




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

__SCREAMING_SNAKE_CASE 						=TypeVar("""KEY""")
__SCREAMING_SNAKE_CASE 						=TypeVar("""VAL""")







@dataclass(frozen=UpperCAmelCase_    ,  slots=UpperCAmelCase_)
class  __magic_name__						(  Generic[KEY, VAL]):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							KEY
				SCREAMING_SNAKE_CASE__					:							VAL







class  __magic_name__						(  _Item):
				'''simple docstring'''







				def __init__(							self:   int		):
											super().__init__(_snake_case		,					_snake_case		)





				def __bool__(							self:   List[str]		):
											return False






__SCREAMING_SNAKE_CASE 						=_DeletedItem()







class  __magic_name__						(  MutableMapping[KEY, VAL]):
				'''simple docstring'''







				def __init__(							self:   List[Any]		,					_lowerCamelCase:   int = 8		,					_lowerCamelCase:   float = 0.75		):
											SCREAMING_SNAKE_CASE_   	=    initial_block_size
											SCREAMING_SNAKE_CASE_   	=    [None] * initial_block_size
											assert 0.0 < capacity_factor < 1.0
											SCREAMING_SNAKE_CASE_   	=    capacity_factor
											SCREAMING_SNAKE_CASE_   	=    0


				def     _A							(							self:   Dict		,					_lowerCamelCase:   KEY		):
											return hash(_snake_case		) % len(self._buckets		)


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   int		):
											return (ind + 1) % len(self._buckets		)


				def     _A							(							self:   Any		,					_lowerCamelCase:   int		,					_lowerCamelCase:   KEY		,					_lowerCamelCase:   VAL		):
											SCREAMING_SNAKE_CASE_   	=    self._buckets[ind]
											if not stored:
																		SCREAMING_SNAKE_CASE_   	=    _Item(_snake_case		,					_snake_case		)
																		self._len += 1
																		return True
											elif stored.key == key:
																		SCREAMING_SNAKE_CASE_   	=    _Item(_snake_case		,					_snake_case		)
																		return True
											else:
																		return False


				def     _A							(							self:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    len(self._buckets		) * self._capacity_factor
											return len(self		) >= int(_snake_case		)


				def     _A							(							self:   Optional[int]		):


											if len(self._buckets		) <= self._initial_block_size:
																		return False
											SCREAMING_SNAKE_CASE_   	=    len(self._buckets		) * self._capacity_factor / 2
											return len(self		) < limit


				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   int		):
											SCREAMING_SNAKE_CASE_   	=    self._buckets
											SCREAMING_SNAKE_CASE_   	=    [None] * new_size
											SCREAMING_SNAKE_CASE_   	=    0
											for item in old_buckets:
																		if item:
																									self._add_item(item.key		,					item.val		)


				def     _A							(							self:   Optional[Any]		):
											self._resize(len(self._buckets		) * 2		)


				def     _A							(							self:   Dict		):
											self._resize(len(self._buckets		) // 2		)


				def     _A							(							self:   str		,					_lowerCamelCase:   KEY		):
											SCREAMING_SNAKE_CASE_   	=    self._get_bucket_index(_snake_case		)
											for _ in range(len(self._buckets		)		):
																		yield ind
																		SCREAMING_SNAKE_CASE_   	=    self._get_next_ind(_snake_case		)


				def     _A							(							self:   Any		,					_lowerCamelCase:   KEY		,					_lowerCamelCase:   VAL		):


											for ind in self._iterate_buckets(_snake_case		):
																		if self._try_set(_snake_case		,					_snake_case		,					_snake_case		):
																									break


				def __setitem__(							self:   Union[str, Any]		,					_lowerCamelCase:   KEY		,					_lowerCamelCase:   VAL		):


											if self._is_full():
																		self._size_up()

											self._add_item(_snake_case		,					_snake_case		)


				def __delitem__(							self:   List[str]		,					_lowerCamelCase:   KEY		):


											for ind in self._iterate_buckets(_snake_case		):
																		SCREAMING_SNAKE_CASE_   	=    self._buckets[ind]
																		if item is None:
																									raise KeyError(_snake_case		)
																		if item is _deleted:
																									continue
																		if item.key == key:
																									SCREAMING_SNAKE_CASE_   	=    _deleted
																									self._len -= 1
																									break
											if self._is_sparse():
																		self._size_down()


				def __getitem__(							self:   List[Any]		,					_lowerCamelCase:   KEY		):


											for ind in self._iterate_buckets(_snake_case		):
																		SCREAMING_SNAKE_CASE_   	=    self._buckets[ind]
																		if item is None:
																									break
																		if item is _deleted:
																									continue
																		if item.key == key:
																									return item.val
											raise KeyError(_snake_case		)


				def __len__(							self:   Any		):
											return self._len


				def __iter__(							self:   List[str]		):
											yield from (item.key for item in self._buckets if item)





				def __repr__(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    ''' ,'''.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_torch_available


__SCREAMING_SNAKE_CASE 						={
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE 						=[
			    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """XLMRobertaXLForCausalLM""",
			    """XLMRobertaXLForMaskedLM""",
			    """XLMRobertaXLForMultipleChoice""",
			    """XLMRobertaXLForQuestionAnswering""",
			    """XLMRobertaXLForSequenceClassification""",
			    """XLMRobertaXLForTokenClassification""",
			    """XLMRobertaXLModel""",
			    """XLMRobertaXLPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta_xl import (
			    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaXLConfig,
			    XLMRobertaXLOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaXLForCausalLM,
						    XLMRobertaXLForMaskedLM,
						    XLMRobertaXLForMultipleChoice,
						    XLMRobertaXLForQuestionAnswering,
						    XLMRobertaXLForSequenceClassification,
						    XLMRobertaXLForTokenClassification,
						    XLMRobertaXLModel,
						    XLMRobertaXLPreTrainedModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE 						=_LazyModule(__name__, globals()["""__file__"""], _import_structure)


