




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

lowerCamelCase__					:  Any      							=				TypeVar('KEY')
lowerCamelCase__					:  str      							=				TypeVar('VAL')





@dataclass(frozen=_SCREAMING_SNAKE_CASE			,  slots=_SCREAMING_SNAKE_CASE   )
class 			lowerCamelCase_				( Generic[KEY, VAL]   ):


 '''simple docstring'''







 lowercase_							       =       42
 lowercase_							       =       42





class 			lowerCamelCase_				( _Item   ):


 '''simple docstring'''







 def __init__(				self    :						Optional[int]		):
       super().__init__(_lowerCAmelCase							,							_lowerCAmelCase		)




 def __bool__(				self    :						Tuple		):
       return False


lowerCamelCase__					:  str      							=				_DeletedItem()





class 			lowerCamelCase_				( MutableMapping[KEY, VAL]   ):


 '''simple docstring'''







 def __init__(				self    :						Dict							,							_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  lowerCAmelCase_    (				self    :						int							,							_lowerCAmelCase    :						KEY		):
       return hash(_lowerCAmelCase		) % len(self._buckets		)



 def  lowerCAmelCase_    (				self    :						Optional[int]							,							_lowerCAmelCase    :						int		):
       return (ind + 1) % len(self._buckets		)



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



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     len(self._buckets		) * self._capacity_factor
       return len(self		) >= int(_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Tuple		):
       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  lowerCAmelCase_    (				self    :						Optional[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  lowerCAmelCase_    (				self    :						List[Any]		):
       self._resize(len(self._buckets		) * 2		)



 def  lowerCAmelCase_    (				self    :						int		):
       self._resize(len(self._buckets		) // 2		)



 def  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						KEY		):
       SCREAMING_SNAKE_CASE_      		=     self._get_bucket_index(_lowerCAmelCase		)
       for _ in range(len(self._buckets		)		):
             yield ind
             SCREAMING_SNAKE_CASE_      		=     self._get_next_ind(_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						KEY							,							_lowerCAmelCase    :						VAL		):
       for ind in self._iterate_buckets(_lowerCAmelCase		):
             if self._try_set(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		):
                   break



 def __setitem__(				self    :						Optional[Any]							,							_lowerCAmelCase    :						KEY							,							_lowerCAmelCase    :						VAL		):
       if self._is_full():
             self._size_up()

       self._add_item(_lowerCAmelCase							,							_lowerCAmelCase		)



 def __delitem__(				self    :						Any							,							_lowerCAmelCase    :						KEY		):
       for ind in self._iterate_buckets(_lowerCAmelCase		):
             SCREAMING_SNAKE_CASE_      		=     self._buckets[ind]
             if item is None:
                   raise KeyError(_lowerCAmelCase		)
             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    :						Dict							,							_lowerCAmelCase    :						KEY		):
       for ind in self._iterate_buckets(_lowerCAmelCase		):
             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(_lowerCAmelCase		)



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



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




 def __repr__(				self    :						Dict		):
       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_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase							:   int						=						{
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   List[Any]						=						[
				    "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RobertaPreLayerNormForCausalLM",
				    "RobertaPreLayerNormForMaskedLM",
				    "RobertaPreLayerNormForMultipleChoice",
				    "RobertaPreLayerNormForQuestionAnswering",
				    "RobertaPreLayerNormForSequenceClassification",
				    "RobertaPreLayerNormForTokenClassification",
				    "RobertaPreLayerNormModel",
				    "RobertaPreLayerNormPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   Optional[int]						=						[
				    "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRobertaPreLayerNormForCausalLM",
				    "TFRobertaPreLayerNormForMaskedLM",
				    "TFRobertaPreLayerNormForMultipleChoice",
				    "TFRobertaPreLayerNormForQuestionAnswering",
				    "TFRobertaPreLayerNormForSequenceClassification",
				    "TFRobertaPreLayerNormForTokenClassification",
				    "TFRobertaPreLayerNormMainLayer",
				    "TFRobertaPreLayerNormModel",
				    "TFRobertaPreLayerNormPreTrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   str						=						[
				    "FlaxRobertaPreLayerNormForCausalLM",
				    "FlaxRobertaPreLayerNormForMaskedLM",
				    "FlaxRobertaPreLayerNormForMultipleChoice",
				    "FlaxRobertaPreLayerNormForQuestionAnswering",
				    "FlaxRobertaPreLayerNormForSequenceClassification",
				    "FlaxRobertaPreLayerNormForTokenClassification",
				    "FlaxRobertaPreLayerNormModel",
				    "FlaxRobertaPreLayerNormPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roberta_prelayernorm import (
				    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    RobertaPreLayerNormConfig,
				    RobertaPreLayerNormOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roberta_prelayernorm import (
								    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RobertaPreLayerNormForCausalLM,
								    RobertaPreLayerNormForMaskedLM,
								    RobertaPreLayerNormForMultipleChoice,
								    RobertaPreLayerNormForQuestionAnswering,
								    RobertaPreLayerNormForSequenceClassification,
								    RobertaPreLayerNormForTokenClassification,
								    RobertaPreLayerNormModel,
								    RobertaPreLayerNormPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roberta_prelayernorm import (
								    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRobertaPreLayerNormForCausalLM,
								    TFRobertaPreLayerNormForMaskedLM,
								    TFRobertaPreLayerNormForMultipleChoice,
								    TFRobertaPreLayerNormForQuestionAnswering,
								    TFRobertaPreLayerNormForSequenceClassification,
								    TFRobertaPreLayerNormForTokenClassification,
								    TFRobertaPreLayerNormMainLayer,
								    TFRobertaPreLayerNormModel,
								    TFRobertaPreLayerNormPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roberta_prelayernorm import (
								    FlaxRobertaPreLayerNormForCausalLM,
								    FlaxRobertaPreLayerNormForMaskedLM,
								    FlaxRobertaPreLayerNormForMultipleChoice,
								    FlaxRobertaPreLayerNormForQuestionAnswering,
								    FlaxRobertaPreLayerNormForSequenceClassification,
								    FlaxRobertaPreLayerNormForTokenClassification,
								    FlaxRobertaPreLayerNormModel,
								    FlaxRobertaPreLayerNormPreTrainedModel,
								)

else:
				import sys

				_lowercase							:   List[Any]						=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



