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

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


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

UpperCAmelCase__				:  List[str]       					=				{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__				:  List[Any]       					=				{
    '''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 			UpperCamelCase_			(						__UpperCamelCase   ):




							'''simple docstring'''

							def __init__(				self     ,						UpperCamelCase=None     ,						UpperCamelCase=None     ,						UpperCamelCase=False     ,						UpperCamelCase=True     ,						UpperCamelCase=False     ,						UpperCamelCase="<s>"     ,						UpperCamelCase="</s>"     ,						UpperCamelCase="<unk>"     ,						UpperCamelCase="<sep>"     ,						UpperCamelCase="<pad>"     ,						UpperCamelCase="<cls>"     ,						UpperCamelCase="<mask>"     ,						UpperCamelCase=["<eop>", "<eod>"]     ,						**UpperCamelCase     ,						) ->	str:
										UpperCamelCase__    :		List[str]     =				AddedToken(UpperCamelCase     ,						lstrip=UpperCamelCase     ,						rstrip=UpperCamelCase) if isinstance(UpperCamelCase     ,						UpperCamelCase) else mask_token

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

										UpperCamelCase__    :		List[str]     =				3
										UpperCamelCase__    :		Any     =				do_lower_case
										UpperCamelCase__    :		Optional[int]     =				remove_space
										UpperCamelCase__    :		Optional[int]     =				keep_accents
										UpperCamelCase__    :		str     =				vocab_file
										UpperCamelCase__    :		Optional[Any]     =				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.')
										UpperCamelCase__    :		Any     =				jieba
										UpperCamelCase__    :		Any     =				str.maketrans(' \n'     ,						'\u2582\u2583')
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase = None) ->	List[int]:
										UpperCamelCase__    :		List[Any]     =				[self.sep_token_id]
										UpperCamelCase__    :		Tuple     =				[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        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase = None) ->	List[int]:
										UpperCamelCase__    :		Optional[int]     =				[self.sep_token_id]
										UpperCamelCase__    :		Any     =				[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        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase = None) ->	Tuple[str]:
										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(UpperCamelCase):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
													return
										UpperCamelCase__    :		Tuple     =				os.path.join(
										    UpperCamelCase     ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

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

										return (out_vocab_file,)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						*UpperCamelCase     ,						**UpperCamelCase) ->	List[str]:
										UpperCamelCase__    :		Optional[Any]     =				[
										    ' '.join([x.translate(self.translator) for x in self.jieba.cut(UpperCamelCase     ,						cut_all=UpperCamelCase)])
										    for text in batch_text_or_text_pairs
										]
										return super()._batch_encode_plus(UpperCamelCase     ,						*UpperCamelCase     ,						**UpperCamelCase)






							def        lowerCAmelCase__ (				self     ,						*UpperCamelCase     ,						**UpperCamelCase) ->	str:
										UpperCamelCase__    :		List[str]     =				super()._decode(*UpperCamelCase     ,						**UpperCamelCase)
										UpperCamelCase__    :		Any     =				text.replace(' '     ,						'').replace('\u2582'     ,						' ').replace('\u2583'     ,						'\n')
										return text





from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCAmelCase__				:  Tuple       					=				[
    '''AutomaticSpeechRecognition''',
    '''AudioClassification''',
    '''ImageClassification''',
    '''LanguageModeling''',
    '''QuestionAnsweringExtractive''',
    '''Summarization''',
    '''TaskTemplate''',
    '''TextClassification''',
]

UpperCAmelCase__				:  Dict       					=				get_logger(__name__)


UpperCAmelCase__				:  Dict       					=				{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}



def       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     Optional[TaskTemplate]:
			UpperCamelCase__    :		Any     =				task_template_dict.get('task' )
			if task_name is None:
						logger.warning(F"""Couldn't find template for task '{task_name}'. Available templates: {list(__SCREAMING_SNAKE_CASE )}""" )
						return None
			UpperCamelCase__    :		Any     =				NAMEaTEMPLATE.get(__SCREAMING_SNAKE_CASE )
			return template.from_dict(__SCREAMING_SNAKE_CASE )



