



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE           =  {
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE           =  ["""RobertaTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE           =  [
							    """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RobertaForCausalLM""",
							    """RobertaForMaskedLM""",
							    """RobertaForMultipleChoice""",
							    """RobertaForQuestionAnswering""",
							    """RobertaForSequenceClassification""",
							    """RobertaForTokenClassification""",
							    """RobertaModel""",
							    """RobertaPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE           =  [
							    """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRobertaForCausalLM""",
							    """TFRobertaForMaskedLM""",
							    """TFRobertaForMultipleChoice""",
							    """TFRobertaForQuestionAnswering""",
							    """TFRobertaForSequenceClassification""",
							    """TFRobertaForTokenClassification""",
							    """TFRobertaMainLayer""",
							    """TFRobertaModel""",
							    """TFRobertaPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE           =  [
							    """FlaxRobertaForCausalLM""",
							    """FlaxRobertaForMaskedLM""",
							    """FlaxRobertaForMultipleChoice""",
							    """FlaxRobertaForQuestionAnswering""",
							    """FlaxRobertaForSequenceClassification""",
							    """FlaxRobertaForTokenClassification""",
							    """FlaxRobertaModel""",
							    """FlaxRobertaPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
							from .tokenization_roberta import RobertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_roberta_fast import RobertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roberta import (
														    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RobertaForCausalLM,
														    RobertaForMaskedLM,
														    RobertaForMultipleChoice,
														    RobertaForQuestionAnswering,
														    RobertaForSequenceClassification,
														    RobertaForTokenClassification,
														    RobertaModel,
														    RobertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roberta import (
														    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRobertaForCausalLM,
														    TFRobertaForMaskedLM,
														    TFRobertaForMultipleChoice,
														    TFRobertaForQuestionAnswering,
														    TFRobertaForSequenceClassification,
														    TFRobertaForTokenClassification,
														    TFRobertaMainLayer,
														    TFRobertaModel,
														    TFRobertaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roberta import (
														    FlaxRobertaForCausalLM,
														    FlaxRobertaForMaskedLM,
														    FlaxRobertaForMultipleChoice,
														    FlaxRobertaForQuestionAnswering,
														    FlaxRobertaForSequenceClassification,
														    FlaxRobertaForTokenClassification,
														    FlaxRobertaModel,
														    FlaxRobertaPreTrainedModel,
														)

else:
							import sys

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




from collections import deque

from .hash_table import HashTable

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						def __init__(	self      :			Union[str, Any]	,	*__lowerCamelCase      :			Dict	,	**__lowerCamelCase      :			int )      ->				Optional[int]:
									super().__init__(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Union[str, Any] )      ->				Union[str, Any]:
									A							:					Optional[Any]              =						deque([] ) if self.values[key] is None else self.values[key]
									self.values[key].appendleft(__lowerCamelCase )
									A							:					Dict              =						self.values[key]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				str:
									return (
									    sum(self.charge_factor - len(__lowerCamelCase ) for slot in self.values )
									    / self.size_table
									    * self.charge_factor
									)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			List[Any]=None )      ->				Optional[int]:
									if not (
									    len(self.values[key] ) == self.charge_factor and self.values.count(__lowerCamelCase ) == 0
									):
												return key
									return super()._collision_resolution(__lowerCamelCase	,	__lowerCamelCase )