
'''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


__snake_case:  Dict								=			get_logger(__name__)


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






def 	_snake_case						(      A_       :    List[Any] = None   ):
      """simple docstring"""







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






def 	_snake_case						(      A_       :    Tuple     ,		A_       :    Dict = None     ,		A_       :    Optional[int] = None   ):
      """simple docstring"""







      if token is None:
            a_      :	str      =							HfFolder.get_token()
      if organization is None:
            a_      :	Dict      =							whoami(A_   )["""name"""]
            return f'''{username}/{model_id}'''
      else:
            return f'''{organization}/{model_id}'''






def 	_snake_case						(      A_       :    Any     ,		A_       :    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(A_     ,		"""local_rank"""   ) and args.local_rank not in [-1, 0]:
            return

      a_      :	Union[str, Any]      =							args.hub_token if hasattr(A_     ,		"""hub_token"""   ) else None
      a_      :	int      =							get_full_repo_name(A_     ,		token=A_   )

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

      a_      :	Any      =							os.path.join(args.output_dir     ,		"""README.md"""   )
      model_card.save(A_   )






def 	_snake_case						(      A_       :    List[str]     ,		A_       :    Dict = None   ):
      """simple docstring"""







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






def 	_snake_case						(      A_       :    Tuple = None     ,		A_       :    Tuple = None   ):
      """simple docstring"""







      if new_cache_dir is None:
            a_      :	List[Any]      =							DIFFUSERS_CACHE
      if old_cache_dir is None:
            a_      :	int      =							old_diffusers_cache

      a_      :	Tuple      =							Path(A_   ).expanduser()
      a_      :	List[str]      =							Path(A_   ).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_      :	Tuple      =							new_cache_dir / old_blob_path.relative_to(A_   )
                  new_blob_path.parent.mkdir(parents=A_     ,		exist_ok=A_   )
                  os.replace(A_     ,		A_   )
                  try:
                        os.symlink(A_     ,		A_   )
                  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).


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

if cache_version < 1:
    __snake_case:  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:
            __snake_case:  Optional[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						(      A_       :    int     ,		A_       :    str = None   ):
      """simple docstring"""







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

      return weights_name






def 	_snake_case						(      A_       :    Optional[Any]     ,		*,
    A_       :    str     ,		A_       :    Optional[Any]     ,		A_       :    int     ,		A_       :    int     ,		A_       :    Optional[int]     ,		A_       :    List[Any]     ,		A_       :    str     ,		A_       :    List[str]     ,		A_       :    Optional[Any]     ,		A_       :    List[Any]     ,		A_       :    int=None     ,		):
      """simple docstring"""







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







  if len(A_   ) <= 1:
    return lst

  a_      :	Any      =							1

  while i < len(A_   ):
    if lst[i - 1] <= lst[i]:
      i += 1
    else:
      a_      , a_      :	int      =							lst[i], lst[i - 1]
      i -= 1
      if i == 0:
        a_      :	List[str]      =							1

  return lst


if __name__ == "__main__":
    __snake_case:  List[Any]								=			input("Enter numbers separated by a comma:\n").strip()
    __snake_case:  Optional[int]								=			[int(item) for item in user_input.split(",")]
    print(gnome_sort(unsorted))


