
'''simple docstring'''





from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers

class     a :




				def __init__(				self						:					int,     SCREAMING_SNAKE_CASE_						:					Tuple   ):
										snake_case							:  Optional[int]			=     vocab
										snake_case							:  Dict			=     normalizers.BertNormalizer(
										    clean_text=SCREAMING_SNAKE_CASE_,     handle_chinese_chars=SCREAMING_SNAKE_CASE_,     strip_accents=SCREAMING_SNAKE_CASE_,     lowercase=SCREAMING_SNAKE_CASE_,     )
										try:
																import rjieba
										except ImportError:
																raise ImportError(
																    '''You need to install rjieba to use RoFormerTokenizer. '''
																    '''See https://pypi.org/project/rjieba/ for installation.'''   )
										snake_case							:  Dict			=     rjieba




				def 						__snake_case				(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					NormalizedString   ):
										snake_case							:  Optional[int]			=     []

										# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
										for token, start, end in self.jieba.tokenize(str(SCREAMING_SNAKE_CASE_   ),     hmm=SCREAMING_SNAKE_CASE_   ):
																if token in self.vocab:
																						splits.append(normalized_string[start:end]   )
																else:
																						snake_case							:  Union[str, Any]			=     self.normalizers.normalize_str(SCREAMING_SNAKE_CASE_   ).split()
																						for token in token_list:
																												if token:
																																		snake_case							:  Optional[Any]			=     start + len(SCREAMING_SNAKE_CASE_   )
																																		splits.append(normalized_string[start:end]   )
																																		snake_case							:  Any			=     end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

										return splits




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					PreTokenizedString   ):
										pretok.split(self.jieba_split   )







from typing import TYPE_CHECKING

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


UpperCamelCase__						=		{
    'configuration_sam': [
        'SAM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SamConfig',
        'SamMaskDecoderConfig',
        'SamPromptEncoderConfig',
        'SamVisionConfig',
    ],
    'processing_sam': ['SamProcessor'],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SamModel',
							    'SamPreTrainedModel',
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFSamModel',
							    'TFSamPreTrainedModel',
							]
try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		['SamImageProcessor']


if TYPE_CHECKING:
							from .configuration_sam import (
							    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SamConfig,
							    SamMaskDecoderConfig,
							    SamPromptEncoderConfig,
							    SamVisionConfig,
							)
							from .processing_sam import SamProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_sam import SamImageProcessor

else:
							import sys

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