
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowerCamelCase_						: int         =  logging.get_logger(__name__)

lowerCamelCase_						: str         =  {"""vocab_file""": """vocab.txt"""}

lowerCamelCase_						: List[Any]         =  {
    """vocab_file""": {
        """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt""",
        """YituTech/conv-bert-medium-small""": (
            """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"""
        ),
        """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt""",
    }
}

lowerCamelCase_						: Tuple         =  {
    """YituTech/conv-bert-base""": 5_12,
    """YituTech/conv-bert-medium-small""": 5_12,
    """YituTech/conv-bert-small""": 5_12,
}


lowerCamelCase_						: List[Any]         =  {
    """YituTech/conv-bert-base""": {"""do_lower_case""": True},
    """YituTech/conv-bert-medium-small""": {"""do_lower_case""": True},
    """YituTech/conv-bert-small""": {"""do_lower_case""": True},
}

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						Any					      = VOCAB_FILES_NAMES
			__UpperCamelCase      :						Any					      = PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase      :						List[Any]					      = PRETRAINED_INIT_CONFIGURATION
			__UpperCamelCase      :						Union[str, Any]					      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase      :						Optional[Any]					      = ConvBertTokenizer

			def __init__(       self :       Dict						,	snake_case_ :       str=None						,	snake_case_ :       int=None						,	snake_case_ :       List[Any]=True						,	snake_case_ :       List[Any]="[UNK]"						,	snake_case_ :       List[Any]="[SEP]"						,	snake_case_ :       Optional[Any]="[PAD]"						,	snake_case_ :       List[Any]="[CLS]"						,	snake_case_ :       Any="[MASK]"						,	snake_case_ :       Optional[int]=True						,	snake_case_ :       Dict=None						,	**snake_case_ :       Dict						,	):
							super().__init__(
							    snake_case_						,	tokenizer_file=snake_case_						,	do_lower_case=snake_case_						,	unk_token=snake_case_						,	sep_token=snake_case_						,	pad_token=snake_case_						,	cls_token=snake_case_						,	mask_token=snake_case_						,	tokenize_chinese_chars=snake_case_						,	strip_accents=snake_case_						,	**snake_case_						,	)

							UpperCamelCase_:							Dict	=						json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
							if (
							    normalizer_state.get("""lowercase"""						,	snake_case_      ) != do_lower_case
							    or normalizer_state.get("""strip_accents"""						,	snake_case_      ) != strip_accents
							    or normalizer_state.get("""handle_chinese_chars"""						,	snake_case_      ) != tokenize_chinese_chars
							):
											UpperCamelCase_:							int	=						getattr(snake_case_						,	normalizer_state.pop("""type"""      )      )
											UpperCamelCase_:							List[str]	=						do_lower_case
											UpperCamelCase_:							Any	=						strip_accents
											UpperCamelCase_:							Any	=						tokenize_chinese_chars
											UpperCamelCase_:							Tuple	=						normalizer_class(**snake_case_      )

							UpperCamelCase_:							Dict	=						do_lower_case

			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       List[Any]						,	snake_case_ :       Dict=None      ):
							UpperCamelCase_:							Optional[int]	=						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
											output += token_ids_a + [self.sep_token_id]

							return output

			def       lowerCAmelCase__	(       self :       List[str]						,	snake_case_ :       List[int]						,	snake_case_ :       Optional[List[int]] = None      ):
							UpperCamelCase_:							Dict	=						[self.sep_token_id]
							UpperCamelCase_:							Optional[Any]	=						[self.cls_token_id]
							if token_ids_a is None:
											return len(cls + token_ids_a + sep      ) * [0]
							return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]




			def       lowerCAmelCase__	(       self :       Tuple						,	snake_case_ :       str						,	snake_case_ :       Optional[str] = None      ):
							UpperCamelCase_:							Any	=						self._tokenizer.model.save(snake_case_						,	name=snake_case_      )
							return tuple(snake_case_      )








from typing import TYPE_CHECKING

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


lowerCamelCase_						: str         =  {
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Union[str, Any]         =  ["""RoFormerTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Any         =  [
							    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RoFormerForCausalLM""",
							    """RoFormerForMaskedLM""",
							    """RoFormerForMultipleChoice""",
							    """RoFormerForQuestionAnswering""",
							    """RoFormerForSequenceClassification""",
							    """RoFormerForTokenClassification""",
							    """RoFormerLayer""",
							    """RoFormerModel""",
							    """RoFormerPreTrainedModel""",
							    """load_tf_weights_in_roformer""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Dict         =  [
							    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRoFormerForCausalLM""",
							    """TFRoFormerForMaskedLM""",
							    """TFRoFormerForMultipleChoice""",
							    """TFRoFormerForQuestionAnswering""",
							    """TFRoFormerForSequenceClassification""",
							    """TFRoFormerForTokenClassification""",
							    """TFRoFormerLayer""",
							    """TFRoFormerModel""",
							    """TFRoFormerPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[Any]         =  [
							    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """FlaxRoFormerForMaskedLM""",
							    """FlaxRoFormerForMultipleChoice""",
							    """FlaxRoFormerForQuestionAnswering""",
							    """FlaxRoFormerForSequenceClassification""",
							    """FlaxRoFormerForTokenClassification""",
							    """FlaxRoFormerModel""",
							    """FlaxRoFormerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
							from .tokenization_roformer import RoFormerTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_roformer_fast import RoFormerTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roformer import (
														    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RoFormerForCausalLM,
														    RoFormerForMaskedLM,
														    RoFormerForMultipleChoice,
														    RoFormerForQuestionAnswering,
														    RoFormerForSequenceClassification,
														    RoFormerForTokenClassification,
														    RoFormerLayer,
														    RoFormerModel,
														    RoFormerPreTrainedModel,
														    load_tf_weights_in_roformer,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roformer import (
														    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRoFormerForCausalLM,
														    TFRoFormerForMaskedLM,
														    TFRoFormerForMultipleChoice,
														    TFRoFormerForQuestionAnswering,
														    TFRoFormerForSequenceClassification,
														    TFRoFormerForTokenClassification,
														    TFRoFormerLayer,
														    TFRoFormerModel,
														    TFRoFormerPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roformer import (
														    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaxRoFormerForMaskedLM,
														    FlaxRoFormerForMultipleChoice,
														    FlaxRoFormerForQuestionAnswering,
														    FlaxRoFormerForSequenceClassification,
														    FlaxRoFormerForTokenClassification,
														    FlaxRoFormerModel,
														    FlaxRoFormerPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_						: Tuple         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






