

from typing import TYPE_CHECKING

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


a_         =      {
    'configuration_dpr': ['DPR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPRConfig'],
    'tokenization_dpr': [
        'DPRContextEncoderTokenizer',
        'DPRQuestionEncoderTokenizer',
        'DPRReaderOutput',
        'DPRReaderTokenizer',
    ],
}


try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      [
			    'DPRContextEncoderTokenizerFast',
			    'DPRQuestionEncoderTokenizerFast',
			    'DPRReaderTokenizerFast',
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      [
			    'DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'DPRContextEncoder',
			    'DPRPretrainedContextEncoder',
			    'DPRPreTrainedModel',
			    'DPRPretrainedQuestionEncoder',
			    'DPRPretrainedReader',
			    'DPRQuestionEncoder',
			    'DPRReader',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      [
			    'TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFDPRContextEncoder',
			    'TFDPRPretrainedContextEncoder',
			    'TFDPRPretrainedQuestionEncoder',
			    'TFDPRPretrainedReader',
			    'TFDPRQuestionEncoder',
			    'TFDPRReader',
			]


if TYPE_CHECKING:
			from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
			from .tokenization_dpr import (
			    DPRContextEncoderTokenizer,
			    DPRQuestionEncoderTokenizer,
			    DPRReaderOutput,
			    DPRReaderTokenizer,
			)

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_dpr_fast import (
						    DPRContextEncoderTokenizerFast,
						    DPRQuestionEncoderTokenizerFast,
						    DPRReaderTokenizerFast,
						)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_dpr import (
						    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DPRContextEncoder,
						    DPRPretrainedContextEncoder,
						    DPRPreTrainedModel,
						    DPRPretrainedQuestionEncoder,
						    DPRPretrainedReader,
						    DPRQuestionEncoder,
						    DPRReader,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_dpr import (
						    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFDPRContextEncoder,
						    TFDPRPretrainedContextEncoder,
						    TFDPRPretrainedQuestionEncoder,
						    TFDPRPretrainedReader,
						    TFDPRQuestionEncoder,
						    TFDPRReader,
						)

else:
			import sys

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


from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	Optional[int] , a							:	Any		)							->    None:

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    []

				# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
				for token, start, end in self.jieba.tokenize(str(a		) , hmm=a		):
					if token in self.vocab:
						splits.append(normalized_string[start:end]		)
					else:
						SCREAMING_SNAKE_CASE				: Any			       =    self.normalizers.normalize_str(a		).split()
						for token in token_list:
							if token:
								SCREAMING_SNAKE_CASE				: Optional[int]			       =    start + len(a		)
								splits.append(normalized_string[start:end]		)
								SCREAMING_SNAKE_CASE				: List[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 							__UpperCamelCase	(     self							:	Optional[int] , a							:	PreTokenizedString		)							->    Tuple:

				"""simple docstring"""

				pretok.split(self.jieba_split		)