

'''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   :			Optional[Any]         = get_logger(__name__)


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





def       lowercase					(     __magic_name__ = None							):



					'''simple docstring'''







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





def       lowercase					(     __magic_name__ ,    __magic_name__ = None ,    __magic_name__ = None							):



					'''simple docstring'''







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





def       lowercase					(     __magic_name__ ,    __magic_name__							):



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

					UpperCAmelCase		:							Optional[Any]   					=   args.hub_token if hasattr(__UpperCamelCase ,    "hub_token"							) else None
					UpperCAmelCase		:							int   					=   get_full_repo_name(__UpperCamelCase ,    token=__UpperCamelCase							)

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

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





def       lowercase					(     __magic_name__ ,    __magic_name__ = None							):



					'''simple docstring'''







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





def       lowercase					(     __magic_name__ = None ,    __magic_name__ = None							):



					'''simple docstring'''







					if new_cache_dir is None:
										UpperCAmelCase		:							Tuple   					=   DIFFUSERS_CACHE
					if old_cache_dir is None:
										UpperCAmelCase		:							Optional[int]   					=   old_diffusers_cache

					UpperCAmelCase		:							List[str]   					=   Path(__UpperCamelCase							).expanduser()
					UpperCAmelCase		:							Optional[Any]   					=   Path(__UpperCamelCase							).expanduser()
					for old_blob_path in old_cache_dir.glob("**/blobs/*"							):
										if old_blob_path.is_file() and not old_blob_path.is_symlink():
															UpperCAmelCase		:							Optional[Any]   					=   new_cache_dir / old_blob_path.relative_to(__UpperCamelCase							)
															new_blob_path.parent.mkdir(parents=__UpperCamelCase ,    exist_ok=__UpperCamelCase							)
															os.replace(__UpperCamelCase ,    __UpperCamelCase							)
															try:
																				os.symlink(__UpperCamelCase ,    __UpperCamelCase							)
															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   :			List[str]         = os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
				a   :			List[str]         = 0
else:
				with open(cache_version_file) as f:
								try:
												a   :			Dict         = int(f.read())
								except ValueError:
												a   :			Tuple         = 0

if cache_version < 1:
				a   :			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:
												a   :			Tuple         = '''\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       lowercase					(     __magic_name__ ,    __magic_name__ = None							):



					'''simple docstring'''







					if variant is not None:
										UpperCAmelCase		:							Dict   					=   weights_name.split("."							)
										UpperCAmelCase		:							int   					=   splits[:-1] + [variant] + splits[-1:]
										UpperCAmelCase		:							int   					=   ".".join(__UpperCamelCase							)

					return weights_name





def       lowercase					(     __magic_name__ ,    *,
    __magic_name__ ,    __magic_name__ ,    __magic_name__ ,    __magic_name__ ,    __magic_name__ ,    __magic_name__ ,    __magic_name__ ,    __magic_name__ ,    __magic_name__ ,    __magic_name__ ,    __magic_name__=None ,    ):



					'''simple docstring'''







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

import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


__lowerCamelCase			: List[str]							   =		NewType('''DataClass''', Any)
__lowerCamelCase			: Dict							   =		NewType('''DataClassType''', Any)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any	)							->					int:







  """simple docstring"""





  if isinstance(__UpperCamelCase ,     __UpperCamelCase	):
    return v
  if v.lower() in ("yes", "true", "t", "y", "1"):
    return True
  elif v.lower() in ("no", "false", "f", "n", "0"):
    return False
  else:
    raise ArgumentTypeError(
        f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive)."""	)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       list	)							->					Callable[[str], Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      {str(__UpperCamelCase	): choice for choice in choices}
  return lambda __UpperCamelCase	: str_to_choice.get(__UpperCamelCase ,     __UpperCamelCase	)



def 	__SCREAMING_SNAKE_CASE  (					*,
    __UpperCamelCase    :       Union[str, List[str]] = None ,     __UpperCamelCase    :       str = None ,     __UpperCamelCase    :       Any = dataclasses.MISSING ,     __UpperCamelCase    :       Callable[[], Any] = dataclasses.MISSING ,     __UpperCamelCase    :       dict = None ,     **__UpperCamelCase    :       Dict ,     )							->					dataclasses.Field:







  """simple docstring"""





  if metadata is None:
    # Important, don't use as default param in function signature because dict is mutable and shared across function calls
    SCREAMING_SNAKE_CASE__										=      {}
  if aliases is not None:
    SCREAMING_SNAKE_CASE__										=      aliases
  if help is not None:
    SCREAMING_SNAKE_CASE__										=      help

  return dataclasses.field(metadata=__UpperCamelCase ,     default=__UpperCamelCase ,     default_factory=__UpperCamelCase ,     **__UpperCamelCase	)







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							42




   def __init__(    self			:				int       ,	_lowercase			:				Union[DataClassType, Iterable[DataClassType]]       ,	**_lowercase			:				List[str]      ):



     """simple docstring"""






     if "formatter_class" not in kwargs:
       SCREAMING_SNAKE_CASE__										=      ArgumentDefaultsHelpFormatter
     super().__init__(**_lowercase      )
     if dataclasses.is_dataclass(_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      [dataclass_types]
     SCREAMING_SNAKE_CASE__										=      list(_lowercase      )
     for dtype in self.dataclass_types:
       self._add_dataclass_arguments(_lowercase      )




   @staticmethod
   def 	__a						(    _lowercase			:				ArgumentParser       ,	_lowercase			:				dataclasses.Field      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      f"""--{field.name}"""
     SCREAMING_SNAKE_CASE__										=      field.metadata.copy()
     # field.metadata is not used at all by Data Classes,
     # it is provided as a third-party extension mechanism.
     if isinstance(field.type       ,	_lowercase      ):
       raise RuntimeError(
           """Unresolved type detected, which should have been done with the help of """
           """`typing.get_type_hints` method by default"""      )

     SCREAMING_SNAKE_CASE__										=      kwargs.pop("""aliases"""       ,	[]      )
     if isinstance(_lowercase       ,	_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      [aliases]

     SCREAMING_SNAKE_CASE__										=      getattr(field.type       ,	"""__origin__"""       ,	field.type      )
     if origin_type is Union or (hasattr(_lowercase       ,	"""UnionType"""      ) and isinstance(_lowercase       ,	types.UnionType      )):
       if str not in field.type.__args__ and (
           len(field.type.__args__      ) != 2 or type(_lowercase      ) not in field.type.__args__
       ):
         raise ValueError(
             """Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"""
             """ the argument parser only supports one type per argument."""
             f""" Problem encountered in field '{field.name}'."""      )
       if type(_lowercase      ) not in field.type.__args__:
         # filter `str` in Union
         SCREAMING_SNAKE_CASE__										=      field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
         SCREAMING_SNAKE_CASE__										=      getattr(field.type       ,	"""__origin__"""       ,	field.type      )
       elif bool not in field.type.__args__:
         # filter `NoneType` in Union (except for `Union[bool, NoneType]`)
         SCREAMING_SNAKE_CASE__										=      (
             field.type.__args__[0] if isinstance(_lowercase       ,	field.type.__args__[1]      ) else field.type.__args__[1]
         )
         SCREAMING_SNAKE_CASE__										=      getattr(field.type       ,	"""__origin__"""       ,	field.type      )

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
     SCREAMING_SNAKE_CASE__										=      {}
     if origin_type is Literal or (isinstance(field.type       ,	_lowercase      ) and issubclass(field.type       ,	_lowercase      )):
       if origin_type is Literal:
         SCREAMING_SNAKE_CASE__										=      field.type.__args__
       else:
         SCREAMING_SNAKE_CASE__										=      [x.value for x in field.type]

       SCREAMING_SNAKE_CASE__										=      make_choice_type_function(kwargs["""choices"""]      )

       if field.default is not dataclasses.MISSING:
         SCREAMING_SNAKE_CASE__										=      field.default
       else:
         SCREAMING_SNAKE_CASE__										=      True
     elif field.type is bool or field.type == Optional[bool]:
       # Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
       # We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
       SCREAMING_SNAKE_CASE__										=      copy(_lowercase      )

       # Hack because type=bool in argparse does not behave as we want.
       SCREAMING_SNAKE_CASE__										=      string_to_bool
       if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
         # Default value is False if we have no default when of type bool.
         SCREAMING_SNAKE_CASE__										=      False if field.default is dataclasses.MISSING else field.default
         # This is the value that will get picked if we don't include --field_name in any way
         SCREAMING_SNAKE_CASE__										=      default
         # This tells argparse we accept 0 or 1 value after --field_name
         SCREAMING_SNAKE_CASE__										=      """?"""
         # This is the value that will get picked if we do --field_name (without value)
         SCREAMING_SNAKE_CASE__										=      True
     elif isclass(_lowercase      ) and issubclass(_lowercase       ,	_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      field.type.__args__[0]
       SCREAMING_SNAKE_CASE__										=      """+"""
       if field.default_factory is not dataclasses.MISSING:
         SCREAMING_SNAKE_CASE__										=      field.default_factory()
       elif field.default is dataclasses.MISSING:
         SCREAMING_SNAKE_CASE__										=      True
     else:
       SCREAMING_SNAKE_CASE__										=      field.type
       if field.default is not dataclasses.MISSING:
         SCREAMING_SNAKE_CASE__										=      field.default
       elif field.default_factory is not dataclasses.MISSING:
         SCREAMING_SNAKE_CASE__										=      field.default_factory()
       else:
         SCREAMING_SNAKE_CASE__										=      True
     parser.add_argument(_lowercase       ,	*_lowercase       ,	**_lowercase      )

     # Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
     # Order is important for arguments with the same destination!
     # We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
     # here and we do not need those changes/additional keys.
     if field.default is True and (field.type is bool or field.type == Optional[bool]):
       SCREAMING_SNAKE_CASE__										=      False
       parser.add_argument(f"""--no_{field.name}"""       ,	action="""store_false"""       ,	dest=field.name       ,	**_lowercase      )




   def 	__a						(    self			:				List[str]       ,	_lowercase			:				DataClassType      ):



     """simple docstring"""






     if hasattr(_lowercase       ,	"""_argument_group_name"""      ):
       SCREAMING_SNAKE_CASE__										=      self.add_argument_group(dtype._argument_group_name      )
     else:
       SCREAMING_SNAKE_CASE__										=      self

     try:
       SCREAMING_SNAKE_CASE__										=      get_type_hints(_lowercase      )
     except NameError:
       raise RuntimeError(
           f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
           """removing line of `from __future__ import annotations` which opts in Postponed """
           """Evaluation of Annotations (PEP 563)"""      )
     except TypeError as ex:
       # Remove this block when we drop Python 3.9 support
       if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(_lowercase      ):
         SCREAMING_SNAKE_CASE__										=      """.""".join(map(_lowercase       ,	sys.version_info[:3]      )      )
         raise RuntimeError(
             f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
             """line of `from __future__ import annotations` which opts in union types as """
             """`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To """
             """support Python versions that lower than 3.10, you need to use """
             """`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of """
             """`X | None`."""      ) from ex
       raise

     for field in dataclasses.fields(_lowercase      ):
       if not field.init:
         continue
       SCREAMING_SNAKE_CASE__										=      type_hints[field.name]
       self._parse_dataclass_field(_lowercase       ,	_lowercase      )




   def 	__a						(    self			:				str       ,	_lowercase			:				int=None       ,	_lowercase			:				Optional[Any]=False       ,	_lowercase			:				Union[str, Any]=True       ,	_lowercase			:				Any=None       ,	_lowercase			:				int=None       ,	):



     """simple docstring"""






     if args_file_flag or args_filename or (look_for_args_file and len(sys.argv      )):
       SCREAMING_SNAKE_CASE__										=      []

       if args_filename:
         args_files.append(Path(_lowercase      )      )
       elif look_for_args_file and len(sys.argv      ):
         args_files.append(Path(sys.argv[0]      ).with_suffix(""".args"""      )      )

       # args files specified via command line flag should overwrite default args files so we add them last
       if args_file_flag:
         # Create special parser just to extract the args_file_flag values
         SCREAMING_SNAKE_CASE__										=      ArgumentParser()
         args_file_parser.add_argument(_lowercase       ,	type=_lowercase       ,	action="""append"""      )

         # Use only remaining args for further parsing (remove the args_file_flag)
         SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      args_file_parser.parse_known_args(args=_lowercase      )
         SCREAMING_SNAKE_CASE__										=      vars(_lowercase      ).get(args_file_flag.lstrip("""-"""      )       ,	_lowercase      )

         if cmd_args_file_paths:
           args_files.extend([Path(_lowercase      ) for p in cmd_args_file_paths]      )

       SCREAMING_SNAKE_CASE__										=      []
       for args_file in args_files:
         if args_file.exists():
           file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
       SCREAMING_SNAKE_CASE__										=      file_args + args if args is not None else file_args + sys.argv[1:]
     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      self.parse_known_args(args=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      []
     for dtype in self.dataclass_types:
       SCREAMING_SNAKE_CASE__										=      {f.name for f in dataclasses.fields(_lowercase      ) if f.init}
       SCREAMING_SNAKE_CASE__										=      {k: v for k, v in vars(_lowercase      ).items() if k in keys}
       for k in keys:
         delattr(_lowercase       ,	_lowercase      )
       SCREAMING_SNAKE_CASE__										=      dtype(**_lowercase      )
       outputs.append(_lowercase      )
     if len(namespace.__dict__      ) > 0:
       # additional namespace.
       outputs.append(_lowercase      )
     if return_remaining_strings:
       return (*outputs, remaining_args)
     else:
       if remaining_args:
         raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}"""      )

       return (*outputs,)




   def 	__a						(    self			:				List[str]       ,	_lowercase			:				Dict[str, Any]       ,	_lowercase			:				bool = False      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      set(args.keys()      )
     SCREAMING_SNAKE_CASE__										=      []
     for dtype in self.dataclass_types:
       SCREAMING_SNAKE_CASE__										=      {f.name for f in dataclasses.fields(_lowercase      ) if f.init}
       SCREAMING_SNAKE_CASE__										=      {k: v for k, v in args.items() if k in keys}
       unused_keys.difference_update(inputs.keys()      )
       SCREAMING_SNAKE_CASE__										=      dtype(**_lowercase      )
       outputs.append(_lowercase      )
     if not allow_extra_keys and unused_keys:
       raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(_lowercase      )}"""      )
     return tuple(_lowercase      )




   def 	__a						(    self			:				List[str]       ,	_lowercase			:				str       ,	_lowercase			:				bool = False      ):



     """simple docstring"""






     with open(Path(_lowercase      )       ,	encoding="""utf-8"""      ) as open_json_file:
       SCREAMING_SNAKE_CASE__										=      json.loads(open_json_file.read()      )
     SCREAMING_SNAKE_CASE__										=      self.parse_dict(_lowercase       ,	allow_extra_keys=_lowercase      )
     return tuple(_lowercase      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				str       ,	_lowercase			:				bool = False      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.parse_dict(yaml.safe_load(Path(_lowercase      ).read_text()      )       ,	allow_extra_keys=_lowercase      )
     return tuple(_lowercase      )
