

"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


SCREAMING_SNAKE_CASE_       :						List[Any]					=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_       :						str					=    {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE_       :						List[str]					=    {
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}



class        a    (							__snake_case					):



    """simple docstring"""



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






         """simple docstring"""


         A__				      =	AddedToken(a_     ,    lstrip=a_     ,    rstrip=a_							) if isinstance(a_     ,    a_							) else mask_token

         super().__init__(
             vocab_file=a_     ,    tokenizer_file=a_     ,    do_lower_case=a_     ,    remove_space=a_     ,    keep_accents=a_     ,    bos_token=a_     ,    eos_token=a_     ,    unk_token=a_     ,    sep_token=a_     ,    pad_token=a_     ,    cls_token=a_     ,    mask_token=a_     ,    additional_special_tokens=a_     ,    **a_     ,    )

         A__				      =	3
         A__				      =	do_lower_case
         A__				      =	remove_space
         A__				      =	keep_accents
         A__				      =	vocab_file
         A__				      =	False if not self.vocab_file else True

         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."""							)
         A__				      =	jieba
         A__				      =	str.maketrans(""" \n"""     ,    """\u2582\u2583"""							)

    def 					UpperCamelCase						(  self:			Optional[int]     ,    UpperCamelCase:			Optional[int]     ,    UpperCamelCase:			List[Any] = None							):






         """simple docstring"""


         A__				      =	[self.sep_token_id]
         A__				      =	[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]     ,    UpperCamelCase:			List[Any]     ,    UpperCamelCase:			str = None							):






         """simple docstring"""


         A__				      =	[self.sep_token_id]
         A__				      =	[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:			List[str]     ,    UpperCamelCase:			List[str]     ,    UpperCamelCase:			Optional[Any] = None							):






         """simple docstring"""


         if not self.can_save_slow_tokenizer:
              raise ValueError(
                  """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                  """tokenizer."""							)

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

         if os.path.abspath(self.vocab_file							) != os.path.abspath(a_							):
              copyfile(self.vocab_file     ,    a_							)

         return (out_vocab_file,)

    def 					UpperCamelCase						(  self:			int     ,    UpperCamelCase:			List[Any]     ,    *UpperCamelCase:			Tuple     ,    **UpperCamelCase:			Any							):






         """simple docstring"""


         A__				      =	[
             ''' '''.join([x.translate(self.translator							) for x in self.jieba.cut(a_     ,    cut_all=a_							)]							)
             for text in batch_text_or_text_pairs
         ]
         return super()._batch_encode_plus(a_     ,    *a_     ,    **a_							)





    def 					UpperCamelCase						(  self:			Optional[Any]     ,    *UpperCamelCase:			Any     ,    **UpperCamelCase:			Optional[int]							):






         """simple docstring"""


         A__				      =	super()._decode(*a_     ,    **a_							)
         A__				      =	text.replace(""" """     ,    """"""							).replace("""\u2582"""     ,    """ """							).replace("""\u2583"""     ,    """\n"""							)
         return text










"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							{
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Dict							   =							["""BartTokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       List[Any]							   =							[
			    """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """BartForCausalLM""",
			    """BartForConditionalGeneration""",
			    """BartForQuestionAnswering""",
			    """BartForSequenceClassification""",
			    """BartModel""",
			    """BartPretrainedModel""",
			    """PretrainedBartModel""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Any							   =							[
			    """TFBartForConditionalGeneration""",
			    """TFBartForSequenceClassification""",
			    """TFBartModel""",
			    """TFBartPretrainedModel""",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       str							   =							[
			    """FlaxBartDecoderPreTrainedModel""",
			    """FlaxBartForCausalLM""",
			    """FlaxBartForConditionalGeneration""",
			    """FlaxBartForQuestionAnswering""",
			    """FlaxBartForSequenceClassification""",
			    """FlaxBartModel""",
			    """FlaxBartPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
			from .tokenization_bart import BartTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bart_fast import BartTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bart import (
						    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BartForCausalLM,
						    BartForConditionalGeneration,
						    BartForQuestionAnswering,
						    BartForSequenceClassification,
						    BartModel,
						    BartPretrainedModel,
						    PretrainedBartModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_bart import (
						    TFBartForConditionalGeneration,
						    TFBartForSequenceClassification,
						    TFBartModel,
						    TFBartPretrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_bart import (
						    FlaxBartDecoderPreTrainedModel,
						    FlaxBartForCausalLM,
						    FlaxBartForConditionalGeneration,
						    FlaxBartForQuestionAnswering,
						    FlaxBartForSequenceClassification,
						    FlaxBartModel,
						    FlaxBartPreTrainedModel,
						)

else:
			import sys

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





