





import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


_SCREAMING_SNAKE_CASE     :   Dict      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :   List[str]      	=		{"""vocab_file""": """spiece.model"""}

_SCREAMING_SNAKE_CASE     :   Dict      	=		{
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    }
}
class SCREAMING_SNAKE_CASE_						(					_UpperCAmelCase					):






						def __init__(      self      :      Tuple		,   _A      :      Optional[Any]		,   _A      :      int=False		,   _A      :      str=True		,   _A      :      int=False		,   _A      :      List[str]="<s>"		,   _A      :      Optional[Any]="</s>"		,   _A      :      Any="<unk>"		,   _A      :      Optional[Any]="<sep>"		,   _A      :      Optional[Any]="<pad>"		,   _A      :      int="<cls>"		,   _A      :      Optional[int]="<mask>"		,   _A      :      List[Any]=["<eop>", "<eod>"]		,   _A      :      Optional[int] = None		,   **_A      :      Dict		,   )							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	AddedToken(lowercase__		,   lstrip=lowercase__		,   rstrip=lowercase__	) if isinstance(lowercase__		,   lowercase__	) else mask_token

							snake_case_		:			str											=	{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    do_lower_case=lowercase__		,   remove_space=lowercase__		,   keep_accents=lowercase__		,   bos_token=lowercase__		,   eos_token=lowercase__		,   unk_token=lowercase__		,   sep_token=lowercase__		,   pad_token=lowercase__		,   cls_token=lowercase__		,   mask_token=lowercase__		,   additional_special_tokens=lowercase__		,   sp_model_kwargs=self.sp_model_kwargs		,   **lowercase__		,   )

							snake_case_		:			int											=	3

							snake_case_		:			Tuple											=	do_lower_case
							snake_case_		:			Optional[int]											=	remove_space
							snake_case_		:			Tuple											=	keep_accents
							snake_case_		:			List[Any]											=	vocab_file

							snake_case_		:			Union[str, Any]											=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
							self.sp_model.Load(lowercase__	)

							try:
								import jieba
							except ModuleNotFoundError as error:
								raise error.__class__(
								    'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
								    'See https://pypi.org/project/jieba/ for installation.'	)
							snake_case_		:			str											=	jieba
							snake_case_		:			Dict											=	str.maketrans(' \n'		,   '\u2582\u2583'	)






						@property
						# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
						def UpperCAmelCase_				(      self      :      Dict	)							->    Optional[int]:







							"""simple docstring"""


							return len(self.sp_model	)






						def UpperCAmelCase_				(      self      :      str	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	{self.convert_ids_to_tokens(lowercase__	): i for i in range(self.vocab_size	)}
							vocab.update(self.added_tokens_encoder	)
							return vocab






						def __getstate__(      self      :      List[Any]	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			List[str]											=	self.__dict__.copy()
							snake_case_		:			Tuple											=	None
							return state






						def __setstate__(      self      :      Dict		,   _A      :      List[str]	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	d

							# for backward compatibility
							if not hasattr(self		,   'sp_model_kwargs'	):
								snake_case_		:			str											=	{}

							snake_case_		:			List[str]											=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
							self.sp_model.Load(self.vocab_file	)






						def UpperCAmelCase_				(      self      :      str		,   _A      :      List[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							if self.remove_space:
								snake_case_		:			int											=	" ".join(inputs.strip().split()	)
							else:
								snake_case_		:			Any											=	inputs
							snake_case_		:			int											=	outputs.replace('``'		,   '\"'	).replace('\'\''		,   '\"'	)

							if not self.keep_accents:
								snake_case_		:			int											=	unicodedata.normalize('NFKD'		,   lowercase__	)
								snake_case_		:			Optional[Any]											=	"".join([c for c in outputs if not unicodedata.combining(lowercase__	)]	)
							if self.do_lower_case:
								snake_case_		:			int											=	outputs.lower()

							return outputs






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      str	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Dict											=	self.preprocess_text(lowercase__	)
							snake_case_		:			List[str]											=	self.sp_model.encode(lowercase__		,   out_type=lowercase__	)
							snake_case_		:			Optional[Any]											=	[]
							for piece in pieces:
								if len(lowercase__	) > 1 and piece[-1] == str(','	) and piece[-2].isdigit():
									snake_case_		:			Any											=	self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase__		,   ''	)	)
									if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
										if len(cur_pieces[0]	) == 1:
											snake_case_		:			str											=	cur_pieces[1:]
										else:
											snake_case_		:			List[str]											=	cur_pieces[0][1:]
									cur_pieces.append(piece[-1]	)
									new_pieces.extend(lowercase__	)
								else:
									new_pieces.append(lowercase__	)

							return new_pieces






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      str	)							->    List[str]:







							"""simple docstring"""


							return self.sp_model.PieceToId(lowercase__	)






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      Optional[Any]	)							->    Any:







							"""simple docstring"""


							return self.sp_model.IdToPiece(lowercase__	)






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      Any	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	"".join(lowercase__	).replace(lowercase__		,   ' '	).strip()
							return out_string






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      List[Any]		,   _A      :      Optional[int] = None	)							->    str:







							"""simple docstring"""


							snake_case_		:			List[str]											=	[self.sep_token_id]
							snake_case_		:			Any											=	[self.cls_token_id]
							if token_ids_a is None:
								return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls






						def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      List[str]		,   _A      :      Dict = None		,   _A      :      List[str] = False	)							->    Dict:







							"""simple docstring"""


							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 ([0] * len(lowercase__	)) + [1] + ([0] * len(lowercase__	)) + [1, 1]
							return ([0] * len(lowercase__	)) + [1, 1]






						def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      List[Any]		,   _A      :      Any = None	)							->    Any:







							"""simple docstring"""


							snake_case_		:			List[str]											=	[self.sep_token_id]
							snake_case_		:			int											=	[2]

							if token_ids_a is None:
								return len(token_ids_a + sep	) * [0] + cls_segment_id
							return len(token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1] + cls_segment_id






						def UpperCAmelCase_				(      self      :      int		,   _A      :      Dict		,   _A      :      Optional[int] = None	)							->    Tuple:







							"""simple docstring"""


							if not os.path.isdir(lowercase__	):
								logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
								return
							snake_case_		:			Tuple											=	os.path.join(
							    lowercase__		,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

							if os.path.abspath(self.vocab_file	) != os.path.abspath(lowercase__	) and os.path.isfile(self.vocab_file	):
								copyfile(self.vocab_file		,   lowercase__	)
							elif not os.path.isfile(self.vocab_file	):
								with open(lowercase__		,   'wb'	) as fi:
									snake_case_		:			int											=	self.sp_model.serialized_model_proto()
									fi.write(lowercase__	)

							return (out_vocab_file,)






						def UpperCAmelCase_				(      self      :      List[Any]		,   *_A      :      List[Any]		,   **_A      :      Union[str, Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	super()._decode(*lowercase__		,   **lowercase__	)
							snake_case_		:			Any											=	text.replace(' '		,   ''	).replace('\u2582'		,   ' '	).replace('\u2583'		,   '\n'	)
							return text








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE      	=		{
    """configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertConfig"""],
    """tokenization_lxmert""": ["""LxmertTokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		["""LxmertTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """LxmertEncoder""",
						    """LxmertForPreTraining""",
						    """LxmertForQuestionAnswering""",
						    """LxmertModel""",
						    """LxmertPreTrainedModel""",
						    """LxmertVisualFeatureEncoder""",
						    """LxmertXLayer""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFLxmertForPreTraining""",
						    """TFLxmertMainLayer""",
						    """TFLxmertModel""",
						    """TFLxmertPreTrainedModel""",
						    """TFLxmertVisualFeatureEncoder""",
						]


if TYPE_CHECKING:
						from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
						from .tokenization_lxmert import LxmertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_lxmert_fast import LxmertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lxmert import (
												    LxmertEncoder,
												    LxmertForPreTraining,
												    LxmertForQuestionAnswering,
												    LxmertModel,
												    LxmertPreTrainedModel,
												    LxmertVisualFeatureEncoder,
												    LxmertXLayer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_lxmert import (
												    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLxmertForPreTraining,
												    TFLxmertMainLayer,
												    TFLxmertModel,
												    TFLxmertPreTrainedModel,
												    TFLxmertVisualFeatureEncoder,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE      	=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

