




"""simple docstring"""







from __future__ import annotations

def 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


		if not nums:
				return 0
		__lowercase				:		Any												=				nums[0]
		__lowercase				:		Tuple												=				0
		for num in nums[1:]:
				__lowercase				:		List[str]												=				(
				    max_excluding + num,
				    max(_lowerCamelCase,  _lowerCamelCase				),
				)
		return max(_lowerCamelCase,  _lowerCamelCase				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()








"""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


__A   :  List[Any]					=      get_logger(__name__)


__A   :  Tuple					=      Path(__file__).parent / 'model_card_template.md'
__A   :  Any					=      uuida().hex
__A   :  Optional[int]					=      os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
__A   :  Tuple					=      os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
__A   :  List[Any]					=      HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'

def 						snake_case__       (					_lowerCamelCase = None				)			->str:






		"""simple docstring"""


		__lowercase				:		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(_lowerCamelCase,  _lowerCamelCase				):
				ua += "; " + "; ".join(F'{k}/{v}' for k, v in user_agent.items()				)
		elif isinstance(_lowerCamelCase,  _lowerCamelCase				):
				ua += "; " + user_agent
		return ua

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase = None,  _lowerCamelCase = None				)			->List[Any]:






		"""simple docstring"""


		if token is None:
				__lowercase				:		Any												=				HfFolder.get_token()
		if organization is None:
				__lowercase				:		int												=				whoami(_lowerCamelCase				)["name"]
				return F'{username}/{model_id}'
		else:
				return F'{organization}/{model_id}'

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->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(_lowerCamelCase,  "local_rank"				) and args.local_rank not in [-1, 0]:
				return

		__lowercase				:		Tuple												=				args.hub_token if hasattr(_lowerCamelCase,  "hub_token"				) else None
		__lowercase				:		int												=				get_full_repo_name(_lowerCamelCase,  token=_lowerCamelCase				)

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

		__lowercase				:		Dict												=				os.path.join(args.output_dir,  "README.md"				)
		model_card.save(_lowerCamelCase				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase = None				)			->Tuple:






		"""simple docstring"""


		if resolved_file is None or commit_hash is not None:
				return commit_hash
		__lowercase				:		Union[str, Any]												=				str(Path(_lowerCamelCase				).as_posix()				)
		__lowercase				:		int												=				re.search(R"snapshots/([^/]+)/",  _lowerCamelCase				)
		if search is None:
				return None
		__lowercase				:		Union[str, Any]												=				search.groups()[0]
		return commit_hash if REGEX_COMMIT_HASH.match(_lowerCamelCase				) 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   :  Union[str, Any]					=      os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
__A   :  List[str]					=      os.path.join(hf_cache_home, 'diffusers')

def 						snake_case__       (					_lowerCamelCase = None,  _lowerCamelCase = None				)			->None:






		"""simple docstring"""


		if new_cache_dir is None:
				__lowercase				:		int												=				DIFFUSERS_CACHE
		if old_cache_dir is None:
				__lowercase				:		Tuple												=				old_diffusers_cache

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

if cache_version < 1:
						__A   :  Optional[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   :  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__       (					_lowerCamelCase,  _lowerCamelCase = None				)			->str:






		"""simple docstring"""


		if variant is not None:
				__lowercase				:		str												=				weights_name.split("."				)
				__lowercase				:		str												=				splits[:-1] + [variant] + splits[-1:]
				__lowercase				:		Dict												=				".".join(_lowerCamelCase				)

		return weights_name





def 						snake_case__       (					_lowerCamelCase,  *,
    _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase=None,  )			->str:






		"""simple docstring"""


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