





"""simple docstring"""





from typing import TYPE_CHECKING

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


lowerCAmelCase__   	=	{
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__   	=	[
						    '''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:
						lowerCAmelCase__   	=	[
						    '''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:
						lowerCAmelCase__   	=	[
						    '''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

						lowerCAmelCase__   	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)











import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union

@dataclass
class     _A       :
       UpperCamelCase__  :    Optional[Union[str, Path]]											=						None
       UpperCamelCase__  :    bool											=						False
       UpperCamelCase__  :    bool											=						False
       UpperCamelCase__  :    bool											=						False
       UpperCamelCase__  :    Optional[Dict]											=						None
       UpperCamelCase__  :    Optional[str]											=						None
       UpperCamelCase__  :    bool											=						False
       UpperCamelCase__  :    bool											=						False
       UpperCamelCase__  :    bool											=						False
       UpperCamelCase__  :    bool											=						True
       UpperCamelCase__  :    Optional[int]											=						None
       UpperCamelCase__  :    int											=						1
       UpperCamelCase__  :    Optional[Union[str, bool]]											=						None
       UpperCamelCase__  :    bool											=						False
       UpperCamelCase__  :    Optional[Dict]											=						None
       UpperCamelCase__  :    Optional[str]											=						None



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


           '''simple docstring'''

           return self.__class__(**{k: copy.deepcopy(__SCREAMING_SNAKE_CASE) for k, v in self.__dict__.items()})





