


'''simple docstring'''
from __future__ import annotations

from collections import deque
class      lowercase			:

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :list[dict]							      =				[]
							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					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				0
							for character in keyword:
												UpperCamelCase__     :Dict							      =				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							)
																	UpperCamelCase__     :str							      =				len(self.adlist							) - 1
												else:
																	UpperCamelCase__     :Dict							      =				next_state
							self.adlist[current_state]["output"].append(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :deque							      =				deque()
							for node in self.adlist[0]["next_states"]:
												q.append(UpperCamelCase_							)
												UpperCamelCase__     :int							      =				0
							while q:
												UpperCamelCase__     :List[Any]							      =				q.popleft()
												for child in self.adlist[r]["next_states"]:
																	q.append(UpperCamelCase_							)
																	UpperCamelCase__     :str							      =				self.adlist[r]['''fail_state''']
																	while (
																	    self.find_next_state(UpperCamelCase_					,     self.adlist[child]['''value''']							) is None
																	    and state != 0
																	):
																						UpperCamelCase__     :int							      =				self.adlist[state]['''fail_state''']
																	UpperCamelCase__     :Dict							      =				self.find_next_state(
																	    UpperCamelCase_					,     self.adlist[child]['''value''']							)
																	if self.adlist[child]["fail_state"] is None:
																						UpperCamelCase__     :List[Any]							      =				0
																	UpperCamelCase__     :str							      =				(
																	    self.adlist[child]['''output''']
																	    + self.adlist[self.adlist[child]['''fail_state''']]['''output''']
																	)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


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


if __name__ == "__main__":
		import doctest

		doctest.testmod()



'''simple docstring'''
from typing import TYPE_CHECKING

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


__snake_case							   =  {
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  ['''LayoutLMv3TokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  [
		    '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LayoutLMv3ForQuestionAnswering''',
		    '''LayoutLMv3ForSequenceClassification''',
		    '''LayoutLMv3ForTokenClassification''',
		    '''LayoutLMv3Model''',
		    '''LayoutLMv3PreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  [
		    '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFLayoutLMv3ForQuestionAnswering''',
		    '''TFLayoutLMv3ForSequenceClassification''',
		    '''TFLayoutLMv3ForTokenClassification''',
		    '''TFLayoutLMv3Model''',
		    '''TFLayoutLMv3PreTrainedModel''',
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  ['''LayoutLMv3FeatureExtractor''']
		__snake_case							   =  ['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
		from .configuration_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    LayoutLMvaConfig,
		    LayoutLMvaOnnxConfig,
		)
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_layoutlmva import (
				    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMvaForQuestionAnswering,
				    TFLayoutLMvaForSequenceClassification,
				    TFLayoutLMvaForTokenClassification,
				    TFLayoutLMvaModel,
				    TFLayoutLMvaPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
				from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
		import sys

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