''').upper() in ENV_VARS_TRUE_VALUES
A	     =					os.getenv('''DISABLE_TELEMETRY''', '''''').upper() in ENV_VARS_TRUE_VALUES
A	     =					HUGGINGFACE_CO_RESOLVE_ENDPOINT + '''/api/telemetry/'''

def       __A (     a_  :Union[Dict, str, None] = None)						->						str:
    __a	:  Optional[int]         =					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       __A (     a_  :str				,    a_  :Optional[str] = None				,    a_  :Optional[str] = None)						->						int:
    if token is None:
        __a	:  Union[str, Any]         =					HfFolder.get_token()
    if organization is None:
        __a	:  List[Any]         =					whoami(a__)['''name''']
        return F"""{username}/{model_id}"""
    else:
        return F"""{organization}/{model_id}"""

def       __A (     a_  :str				,    a_  :Optional[int])						->						List[Any]:
    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	:  int         =					args.hub_token if hasattr(a__				,    '''hub_token''') else None
    __a	:  List[str]         =					get_full_repo_name(a__				,    token=a__)

    __a	:  int         =					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	:  int         =					os.path.join(args.output_dir				,    '''README.md''')
    model_card.save(a__)

def       __A (     a_  :Optional[str]				,    a_  :Optional[str] = None)						->						List[Any]:
    if resolved_file is None or commit_hash is not None:
        return commit_hash
    __a	:  int         =					str(Path(a__).as_posix())
    __a	:  Dict         =					re.search(R'''snapshots/([^/]+)/'''				,    a__)
    if search is None:
        return None
    __a	:  Optional[Any]         =					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.
A	     =					os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
A	     =					os.path.join(hf_cache_home, '''diffusers''')

def       __A (     a_  :Optional[str] = None				,    a_  :Optional[str] = None)						->						None:
    if new_cache_dir is None:
        __a	:  int         =					DIFFUSERS_CACHE
    if old_cache_dir is None:
        __a	:  Dict         =					old_diffusers_cache

    __a	:  int         =					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	:  List[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).


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

if cache_version < 1:
  A	     =					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:
      A	     =					'''\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 (     a_  :str				,    a_  :Optional[str] = None)						->						str:
    if variant is not None:
        __a	:  Optional[Any]         =					weights_name.split('''.''')
        __a	:  Optional[Any]         =					splits[:-1] + [variant] + splits[-1:]
        __a	:  int         =					'''.'''.join(a__)

    return weights_name






def       __A (     a_  :Union[str, Any]				,    *,
    a_  :str				,    a_  :Any				,    a_  :Dict				,    a_  :List[Any]				,    a_  :Optional[int]				,    a_  :List[Any]				,    a_  :Union[str, Any]				,    a_  :Union[str, Any]				,    a_  :List[str]				,    a_  :Union[str, Any]				,    a_  :List[Any]=None				,    )						->						Dict:
    __a	:  str         =					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	:  Tuple         =					os.path.join(a__				,    a__)
            return model_file
        elif subfolder is not None and os.path.isfile(
            os.path.join(a__				,    a__				,    a__)):
            __a	:  List[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	:  Union[str, 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	:  int         =					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}""")






"""simple docstring"""






def       __A (     a_  :int = 1_00_00_00)						->						int:
				__a	:  Tuple         =					[i - 1 for i in range(limit + 1)]

				for i in range(2				,    limit + 1):
								if phi[i] == i - 1:
												for j in range(2 * i				,    limit + 1				,    a_):
																phi[j] -= phi[j] // i

				return sum(phi[2 : limit + 1])


if __name__ == "__main__":
		print(solution())