


"""simple docstring"""
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							:    List[Any]		=    {
    '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							:    Tuple		=    ['RobertaTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __SCREAMING_SNAKE_CASE							:    List[Any]		=    [
        '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							:    List[str]		=    [
        '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							:    Optional[int]		=    [
        '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							:    str		=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







"""simple docstring"""
from __future__ import annotations

from collections import deque





class      __A     :





							'''simple docstring'''

							def __init__(				self							:      List[Any]		,       UpperCAmelCase_							:      list[str]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		[]
														self.adlist.append(
														    {"""value""": """""", """next_states""": [], """fail_state""": 0, """output""": []}       )

														for keyword in keywords:
																					self.add_keyword(UpperCAmelCase_       )
														self.set_fail_transitions()



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      str       )					->int | None:



														"""simple docstring"""



														for state in self.adlist[current_state]["next_states"]:
																					if char == self.adlist[state]["value"]:
																												return state
														return None



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      str       )					->None:



														"""simple docstring"""



														snake_case_           =		0
														for character in keyword:
																					snake_case_           =		self.find_next_state(UpperCAmelCase_		,       UpperCAmelCase_       )
																					if next_state is None:
																												self.adlist.append(
																												    {
																												        """value""": character,
																												        """next_states""": [],
																												        """fail_state""": 0,
																												        """output""": [],
																												    }       )
																												self.adlist[current_state]["next_states"].append(len(self.adlist       ) - 1       )
																												snake_case_           =		len(self.adlist       ) - 1
																					else:
																												snake_case_           =		next_state
														self.adlist[current_state]["output"].append(UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->None:



														"""simple docstring"""



														snake_case_           =		deque()
														for node in self.adlist[0]["next_states"]:
																					q.append(UpperCAmelCase_       )
																					snake_case_           =		0
														while q:
																					snake_case_           =		q.popleft()
																					for child in self.adlist[r]["next_states"]:
																												q.append(UpperCAmelCase_       )
																												snake_case_           =		self.adlist[r]["""fail_state"""]
																												while (
																												    self.find_next_state(UpperCAmelCase_		,       self.adlist[child]["""value"""]       ) is None
																												    and state != 0
																												):
																																			snake_case_           =		self.adlist[state]["""fail_state"""]
																												snake_case_           =		self.find_next_state(
																												    UpperCAmelCase_		,       self.adlist[child]["""value"""]       )
																												if self.adlist[child]["fail_state"] is None:
																																			snake_case_           =		0
																												snake_case_           =		(
																												    self.adlist[child]["""output"""]
																												    + self.adlist[self.adlist[child]["""fail_state"""]]["""output"""]
																												)



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      str       )					->dict[str, list[int]]:



														"""simple docstring"""



														snake_case_           =		{}  # returns a dict with keywords and list of its occurrences
														snake_case_           =		0
														for i in range(len(UpperCAmelCase_       )       ):
																					while (
																					    self.find_next_state(UpperCAmelCase_		,       string[i]       ) is None
																					    and current_state != 0
																					):
																												snake_case_           =		self.adlist[current_state]["""fail_state"""]
																					snake_case_           =		self.find_next_state(UpperCAmelCase_		,       string[i]       )
																					if next_state is None:
																												snake_case_           =		0
																					else:
																												snake_case_           =		next_state
																												for key in self.adlist[current_state]["output"]:
																																			if key not in result:
																																										snake_case_           =		[]
																																			result[key].append(i - len(UpperCAmelCase_       ) + 1       )
														return result


if __name__ == "__main__":
				import doctest

				doctest.testmod()



