





from typing import TYPE_CHECKING

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


A_												=     {
    "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:
						A_												=     ["LayoutLMv3TokenizerFast"]

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

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

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_												=     ["LayoutLMv3FeatureExtractor"]
						A_												=     ["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

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








from __future__ import annotations

class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__    ,		lowerCamelCase__       =       text, pattern
										lowerCamelCase__    ,		lowerCamelCase__       =       len(_lowerCAmelCase	), len(_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):

										for i in range(self.patLen - 1	,  -1	,  -1	):
															if char == self.pattern[i]:
																				return i
										return -1





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):

										for i in range(self.patLen - 1	,  -1	,  -1	):
															if self.pattern[i] != self.text[current_pos + i]:
																				return current_pos + i
										return -1






					def 					__magic_name__ ( self	):
										# searches pattern in text and returns index positions
										lowerCamelCase__       =       []
										for i in range(self.textLen - self.patLen + 1	):
															lowerCamelCase__       =       self.mismatch_in_text(_lowerCAmelCase	)
															if mismatch_index == -1:
																				positions.append(_lowerCAmelCase	)
															else:
																				lowerCamelCase__       =       self.match_in_pattern(self.text[mismatch_index]	)
																				lowerCamelCase__       =       (
																				    mismatch_index - match_index
																				)  # shifting index lgtm [py/multiple-definition]
										return positions

A_												=     "ABAABA"
A_												=     "AB"
A_												=     BoyerMooreSearch(text, pattern)
A_												=     bms.bad_character_heuristic()

if len(positions) == 0:
						print("No match found")
else:
						print("Pattern found in following positions: ")
						print(positions)

