



"""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


SCREAMING_SNAKE_CASE_				= get_logger(__name__)


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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__ = None	)			->      Any:
		a_     :      Optional[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        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ = None, SCREAMING_SNAKE_CASE__ = None	)			->      int:




		if token is None:
				a_     :      Optional[int] 						=     HfFolder.get_token()
		if organization is None:
				a_     :      int 						=     whoami(SCREAMING_SNAKE_CASE__	)["name"]
				return F"""{username}/{model_id}"""
		else:
				return F"""{organization}/{model_id}"""



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      List[str]:




		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

		a_     :      Dict 						=     args.hub_token if hasattr(SCREAMING_SNAKE_CASE__, "hub_token"	) else None
		a_     :      int 						=     get_full_repo_name(SCREAMING_SNAKE_CASE__, token=SCREAMING_SNAKE_CASE__	)

		a_     :      Tuple 						=     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, )

		a_     :      Union[str, Any] 						=     os.path.join(args.output_dir, "README.md"	)
		model_card.save(SCREAMING_SNAKE_CASE__	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ = None	)			->      Optional[int]:




		if resolved_file is None or commit_hash is not None:
				return commit_hash
		a_     :      Optional[Any] 						=     str(Path(SCREAMING_SNAKE_CASE__	).as_posix()	)
		a_     :      Optional[int] 						=     re.search(r"snapshots/([^/]+)/", SCREAMING_SNAKE_CASE__	)
		if search is None:
				return None
		a_     :      List[str] 						=     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.
SCREAMING_SNAKE_CASE_				= os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
SCREAMING_SNAKE_CASE_				= os.path.join(hf_cache_home, """diffusers""")



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__ = None, SCREAMING_SNAKE_CASE__ = None	)			->      Union[str, Any]:




		if new_cache_dir is None:
				a_     :      Dict 						=     DIFFUSERS_CACHE
		if old_cache_dir is None:
				a_     :      Tuple 						=     old_diffusers_cache

		a_     :      Any 						=     Path(SCREAMING_SNAKE_CASE__	).expanduser()
		a_     :      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():
						a_     :      List[str] 						=     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).


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

if cache_version < 1:
					SCREAMING_SNAKE_CASE_				= 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:
															SCREAMING_SNAKE_CASE_				= '''\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        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ = None	)			->      Tuple:




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

		return weights_name



def        lowerCAmelCase_						(							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, )			->      Dict:
		a_     :      Optional[Any] 						=     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
						a_     :      Dict 						=     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__	)	):
						a_     :      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:
								a_     :      int 						=     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
						a_     :      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"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ = False	)			->      bool:
		if n == 2:
				return True
		if not n % 2 or n < 2:
				return False
		if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
				return False
		if n > 3_317_044_064_679_887_385_961_981 and not allow_probable:
				raise ValueError(
				    "Warning: upper bound of deterministic test is exceeded. "
				    "Pass allow_probable=True to allow probabilistic test. "
				    "A return value of True indicates a probable prime."	)
		# array bounds provided by analysis
		a_     :      Optional[Any] 						=     [
		    2_047,
		    1_373_653,
		    25_326_001,
		    3_215_031_751,
		    2_152_302_898_747,
		    3_474_749_660_383,
		    341_550_071_728_321,
		    1,
		    3_825_123_056_546_413_051,
		    1,
		    1,
		    318_665_857_834_031_151_167_461,
		    3_317_044_064_679_887_385_961_981,
		]

		a_     :      Optional[int] 						=     [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
		for idx, _p in enumerate(SCREAMING_SNAKE_CASE__, 1	):
				if n < _p:
						# then we have our last prime to check
						a_     :      List[Any] 						=     primes[:idx]
						break
		a_						,				a_     :      Tuple 						=     n - 1, 0
		# break up n -1 into a power of 2 (s) and
		# remaining odd component
		# essentially, solve for d * 2 ** s == n - 1
		while d % 2 == 0:
				d //= 2
				s += 1
		for prime in plist:
				a_     :      str 						=     False
				for r in range(SCREAMING_SNAKE_CASE__	):
						a_     :      List[str] 						=     pow(SCREAMING_SNAKE_CASE__, d * 2**r, SCREAMING_SNAKE_CASE__	)
						# see article for analysis explanation for m
						if (r == 0 and m == 1) or ((m + 1) % n == 0):
								a_     :      List[Any] 						=     True
								# this loop will not determine compositeness
								break
				if pr:
						continue
				# if pr is False, then the above loop never evaluated to true,
				# and the n MUST be composite
				return False
		return True



def        lowerCAmelCase_						(							)			->      None:
		assert not miller_rabin(561	)
		assert miller_rabin(563	)
		# 2047

		assert not miller_rabin(838_201	)
		assert miller_rabin(838_207	)
		# 1_373_653

		assert not miller_rabin(17_316_001	)
		assert miller_rabin(17_316_017	)
		# 25_326_001

		assert not miller_rabin(3_078_386_641	)
		assert miller_rabin(3_078_386_653	)
		# 3_215_031_751

		assert not miller_rabin(1_713_045_574_801	)
		assert miller_rabin(1_713_045_574_819	)
		# 2_152_302_898_747

		assert not miller_rabin(2_779_799_728_307	)
		assert miller_rabin(2_779_799_728_327	)
		# 3_474_749_660_383

		assert not miller_rabin(113_850_023_909_441	)
		assert miller_rabin(113_850_023_909_527	)
		# 341_550_071_728_321

		assert not miller_rabin(1_275_041_018_848_804_351	)
		assert miller_rabin(1_275_041_018_848_804_391	)
		# 3_825_123_056_546_413_051

		assert not miller_rabin(79_666_464_458_507_787_791_867	)
		assert miller_rabin(79_666_464_458_507_787_791_951	)
		# 318_665_857_834_031_151_167_461

		assert not miller_rabin(552_840_677_446_647_897_660_333	)
		assert miller_rabin(552_840_677_446_647_897_660_359	)
		# 3_317_044_064_679_887_385_961_981
		# upper limit for probabilistic test


if __name__ == "__main__":
					test_miller_rabin()