




"""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


_lowerCamelCase					:     str				       =    logging.get_logger(__name__)

_lowerCamelCase					:     Optional[Any]				       =    {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase					:     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     lowercase (      __UpperCAmelCase):







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



					"""simple docstring"""

					A_					:			List[Any]						=						AddedToken(_lowerCamelCase ,       lstrip=_lowerCamelCase ,       rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase ,       _lowerCamelCase    ) else mask_token

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

					A_					:			Dict						=						3
					A_					:			Tuple						=						do_lower_case
					A_					:			Tuple						=						remove_space
					A_					:			Any						=						keep_accents
					A_					:			Optional[Any]						=						vocab_file
					A_					:			Optional[int]						=						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_					:			List[str]						=						jieba
					A_					:			Optional[Any]						=						str.maketrans(''' \n''' ,       '''\u2582\u2583'''    )







				def    a_					(							self				:   Tuple ,       _lowerCamelCase				:   List[int] ,       _lowerCamelCase				:   Optional[List[int]] = None    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						[self.sep_token_id]
					A_					:			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    a_					(							self				:   Optional[Any] ,       _lowerCamelCase				:   List[int] ,       _lowerCamelCase				:   Optional[List[int]] = None    ):



					"""simple docstring"""

					A_					:			str						=						[self.sep_token_id]
					A_					:			Optional[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    a_					(							self				:   List[Any] ,       _lowerCamelCase				:   str ,       _lowerCamelCase				:   Optional[str] = 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(_lowerCamelCase    ):
						logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
						return
					A_					:			List[str]						=						os.path.join(
					    _lowerCamelCase ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

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

					return (out_vocab_file,)







				def    a_					(							self				:   Tuple ,       _lowerCamelCase				:   Optional[Any] ,       *_lowerCamelCase				:   List[Any] ,       **_lowerCamelCase				:   Dict    ):



					"""simple docstring"""

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







				def    a_					(							self				:   Tuple ,       *_lowerCamelCase				:   Dict ,       **_lowerCamelCase				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						super()._decode(*_lowerCamelCase ,       **_lowerCamelCase    )
					A_					:			Any						=						text.replace(''' ''' ,       ''''''    ).replace('''\u2582''' ,       ''' '''    ).replace('''\u2583''' ,       '''\n'''    )
					return text











"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


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

_lowerCamelCase					:     Dict				       =    {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class     lowercase (      __UpperCAmelCase):
				__lowerCAmelCase      :      List[str]					    =				"""plbart"""
				__lowerCAmelCase      :      int					    =				["""past_key_values"""]
				__lowerCAmelCase      :      Union[str, Any]					    =				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







				def __init__(							self				:   Any ,       _lowerCamelCase				:   Any=5_00_05 ,       _lowerCamelCase				:   List[str]=10_24 ,       _lowerCamelCase				:   Union[str, Any]=6 ,       _lowerCamelCase				:   Any=30_72 ,       _lowerCamelCase				:   List[str]=12 ,       _lowerCamelCase				:   Tuple=6 ,       _lowerCamelCase				:   Dict=30_72 ,       _lowerCamelCase				:   Optional[int]=12 ,       _lowerCamelCase				:   Optional[int]=0.0 ,       _lowerCamelCase				:   Optional[int]=0.0 ,       _lowerCamelCase				:   Tuple=True ,       _lowerCamelCase				:   Optional[int]=True ,       _lowerCamelCase				:   str="gelu" ,       _lowerCamelCase				:   int=7_68 ,       _lowerCamelCase				:   Union[str, Any]=0.1 ,       _lowerCamelCase				:   Optional[Any]=0.1 ,       _lowerCamelCase				:   Tuple=0.0 ,       _lowerCamelCase				:   Optional[int]=0.02 ,       _lowerCamelCase				:   Dict=0.0 ,       _lowerCamelCase				:   Dict=True ,       _lowerCamelCase				:   List[str]=1 ,       _lowerCamelCase				:   str=0 ,       _lowerCamelCase				:   int=2 ,       _lowerCamelCase				:   str=2 ,       **_lowerCamelCase				:   Dict ,       ):



					"""simple docstring"""

					A_					:			int						=						vocab_size
					A_					:			Tuple						=						max_position_embeddings
					A_					:			Optional[Any]						=						d_model
					A_					:			Optional[int]						=						encoder_ffn_dim
					A_					:			Optional[int]						=						encoder_layers
					A_					:			List[str]						=						encoder_attention_heads
					A_					:			Dict						=						decoder_ffn_dim
					A_					:			List[str]						=						decoder_layers
					A_					:			str						=						decoder_attention_heads
					A_					:			List[Any]						=						dropout
					A_					:			int						=						attention_dropout
					A_					:			Dict						=						activation_dropout
					A_					:			int						=						activation_function
					A_					:			List[str]						=						init_std
					A_					:			int						=						encoder_layerdrop
					A_					:			int						=						decoder_layerdrop
					A_					:			Optional[int]						=						classifier_dropout
					A_					:			Optional[int]						=						use_cache
					A_					:			Dict						=						encoder_layers
					A_					:			int						=						scale_embedding  # scale factor will be sqrt(d_model) if True
					super().__init__(
					    pad_token_id=_lowerCamelCase ,       bos_token_id=_lowerCamelCase ,       eos_token_id=_lowerCamelCase ,       is_encoder_decoder=_lowerCamelCase ,       forced_eos_token_id=_lowerCamelCase ,       **_lowerCamelCase ,       )

class     lowercase (      __UpperCAmelCase):







				@property
				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					return OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
					    ]    )







				@property
				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					if self.use_past:
						return OrderedDict(
						    [
						        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						        ('''past_keys''', {0: '''batch''', 2: '''sequence'''}),
						        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						    ]    )
					else:
						return OrderedDict(
						    [
						        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						    ]    )





