



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


snake_case    :						List[Any]  =						get_logger(__name__)


snake_case    :						Optional[int]  =						Path(__file__).parent / 'model_card_template.md'
snake_case    :						Any  =						uuida().hex
snake_case    :						List[str]  =						os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
snake_case    :						Union[str, Any]  =						os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
snake_case    :						List[str]  =						HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'
def 	snake_case__       (					__lowercase = None       )		->		str:







						"""simple docstring"""

						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(__lowercase							,     __lowercase       ):
												ua += "; " + "; ".join(F'{k}/{v}' for k, v in user_agent.items()       )
						elif isinstance(__lowercase							,     __lowercase       ):
												ua += "; " + user_agent
						return ua
def 	snake_case__       (					__lowercase							,     __lowercase = None							,     __lowercase = None       )		->		Optional[Any]:







						"""simple docstring"""

						if token is None:
												A__	:							List[Any]						    = HfFolder.get_token()
						if organization is None:
												A__	:							Union[str, Any]						    = whoami(__lowercase       )["name"]
												return F'{username}/{model_id}'
						else:
												return F'{organization}/{model_id}'
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		int:







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

						A__	:							List[Any]						    = args.hub_token if hasattr(__lowercase							,     "hub_token"       ) else None
						A__	:							int						    = get_full_repo_name(__lowercase							,     token=__lowercase       )

						A__	:							Union[str, 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=__lowercase							,     model_name=__lowercase							,     repo_name=__lowercase							,     dataset_name=args.dataset_name if hasattr(__lowercase							,     "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(__lowercase							,     "gradient_accumulation_steps"       ) else None
						    )							,     adam_betaa=args.adam_betaa if hasattr(__lowercase							,     "adam_beta1"       ) else None							,     adam_betaa=args.adam_betaa if hasattr(__lowercase							,     "adam_beta2"       ) else None							,     adam_weight_decay=args.adam_weight_decay if hasattr(__lowercase							,     "adam_weight_decay"       ) else None							,     adam_epsilon=args.adam_epsilon if hasattr(__lowercase							,     "adam_epsilon"       ) else None							,     lr_scheduler=args.lr_scheduler if hasattr(__lowercase							,     "lr_scheduler"       ) else None							,     lr_warmup_steps=args.lr_warmup_steps if hasattr(__lowercase							,     "lr_warmup_steps"       ) else None							,     ema_inv_gamma=args.ema_inv_gamma if hasattr(__lowercase							,     "ema_inv_gamma"       ) else None							,     ema_power=args.ema_power if hasattr(__lowercase							,     "ema_power"       ) else None							,     ema_max_decay=args.ema_max_decay if hasattr(__lowercase							,     "ema_max_decay"       ) else None							,     mixed_precision=args.mixed_precision							,     )

						A__	:							Optional[Any]						    = os.path.join(args.output_dir							,     "README.md"       )
						model_card.save(__lowercase       )
def 	snake_case__       (					__lowercase							,     __lowercase = None       )		->		int:







						"""simple docstring"""

						if resolved_file is None or commit_hash is not None:
												return commit_hash
						A__	:							Union[str, Any]						    = str(Path(__lowercase       ).as_posix()       )
						A__	:							List[Any]						    = re.search(r"snapshots/([^/]+)/"							,     __lowercase       )
						if search is None:
												return None
						A__	:							Optional[Any]						    = search.groups()[0]
						return commit_hash if REGEX_COMMIT_HASH.match(__lowercase       ) 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.
snake_case    :						str  =						os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
snake_case    :						List[str]  =						os.path.join(hf_cache_home, 'diffusers')
def 	snake_case__       (					__lowercase = None							,     __lowercase = None       )		->		None:







						"""simple docstring"""

						if new_cache_dir is None:
												A__	:							List[str]						    = DIFFUSERS_CACHE
						if old_cache_dir is None:
												A__	:							Any						    = old_diffusers_cache

						A__	:							int						    = Path(__lowercase       ).expanduser()
						A__	:							List[Any]						    = Path(__lowercase       ).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__	:							Any						    = new_cache_dir / old_blob_path.relative_to(__lowercase       )
																		new_blob_path.parent.mkdir(parents=__lowercase							,     exist_ok=__lowercase       )
																		os.replace(__lowercase							,     __lowercase       )
																		try:
																								os.symlink(__lowercase							,     __lowercase       )
																		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).


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

if cache_version < 1:
		snake_case    :						Optional[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:
						snake_case    :						Tuple  =						'\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 	snake_case__       (					__lowercase							,     __lowercase = None       )		->		str:







						"""simple docstring"""

						if variant is not None:
												A__	:							Union[str, Any]						    = weights_name.split("."       )
												A__	:							Any						    = splits[:-1] + [variant] + splits[-1:]
												A__	:							Optional[Any]						    = ".".join(__lowercase       )

						return weights_name




def 	snake_case__       (					__lowercase							,     *,
    __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase=None							,     )		->		Dict:







						"""simple docstring"""

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




def 	snake_case__       (					)		->		Union[str, Any]:







						"""simple docstring"""

						A__	:							Tuple						    = []
						A__	:							Optional[int]						    = 1

						while len(__lowercase       ) < 1E6:
												constant.append(str(__lowercase       )       )
												i += 1

						A__	:							Any						    = "".join(__lowercase       )

						return (
						    int(constant[0]       )
						    * int(constant[9]       )
						    * int(constant[9_9]       )
						    * int(constant[9_9_9]       )
						    * int(constant[9_9_9_9]       )
						    * int(constant[9_9_9_9_9]       )
						    * int(constant[9_9_9_9_9_9]       )
						)


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