





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


_SCREAMING_SNAKE_CASE					=						NewType("""DataClass""", Any)
_SCREAMING_SNAKE_CASE					=						NewType("""DataClassType""", Any)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	if isinstance(__a				,	__a				):
		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__  (      __a				):
	snake_case_						:     str     =		{str(__a				): choice for choice in choices}
	return lambda __a				: str_to_choice.get(__a				,	__a				)


def 							SCREAMING_SNAKE_CASE__  (      *,
    __a = None				,	__a = None				,	__a = dataclasses.MISSING				,	__a = dataclasses.MISSING				,	__a = None				,	**__a				,	):
	if metadata is None:
		# Important, don't use as default param in function signature because dict is mutable and shared across function calls
		snake_case_						:     List[str]     =		{}
	if aliases is not None:
		snake_case_						:     Any     =		aliases
	if help is not None:
		snake_case_						:     Optional[Any]     =		help

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


class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     Iterable[DataClassType]






						def __init__( self : Optional[int]						,				_A : Union[DataClassType, Iterable[DataClassType]]						,				**_A : int		)							->      Any:







							"""simple docstring"""


							if "formatter_class" not in kwargs:
								snake_case_						:     int     =		ArgumentDefaultsHelpFormatter
							super().__init__(**_A		)
							if dataclasses.is_dataclass(_A		):
								snake_case_						:     Optional[Any]     =		[dataclass_types]
							snake_case_						:     int     =		list(_A		)
							for dtype in self.dataclass_types:
								self._add_dataclass_arguments(_A		)






						@staticmethod
						def UpperCAmelCase_			( _A : ArgumentParser						,				_A : dataclasses.Field		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     Any     =		F"""--{field.name}"""
							snake_case_						:     Dict     =		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						,				_A		):
								raise RuntimeError(
								    'Unresolved type detected, which should have been done with the help of '
								    '`typing.get_type_hints` method by default'		)

							snake_case_						:     List[str]     =		kwargs.pop('aliases'						,				[]		)
							if isinstance(_A						,				_A		):
								snake_case_						:     Dict     =		[aliases]

							snake_case_						:     Optional[int]     =		getattr(field.type						,				'__origin__'						,				field.type		)
							if origin_type is Union or (hasattr(_A						,				'UnionType'		) and isinstance(_A						,				types.UnionType		)):
								if str not in field.type.__args__ and (
								    len(field.type.__args__		) != 2 or type(_A		) 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(_A		) not in field.type.__args__:
									# filter `str` in Union
									snake_case_						:     Any     =		field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
									snake_case_						:     Union[str, Any]     =		getattr(field.type						,				'__origin__'						,				field.type		)
								elif bool not in field.type.__args__:
									# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
									snake_case_						:     Tuple     =		(
									    field.type.__args__[0] if isinstance(_A						,				field.type.__args__[1]		) else field.type.__args__[1]
									)
									snake_case_						:     Tuple     =		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)
							snake_case_						:     Tuple     =		{}
							if origin_type is Literal or (isinstance(field.type						,				_A		) and issubclass(field.type						,				_A		)):
								if origin_type is Literal:
									snake_case_						:     int     =		field.type.__args__
								else:
									snake_case_						:     List[Any]     =		[x.value for x in field.type]

								snake_case_						:     Any     =		make_choice_type_function(kwargs['choices']		)

								if field.default is not dataclasses.MISSING:
									snake_case_						:     str     =		field.default
								else:
									snake_case_						:     Dict     =		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
								snake_case_						:     str     =		copy(_A		)

								# Hack because type=bool in argparse does not behave as we want.
								snake_case_						:     Optional[Any]     =		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.
									snake_case_						:     Any     =		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
									snake_case_						:     Any     =		default
									# This tells argparse we accept 0 or 1 value after --field_name
									snake_case_						:     List[str]     =		'?'
									# This is the value that will get picked if we do --field_name (without value)
									snake_case_						:     Optional[int]     =		True
							elif isclass(_A		) and issubclass(_A						,				_A		):
								snake_case_						:     str     =		field.type.__args__[0]
								snake_case_						:     Optional[Any]     =		'+'
								if field.default_factory is not dataclasses.MISSING:
									snake_case_						:     str     =		field.default_factory()
								elif field.default is dataclasses.MISSING:
									snake_case_						:     Tuple     =		True
							else:
								snake_case_						:     Dict     =		field.type
								if field.default is not dataclasses.MISSING:
									snake_case_						:     Union[str, Any]     =		field.default
								elif field.default_factory is not dataclasses.MISSING:
									snake_case_						:     Any     =		field.default_factory()
								else:
									snake_case_						:     Optional[int]     =		True
							parser.add_argument(_A						,				*_A						,				**_A		)

							# 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]):
								snake_case_						:     str     =		False
								parser.add_argument(F"""--no_{field.name}"""						,				action='store_false'						,				dest=field.name						,				**_A		)






						def UpperCAmelCase_			( self : int						,				_A : DataClassType		)							->      Union[str, Any]:







							"""simple docstring"""


							if hasattr(_A						,				'_argument_group_name'		):
								snake_case_						:     Union[str, Any]     =		self.add_argument_group(dtype._argument_group_name		)
							else:
								snake_case_						:     List[str]     =		self

							try:
								snake_case_						:     Dict[str, type]     =		get_type_hints(_A		)
							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(_A		):
									snake_case_						:     Optional[int]     =		'.'.join(map(_A						,				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(_A		):
								if not field.init:
									continue
								snake_case_						:     int     =		type_hints[field.name]
								self._parse_dataclass_field(_A						,				_A		)






						def UpperCAmelCase_			( self : Any						,				_A : List[str]=None						,				_A : List[str]=False						,				_A : Any=True						,				_A : List[str]=None						,				_A : str=None						,				)							->      Tuple[DataClass, ...]:







							"""simple docstring"""


							if args_file_flag or args_filename or (look_for_args_file and len(sys.argv		)):
								snake_case_						:     Union[str, Any]     =		[]

								if args_filename:
									args_files.append(Path(_A		)		)
								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
									snake_case_						:     List[str]     =		ArgumentParser()
									args_file_parser.add_argument(_A						,				type=_A						,				action='append'		)

									# Use only remaining args for further parsing (remove the args_file_flag)
									snake_case_						:     Tuple     =		args_file_parser.parse_known_args(args=_A		)
									snake_case_						:     Dict     =		vars(_A		).get(args_file_flag.lstrip('-'		)						,				_A		)

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

								snake_case_						:     List[str]     =		[]
								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
								snake_case_						:     List[str]     =		file_args + args if args is not None else file_args + sys.argv[1:]
							snake_case_						:     Tuple     =		self.parse_known_args(args=_A		)
							snake_case_						:     Optional[Any]     =		[]
							for dtype in self.dataclass_types:
								snake_case_						:     List[Any]     =		{f.name for f in dataclasses.fields(_A		) if f.init}
								snake_case_						:     List[str]     =		{k: v for k, v in vars(_A		).items() if k in keys}
								for k in keys:
									delattr(_A						,				_A		)
								snake_case_						:     Union[str, Any]     =		dtype(**_A		)
								outputs.append(_A		)
							if len(namespace.__dict__		) > 0:
								# additional namespace.
								outputs.append(_A		)
							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 UpperCAmelCase_			( self : int						,				_A : Dict[str, Any]						,				_A : bool = False		)							->      Tuple[DataClass, ...]:







							"""simple docstring"""


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






						def UpperCAmelCase_			( self : str						,				_A : str						,				_A : bool = False		)							->      Tuple[DataClass, ...]:







							"""simple docstring"""


							with open(Path(_A		)						,				encoding='utf-8'		) as open_json_file:
								snake_case_						:     Tuple     =		json.loads(open_json_file.read()		)
							snake_case_						:     Optional[Any]     =		self.parse_dict(_A						,				allow_extra_keys=_A		)
							return tuple(_A		)






						def UpperCAmelCase_			( self : Dict						,				_A : str						,				_A : bool = False		)							->      Tuple[DataClass, ...]:







							"""simple docstring"""


							snake_case_						:     Tuple     =		self.parse_dict(yaml.safe_load(Path(_A		).read_text()		)						,				allow_extra_keys=_A		)
							return tuple(_A		)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """XLMForMultipleChoice""",
						    """XLMForQuestionAnswering""",
						    """XLMForQuestionAnsweringSimple""",
						    """XLMForSequenceClassification""",
						    """XLMForTokenClassification""",
						    """XLMModel""",
						    """XLMPreTrainedModel""",
						    """XLMWithLMHeadModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFXLMForMultipleChoice""",
						    """TFXLMForQuestionAnsweringSimple""",
						    """TFXLMForSequenceClassification""",
						    """TFXLMForTokenClassification""",
						    """TFXLMMainLayer""",
						    """TFXLMModel""",
						    """TFXLMPreTrainedModel""",
						    """TFXLMWithLMHeadModel""",
						]


if TYPE_CHECKING:
						from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
						from .tokenization_xlm import XLMTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm import (
												    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMForMultipleChoice,
												    XLMForQuestionAnswering,
												    XLMForQuestionAnsweringSimple,
												    XLMForSequenceClassification,
												    XLMForTokenClassification,
												    XLMModel,
												    XLMPreTrainedModel,
												    XLMWithLMHeadModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_xlm import (
												    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFXLMForMultipleChoice,
												    TFXLMForQuestionAnsweringSimple,
												    TFXLMForSequenceClassification,
												    TFXLMForTokenClassification,
												    TFXLMMainLayer,
												    TFXLMModel,
												    TFXLMPreTrainedModel,
												    TFXLMWithLMHeadModel,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE 		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

