
'''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


_lowercase			:  str   							=	get_logger(__name__)


_lowercase			:  Union[str, Any]   							=	Path(__file__).parent / "model_card_template.md"
_lowercase			:  List[Any]   							=	uuida().hex
_lowercase			:  int   							=	os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
_lowercase			:  List[str]   							=	os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
_lowercase			:  List[Any]   							=	HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Union[Dict, str, None] = None							):



						"""simple docstring"""





						lowercase_     :					List[str]													=	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  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Optional[str] = None				,				__SCREAMING_SNAKE_CASE :			Optional[str] = None							):



						"""simple docstring"""





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



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

						lowercase_     :					Union[str, Any]													=	args.hub_token if hasattr(__SCREAMING_SNAKE_CASE				,				'''hub_token'''							) else None
						lowercase_     :					Optional[Any]													=	get_full_repo_name(__SCREAMING_SNAKE_CASE				,				token=__SCREAMING_SNAKE_CASE							)

						lowercase_     :					str													=	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				,				)

						lowercase_     :					List[Any]													=	os.path.join(args.output_dir				,				'''README.md'''							)
						model_card.save(__SCREAMING_SNAKE_CASE							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[str]				,				__SCREAMING_SNAKE_CASE :			Optional[str] = None							):



						"""simple docstring"""





						if resolved_file is None or commit_hash is not None:
												return commit_hash
						lowercase_     :					List[str]													=	str(Path(__SCREAMING_SNAKE_CASE							).as_posix()							)
						lowercase_     :					Union[str, Any]													=	re.search(R'''snapshots/([^/]+)/'''				,				__SCREAMING_SNAKE_CASE							)
						if search is None:
												return None
						lowercase_     :					Dict													=	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.
_lowercase			:  Any   							=	os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
_lowercase			:  Optional[int]   							=	os.path.join(hf_cache_home, "diffusers")
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[str] = None				,				__SCREAMING_SNAKE_CASE :			Optional[str] = None							):



						"""simple docstring"""





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

						lowercase_     :					Any													=	Path(__SCREAMING_SNAKE_CASE							).expanduser()
						lowercase_     :					Optional[int]													=	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():
																		lowercase_     :					Union[str, Any]													=	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).


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

if cache_version < 1:
						_lowercase			:  Union[str, 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:
																		_lowercase			:  Union[str, Any]   							=	"\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_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Optional[str] = None							):



						"""simple docstring"""





						if variant is not None:
												lowercase_     :					Optional[Any]													=	weights_name.split('''.'''							)
												lowercase_     :					List[Any]													=	splits[:-1] + [variant] + splits[-1:]
												lowercase_     :					Tuple													=	'''.'''.join(__SCREAMING_SNAKE_CASE							)

						return weights_name




def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Any				,				*,
    __SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			Dict				,				__SCREAMING_SNAKE_CASE :			List[str]				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			Dict				,				__SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			Optional[int]				,				__SCREAMING_SNAKE_CASE :			Optional[int]=None				,				):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	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
																		lowercase_     :					str													=	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							)							):
																		lowercase_     :					Optional[Any]													=	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:
																								lowercase_     :					str													=	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
																		lowercase_     :					int													=	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'''







from PIL import Image
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Image				,				__SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	(259 * (level + 255)) / (255 * (259 - level))

						def contrast(__SCREAMING_SNAKE_CASE :			int							) -> int:
												return int(128 + factor * (c - 128)							)

						return img.point(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							# Load image
							with Image.open("image_data/lena.jpg") as img:
														# Change contrast to 170
														_lowercase			:  Union[str, Any]   							=	change_contrast(img, 1_7_0)
														cont_img.save("image_data/lena_high_contrast.png", format="png")



