
'''simple docstring'''







from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE		:  Tuple        =      {
    'configuration_deformable_detr': ['DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeformableDetrConfig'],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  Optional[Any]        =      ['DeformableDetrFeatureExtractor']
					__SCREAMING_SNAKE_CASE		:  int        =      ['DeformableDetrImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  Dict        =      [
					    'DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'DeformableDetrForObjectDetection',
					    'DeformableDetrModel',
					    'DeformableDetrPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
										from .image_processing_deformable_detr import DeformableDetrImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_deformable_detr import (
										    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DeformableDetrForObjectDetection,
										    DeformableDetrModel,
										    DeformableDetrPreTrainedModel,
										)

else:
					import sys

					__SCREAMING_SNAKE_CASE		:  Optional[Any]        =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

'''simple docstring'''







from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers
class  SCREAMING_SNAKE_CASE__       :
	def __init__(				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Union[str, Any]								=	vocab
					__a       :     int								=	normalizers.BertNormalizer(
					    clean_text=__UpperCamelCase  ,    handle_chinese_chars=__UpperCamelCase  ,    strip_accents=__UpperCamelCase  ,    lowercase=__UpperCamelCase  ,    )
					try:
									import rjieba
					except ImportError:
									raise ImportError(
									    """You need to install rjieba to use RoFormerTokenizer. """
									    """See https://pypi.org/project/rjieba/ for installation."""			)
					__a       :     Optional[Any]								=	rjieba
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Union[str, Any]								=	[]

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



					'''simple docstring'''






					pretok.split(self.jieba_split			)