





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     :    Union[str, Any]         =						get_logger(__name__)


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




				"""simple docstring"""






				snake_case   :					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(_UpperCamelCase		,     _UpperCamelCase   ):
								ua += "; " + "; ".join(F"{k}/{v}" for k, v in user_agent.items()   )
				elif isinstance(_UpperCamelCase		,     _UpperCamelCase   ):
								ua += "; " + user_agent
				return ua
def 							a_	(    __magic_name__		,     __magic_name__ = None		,     __magic_name__ = None   )				->			Dict:




				"""simple docstring"""






				if token is None:
								snake_case   :					Optional[Any]    						=				HfFolder.get_token()
				if organization is None:
								snake_case   :					Any    						=				whoami(_UpperCamelCase   )['''name''']
								return F"{username}/{model_id}"
				else:
								return F"{organization}/{model_id}"
def 							a_	(    __magic_name__		,     __magic_name__   )				->			Dict:




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

				snake_case   :					Optional[Any]    						=				args.hub_token if hasattr(_UpperCamelCase		,     '''hub_token'''   ) else None
				snake_case   :					Any    						=				get_full_repo_name(_UpperCamelCase		,     token=_UpperCamelCase   )

				snake_case   :					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=_UpperCamelCase		,     model_name=_UpperCamelCase		,     repo_name=_UpperCamelCase		,     dataset_name=args.dataset_name if hasattr(_UpperCamelCase		,     '''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(_UpperCamelCase		,     '''gradient_accumulation_steps'''   ) else None
				    )		,     adam_betaa=args.adam_betaa if hasattr(_UpperCamelCase		,     '''adam_beta1'''   ) else None		,     adam_betaa=args.adam_betaa if hasattr(_UpperCamelCase		,     '''adam_beta2'''   ) else None		,     adam_weight_decay=args.adam_weight_decay if hasattr(_UpperCamelCase		,     '''adam_weight_decay'''   ) else None		,     adam_epsilon=args.adam_epsilon if hasattr(_UpperCamelCase		,     '''adam_epsilon'''   ) else None		,     lr_scheduler=args.lr_scheduler if hasattr(_UpperCamelCase		,     '''lr_scheduler'''   ) else None		,     lr_warmup_steps=args.lr_warmup_steps if hasattr(_UpperCamelCase		,     '''lr_warmup_steps'''   ) else None		,     ema_inv_gamma=args.ema_inv_gamma if hasattr(_UpperCamelCase		,     '''ema_inv_gamma'''   ) else None		,     ema_power=args.ema_power if hasattr(_UpperCamelCase		,     '''ema_power'''   ) else None		,     ema_max_decay=args.ema_max_decay if hasattr(_UpperCamelCase		,     '''ema_max_decay'''   ) else None		,     mixed_precision=args.mixed_precision		,     )

				snake_case   :					List[Any]    						=				os.path.join(args.output_dir		,     '''README.md'''   )
				model_card.save(_UpperCamelCase   )
def 							a_	(    __magic_name__		,     __magic_name__ = None   )				->			Optional[int]:




				"""simple docstring"""






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




				"""simple docstring"""






				if new_cache_dir is None:
								snake_case   :					Dict    						=				DIFFUSERS_CACHE
				if old_cache_dir is None:
								snake_case   :					Union[str, Any]    						=				old_diffusers_cache

				snake_case   :					List[Any]    						=				Path(_UpperCamelCase   ).expanduser()
				snake_case   :					Dict    						=				Path(_UpperCamelCase   ).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   :					List[Any]    						=				new_cache_dir / old_blob_path.relative_to(_UpperCamelCase   )
												new_blob_path.parent.mkdir(parents=_UpperCamelCase		,     exist_ok=_UpperCamelCase   )
												os.replace(_UpperCamelCase		,     _UpperCamelCase   )
												try:
																os.symlink(_UpperCamelCase		,     _UpperCamelCase   )
												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     :    str         =						os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
			_a     :    Union[str, Any]         =						0
else:
			with open(cache_version_file) as f:
						try:
									_a     :    Dict         =						int(f.read())
						except ValueError:
									_a     :    int         =						0

if cache_version < 1:
			_a     :    List[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:
									_a     :    List[str]         =						'''\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_	(    __magic_name__		,     __magic_name__ = None   )				->			Union[str, Any]:




				"""simple docstring"""






				if variant is not None:
								snake_case   :					Union[str, Any]    						=				weights_name.split('''.'''   )
								snake_case   :					Optional[Any]    						=				splits[:-1] + [variant] + splits[-1:]
								snake_case   :					Tuple    						=				'''.'''.join(_UpperCamelCase   )

				return weights_name
def 							a_	(    __magic_name__		,     *,
    __magic_name__		,     __magic_name__		,     __magic_name__		,     __magic_name__		,     __magic_name__		,     __magic_name__		,     __magic_name__		,     __magic_name__		,     __magic_name__		,     __magic_name__		,     __magic_name__=None		,     )				->			Optional[int]:




				"""simple docstring"""






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


			'''simple docstring'''







			__lowercase			       = hex_num.strip()
			if not hex_num:
						raise ValueError('''No value was passed to the function'''	)

			__lowercase			       = hex_num[0] == '''-'''
			if is_negative:
						__lowercase			       = hex_num[1:]

			try:
						__lowercase			       = int(_UpperCamelCase							,      16	)
			except ValueError:
						raise ValueError('''Invalid value was passed to the function'''	)

			__lowercase			       = ''''''
			while int_num > 0:
						__lowercase			       = str(int_num % 2	) + bin_str
						int_num >>= 1

			return int(('''-''' + bin_str) if is_negative else bin_str	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


