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


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







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[Dict, str, None] = None )				->       str:





  """simple docstring"""

  __A		:     int												=		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        A_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  Optional[str] = None			,     __SCREAMING_SNAKE_CASE						:  Optional[str] = None )				->       str:





  """simple docstring"""

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







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[int]			,     __SCREAMING_SNAKE_CASE						:  List[str] )				->       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

  __A		:     Optional[Any]												=		args.hub_token if hasattr(__SCREAMING_SNAKE_CASE			,     """hub_token""" ) else None
  __A		:     List[str]												=		get_full_repo_name(__SCREAMING_SNAKE_CASE			,     token=__SCREAMING_SNAKE_CASE )

  __A		:     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			,     )

  __A		:     str												=		os.path.join(args.output_dir			,     """README.md""" )
  model_card.save(__SCREAMING_SNAKE_CASE )







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[str]			,     __SCREAMING_SNAKE_CASE						:  Optional[str] = None )				->       int:





  """simple docstring"""

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







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[str] = None			,     __SCREAMING_SNAKE_CASE						:  Optional[str] = None )				->       None:





  """simple docstring"""

  if new_cache_dir is None:
    __A		:     Any												=		DIFFUSERS_CACHE
  if old_cache_dir is None:
    __A		:     Dict												=		old_diffusers_cache

  __A		:     Optional[Any]												=		Path(__SCREAMING_SNAKE_CASE ).expanduser()
  __A		:     str												=		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		:     Dict												=		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).


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

if cache_version < 1:
     A__   :		Dict 		=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__   :		int 		='\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_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  Optional[str] = None )				->       str:





  """simple docstring"""

  if variant is not None:
    __A		:     Tuple												=		weights_name.split(""".""" )
    __A		:     Dict												=		splits[:-1] + [variant] + splits[-1:]
    __A		:     Optional[int]												=		""".""".join(__SCREAMING_SNAKE_CASE )

  return weights_name







def        A_     (						__SCREAMING_SNAKE_CASE						:  int			,     *,
    __SCREAMING_SNAKE_CASE						:  Optional[int]			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  Tuple			,     __SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any]=None			,     )				->       Any:





  """simple docstring"""

  __A		:     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
      __A		:     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 ) ):
      __A		:     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:
        __A		:     Dict												=		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		:     List[Any]												=		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 collections.abc import Sequence







def        A_     (						__SCREAMING_SNAKE_CASE						:  Sequence[float]			,     __SCREAMING_SNAKE_CASE						:  bool = False )				->       float:





  """simple docstring"""

  if not arr:
    return 0

  __A		:     Any												=		0 if allow_empty_subarrays else float("""-inf""" )
  __A		:     List[Any]												=		0.0
  for num in arr:
    __A		:     Tuple												=		max(0 if allow_empty_subarrays else num			,     curr_sum + num )
    __A		:     Optional[int]												=		max(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  return max_sum


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     A__   :		Any 		=[-2, 1, -3, 4, -1, 2, 1, -5, 4]
     print(F"{max_subarray_sum(nums) = }")



