

from typing import TYPE_CHECKING

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


__A			:					int						=			{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    pass

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __A			:					Union[str, Any]						=			[
        'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'RoCBertForCausalLM',
        'RoCBertForMaskedLM',
        'RoCBertForMultipleChoice',
        'RoCBertForPreTraining',
        'RoCBertForQuestionAnswering',
        'RoCBertForSequenceClassification',
        'RoCBertForTokenClassification',
        'RoCBertLayer',
        'RoCBertModel',
        'RoCBertPreTrainedModel',
        'load_tf_weights_in_roc_bert',
    ]

if TYPE_CHECKING:
    from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
    from .tokenization_roc_bert import RoCBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        raise OptionalDependencyNotAvailable()

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roc_bert import (
            ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            RoCBertForCausalLM,
            RoCBertForMaskedLM,
            RoCBertForMultipleChoice,
            RoCBertForPreTraining,
            RoCBertForQuestionAnswering,
            RoCBertForSequenceClassification,
            RoCBertForTokenClassification,
            RoCBertLayer,
            RoCBertModel,
            RoCBertPreTrainedModel,
            load_tf_weights_in_roc_bert,
        )


else:
    import sys

    __A			:					Tuple						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


from __future__ import annotations





def 	__a     (      A__			:  list[int | str]       ):
    create_state_space_tree(A__			,  []			,  0			,  [0 for i in range(len(A__       )       )]       )





def 	__a     (      A__			:  list[int | str]			,  A__			:  list[int | str]			,  A__			:  int			,  A__			:  list[int]			,  ):

    if index == len(A__       ):
        print(A__       )
        return

    for i in range(len(A__       )       ):
        if not index_used[i]:
            current_sequence.append(sequence[i]       )
            SCREAMING_SNAKE_CASE										=   True
            create_state_space_tree(A__			,  A__			,  index + 1			,  A__       )
            current_sequence.pop()
            SCREAMING_SNAKE_CASE										=   False
__A			:					list[int | str]						=			[3, 1, 2, 4]
generate_all_permutations(sequence)

__A			:					list[int | str]						=			["A", "B", "C"]





generate_all_permutations(sequence_a)