





from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

UpperCAmelCase						:   Union[str, Any]			  =     TypeVar('''KEY''')
UpperCAmelCase						:   List[Any]			  =     TypeVar('''VAL''')





@dataclass(frozen=snake_case__				, slots=snake_case__   )
class 						_A(					Generic[KEY, VAL]   ):


				"""simple docstring"""


				UpperCamelCase    :  KEY
				UpperCamelCase    :  VAL





class 						_A(					_Item   ):


				"""simple docstring"""


				def __init__(			self     ):
									super().__init__(_A     ,					_A     )





				def __bool__(			self     ):
									return False




UpperCAmelCase						:   List[Any]			  =     _DeletedItem()





class 						_A(					MutableMapping[KEY, VAL]   ):


				"""simple docstring"""


				def __init__(			self     ,					_A = 8     ,					_A = 0.7_5     ):
									__A	:						List[Any]       =				initial_block_size
									__A	:						list[_Item | None]       =				[None] * initial_block_size
									assert 0.0 < capacity_factor < 1.0
									__A	:						Tuple       =				capacity_factor
									__A	:						int       =				0



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									return hash(_A     ) % len(self._buckets     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									return (ind + 1) % len(self._buckets     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						Tuple       =				self._buckets[ind]
									if not stored:
														__A	:						List[Any]       =				_Item(_A     ,					_A     )
														self._len += 1
														return True
									elif stored.key == key:
														__A	:						Dict       =				_Item(_A     ,					_A     )
														return True
									else:
														return False



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[Any]       =				len(self._buckets     ) * self._capacity_factor
									return len(self     ) >= int(_A     )



				def 					UpperCAmelCase_		(			self     ):
									if len(self._buckets     ) <= self._initial_block_size:
														return False
									__A	:						str       =				len(self._buckets     ) * self._capacity_factor / 2
									return len(self     ) < limit



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						Any       =				self._buckets
									__A	:						str       =				[None] * new_size
									__A	:						Dict       =				0
									for item in old_buckets:
														if item:
																			self._add_item(item.key     ,					item.val     )



				def 					UpperCAmelCase_		(			self     ):
									self._resize(len(self._buckets     ) * 2     )



				def 					UpperCAmelCase_		(			self     ):
									self._resize(len(self._buckets     ) // 2     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						List[str]       =				self._get_bucket_index(_A     )
									for _ in range(len(self._buckets     )     ):
														yield ind
														__A	:						int       =				self._get_next_ind(_A     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									for ind in self._iterate_buckets(_A     ):
														if self._try_set(_A     ,					_A     ,					_A     ):
																			break



				def __setitem__(			self     ,					_A     ,					_A     ):
									if self._is_full():
														self._size_up()

									self._add_item(_A     ,					_A     )



				def __delitem__(			self     ,					_A     ):
									for ind in self._iterate_buckets(_A     ):
														__A	:						Union[str, Any]       =				self._buckets[ind]
														if item is None:
																			raise KeyError(_A     )
														if item is _deleted:
																			continue
														if item.key == key:
																			__A	:						Optional[Any]       =				_deleted
																			self._len -= 1
																			break
									if self._is_sparse():
														self._size_down()



				def __getitem__(			self     ,					_A     ):
									for ind in self._iterate_buckets(_A     ):
														__A	:						Optional[int]       =				self._buckets[ind]
														if item is None:
																			break
														if item is _deleted:
																			continue
														if item.key == key:
																			return item.val
									raise KeyError(_A     )



				def __len__(			self     ):
									return self._len



				def __iter__(			self     ):
									yield from (item.key for item in self._buckets if item)





				def __repr__(			self     ):
									__A	:						str       =				' ,'.join(
									    F"""{item.key}: {item.val}""" for item in self._buckets if item     )
									return F"""HashMap({val_string})"""











from typing import TYPE_CHECKING

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


UpperCAmelCase						:   Optional[int]			  =     {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Union[str, Any]			  =     [
				    '''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:
				UpperCAmelCase						:   Optional[Any]			  =     [
				    '''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

				UpperCAmelCase						:   str			  =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




