

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


lowerCAmelCase_			      =      get_logger(__name__)


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


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__ = None			):
						snake_case_  =      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     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None			):
						if token is None:
												snake_case_  =      HfFolder.get_token()
						if organization is None:
												snake_case_  =      whoami(SCREAMING_SNAKE_CASE__			)['''name''']
												return F'''{username}/{model_id}'''
						else:
												return F'''{organization}/{model_id}'''


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						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

						snake_case_  =      args.hub_token if hasattr(SCREAMING_SNAKE_CASE__    ,						'''hub_token'''			) else None
						snake_case_  =      get_full_repo_name(SCREAMING_SNAKE_CASE__    ,						token=SCREAMING_SNAKE_CASE__			)

						snake_case_  =      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    ,						)

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


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None			):
						if resolved_file is None or commit_hash is not None:
												return commit_hash
						snake_case_  =      str(Path(SCREAMING_SNAKE_CASE__			).as_posix()			)
						snake_case_  =      re.search(R'''snapshots/([^/]+)/'''    ,						SCREAMING_SNAKE_CASE__			)
						if search is None:
												return None
						snake_case_  =      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.
lowerCAmelCase_			      =      os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
lowerCAmelCase_			      =      os.path.join(hf_cache_home, '''diffusers''')


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None			):
						if new_cache_dir is None:
												snake_case_  =      DIFFUSERS_CACHE
						if old_cache_dir is None:
												snake_case_  =      old_diffusers_cache

						snake_case_  =      Path(SCREAMING_SNAKE_CASE__			).expanduser()
						snake_case_  =      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():
																		snake_case_  =      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).


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

if cache_version < 1:
					lowerCAmelCase_			      =      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:
															lowerCAmelCase_			      =      '''\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   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None			):
						if variant is not None:
												snake_case_  =      weights_name.split('''.'''			)
												snake_case_  =      splits[:-1] + [variant] + splits[-1:]
												snake_case_  =      '''.'''.join(SCREAMING_SNAKE_CASE__			)

						return weights_name


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						*,
    SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None    ,						):
						snake_case_  =      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
																		snake_case_  =      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__			)			):
																		snake_case_  =      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:
																								snake_case_  =      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
																		snake_case_  =      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'''





def 				_lowerCAmelCase			(   __magic_name__						:     int = 600851475143						)				->  int:

						try:
												lowercase :							Any    		=int(__magic_name__						)
						except (TypeError, ValueError):
												raise TypeError('''Parameter n must be int or castable to int.'''						)
						if n <= 0:
												raise ValueError('''Parameter n must be greater than or equal to one.'''						)
						lowercase :							Optional[Any]    		=2
						lowercase :							Dict    		=0
						if n == 2:
												return 2
						while n > 2:
												while n % i != 0:
																		i += 1
												lowercase :							Union[str, Any]    		=i
												while n % i == 0:
																		lowercase :							Optional[int]    		=n // i
												i += 1
						return int(__magic_name__						)


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





