



'''simple docstring'''






import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
						import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__:     str										=    logging.get_logger(__name__)

UpperCamelCase__:     List[Any]										=    {"vocab_file": "vocab.txt"}

UpperCamelCase__:     List[Any]										=    {
    "vocab_file": {
        "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt",
    },
}

UpperCamelCase__:     Any										=    {
    "openbmb/cpm-ant-10b": 1024,
}


def 				snake_case_  (      _lowerCAmelCase						:		Dict						)   ->     int:

							'''simple docstring'''





							UpperCAmelCase				: Any											=	collections.OrderedDict()
							with open(SCREAMING_SNAKE_CASE_     ,   '''r'''     ,   encoding='''utf-8'''						) as reader:
														UpperCAmelCase				: Optional[int]											=	reader.readlines()
							for index, token in enumerate(SCREAMING_SNAKE_CASE_						):
														UpperCAmelCase				: Optional[Any]											=	token.rstrip('''\n'''						)
														UpperCAmelCase				: Tuple											=	index
							return vocab

class        SCREAMING_SNAKE_CASE(							lowercase__    ):







	"""simple docstring"""






	def __init__(		self    :  Dict	,    __snake_case    :  List[Any]	,    __snake_case    :  Optional[int]="<unk>"	,    __snake_case    :  int=200	)    ->	str:
								UpperCAmelCase				: int											=	vocab
								UpperCAmelCase				: Tuple											=	unk_token
								UpperCAmelCase				: Tuple											=	max_input_chars_per_word







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  str	)    ->	str:
								UpperCAmelCase				: List[Any]											=	list(__lowercase	)
								if len(__lowercase	) > self.max_input_chars_per_word:
															return [self.unk_token]

								UpperCAmelCase				: Union[str, Any]											=	0
								UpperCAmelCase				: Any											=	[]
								while start < len(__lowercase	):
															UpperCAmelCase				: Dict											=	len(__lowercase	)
															UpperCAmelCase				: int											=	None
															while start < end:
																						UpperCAmelCase				: Tuple											=	''''''.join(chars[start:end]	)
																						if substr in self.vocab:
																													UpperCAmelCase				: int											=	substr
																													break
																						end -= 1
															if cur_substr is None:
																						sub_tokens.append(self.unk_token	)
																						start += 1
															else:
																						sub_tokens.append(__lowercase	)
																						UpperCAmelCase				: Dict											=	end

								return sub_tokens

class        SCREAMING_SNAKE_CASE(							lowercase__    ):







	"""simple docstring"""






	lowerCamelCase__    				= VOCAB_FILES_NAMES
	lowerCamelCase__    				= PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__    				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__    				= ["""input_ids""", """attention_mask"""]
	lowerCamelCase__    				= False







	def __init__(		self    :  Tuple	,    __snake_case    :  List[Any]	,    __snake_case    :  Optional[Any]="<d>"	,    __snake_case    :  List[str]="</d>"	,    __snake_case    :  Dict="<s>"	,    __snake_case    :  Any="</s>"	,    __snake_case    :  Optional[Any]="<pad>"	,    __snake_case    :  str="<unk>"	,    __snake_case    :  Any="</n>"	,    __snake_case    :  List[str]="</_>"	,    __snake_case    :  Union[str, Any]="left"	,    **__snake_case    :  Dict	,    )    ->	List[str]:
								requires_backends(self	,    ['''jieba''']	)
								super().__init__(
								    bod_token=__lowercase	,    eod_token=__lowercase	,    bos_token=__lowercase	,    eos_token=__lowercase	,    pad_token=__lowercase	,    unk_token=__lowercase	,    line_token=__lowercase	,    space_token=__lowercase	,    padding_side=__lowercase	,    **__lowercase	,    )
								UpperCAmelCase				: Tuple											=	bod_token
								UpperCAmelCase				: List[Any]											=	eod_token
								UpperCAmelCase				: Optional[Any]											=	load_vocab(__lowercase	)
								UpperCAmelCase				: Dict											=	self.encoder[space_token]
								UpperCAmelCase				: Dict											=	self.encoder[line_token]

								del self.encoder[space_token]
								del self.encoder[line_token]

								UpperCAmelCase				: Any											=	collections.OrderedDict(sorted(self.encoder.items()	,    key=lambda __snake_case	: x[1]	)	)
								UpperCAmelCase				: Optional[Any]											=	{v: k for k, v in self.encoder.items()}

								UpperCAmelCase				: List[str]											=	WordpieceTokenizer(vocab=self.encoder	,    unk_token=self.unk_token	)







	@property
	def   A			(		self    :  List[Any]	)    ->	Optional[Any]:
								return self.encoder[self.bod_token]







	@property
	def   A			(		self    :  Optional[Any]	)    ->	Union[str, Any]:
								return self.encoder[self.eod_token]







	@property
	def   A			(		self    :  int	)    ->	Union[str, Any]:
								return self.encoder["\n"]







	@property
	def   A			(		self    :  int	)    ->	List[Any]:
								return len(self.encoder	)







	def   A			(		self    :  Union[str, Any]	)    ->	Tuple:
								return dict(self.encoder	,    **self.added_tokens_encoder	)







	def   A			(		self    :  Any	,    __snake_case    :  Dict	)    ->	int:
								UpperCAmelCase				: List[Any]											=	[]
								for x in jieba.cut(__lowercase	,    cut_all=__lowercase	):
															output_tokens.extend(self.wordpiece_tokenizer.tokenize(__lowercase	)	)
								return output_tokens







	def   A			(		self    :  Optional[int]	,    __snake_case    :  List[Any]	,    **__snake_case    :  List[Any]	)    ->	Union[str, Any]:
								UpperCAmelCase				: List[str]											=	[i for i in token_ids if i >= 0]
								UpperCAmelCase				: Dict											=	[
								    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
								]
								return super()._decode(__lowercase	,    **__lowercase	)







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  List[Any]	)    ->	List[Any]:
								return token in self.encoder







	def   A			(		self    :  int	,    __snake_case    :  List[str]	)    ->	List[Any]:
								return "".join(__lowercase	)







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  str	)    ->	Any:
								return self.encoder.get(__lowercase	,    self.encoder.get(self.unk_token	)	)







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  Any	)    ->	List[str]:
								return self.decoder.get(__lowercase	,    self.unk_token	)







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  str	,    __snake_case    :  Optional[str] = None	)    ->	List[str]:
								if os.path.isdir(__lowercase	):
															UpperCAmelCase				: Union[str, Any]											=	os.path.join(
															    __lowercase	,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)
								else:
															UpperCAmelCase				: Any											=	(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
								UpperCAmelCase				: Optional[Any]											=	0
								if " " in self.encoder:
															UpperCAmelCase				: Union[str, Any]											=	self.encoder[''' ''']
															del self.encoder[" "]
								if "\n" in self.encoder:
															UpperCAmelCase				: List[Any]											=	self.encoder['''\n''']
															del self.encoder["\n"]
								UpperCAmelCase				: Optional[int]											=	collections.OrderedDict(sorted(self.encoder.items()	,    key=lambda __snake_case	: x[1]	)	)
								with open(__lowercase	,    '''w'''	,    encoding='''utf-8'''	) as writer:
															for token, token_index in self.encoder.items():
																						if index != token_index:
																													logger.warning(
																													    F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
																													    ''' Please check that the vocabulary is not corrupted!'''	)
																													UpperCAmelCase				: List[Any]											=	token_index
																						writer.write(token + '''\n'''	)
																						index += 1
								return (vocab_file,)







	def   A			(		self    :  Tuple	,    __snake_case    :  List[int]	,    __snake_case    :  List[int] = None	)    ->	List[Any]:
								if token_ids_a is None:
															return [self.bos_token_id] + token_ids_a
								return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a







	def   A			(		self    :  List[str]	,    __snake_case    :  List[int]	,    __snake_case    :  Optional[List[int]] = None	,    __snake_case    :  bool = False	)    ->	List[str]:

								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=__lowercase	,    token_ids_a=__lowercase	,    already_has_special_tokens=__lowercase	)

								if token_ids_a is not None:
															return [1] + ([0] * len(__lowercase	)) + [1] + ([0] * len(__lowercase	))
								return [1] + ([0] * len(__lowercase	))










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__:     Union[str, Any]										=    {
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:     List[str]										=    [
						    "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LiltForQuestionAnswering",
						    "LiltForSequenceClassification",
						    "LiltForTokenClassification",
						    "LiltModel",
						    "LiltPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lilt import (
												    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LiltForQuestionAnswering,
												    LiltForSequenceClassification,
												    LiltForTokenClassification,
												    LiltModel,
												    LiltPreTrainedModel,
												)

else:
						import sys

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





