


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

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


lowerCAmelCase__						:Tuple         =							logging.get_logger(__name__)

lowerCAmelCase__						:Tuple         =							{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase__						:int         =							{
    '''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   (       UpperCamelCase__    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE="<s>"    ,					_SCREAMING_SNAKE_CASE="</s>"    ,					_SCREAMING_SNAKE_CASE="<unk>"    ,					_SCREAMING_SNAKE_CASE="<sep>"    ,					_SCREAMING_SNAKE_CASE="<pad>"    ,					_SCREAMING_SNAKE_CASE="<cls>"    ,					_SCREAMING_SNAKE_CASE="<mask>"    ,					_SCREAMING_SNAKE_CASE=["<eop>", "<eod>"]    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> str:




									"""simple docstring"""





									_UpperCAmelCase					=    AddedToken(snake_case_    ,					lstrip=snake_case_    ,					rstrip=snake_case_		) if isinstance(snake_case_    ,					snake_case_		) else mask_token

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

									_UpperCAmelCase					=    3
									_UpperCAmelCase					=    do_lower_case
									_UpperCAmelCase					=    remove_space
									_UpperCAmelCase					=    keep_accents
									_UpperCAmelCase					=    vocab_file
									_UpperCAmelCase					=    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					=    jieba
									_UpperCAmelCase					=    str.maketrans(' \n'    ,					'\u2582\u2583'		)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = None		)				-> List[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    [self.sep_token_id]
									_UpperCAmelCase					=    [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    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = None		)				-> List[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    [self.sep_token_id]
									_UpperCAmelCase					=    [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    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = None		)				-> Tuple[str]:




									"""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(snake_case_		):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
																return
									_UpperCAmelCase					=    os.path.join(
									    snake_case_    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

									return (out_vocab_file,)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)				-> Optional[Any]:




									"""simple docstring"""





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






		def 			UpperCAmelCase__       (    self    ,					*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)				-> str:




									"""simple docstring"""





									_UpperCAmelCase					=    super()._decode(*snake_case_    ,					**snake_case_		)
									_UpperCAmelCase					=    text.replace(' '    ,					''		).replace('\u2582'    ,					' '		).replace('\u2583'    ,					'\n'		)
									return text





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 	__a   (       UpperCAmelCase    ):
		_a			:						int    			=			['image_processor', 'tokenizer']
		_a			:						Union[str, Any]    			=			'AutoImageProcessor'
		_a			:						Optional[int]    			=			'AutoTokenizer'
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					**_SCREAMING_SNAKE_CASE		)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    None
									if "feature_extractor" in kwargs:
																warnings.warn(
																    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																    ' instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
																_UpperCAmelCase					=    kwargs.pop('feature_extractor'		)

									_UpperCAmelCase					=    image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
																raise ValueError('You have to specify an image_processor.'		)
									if tokenizer is None:
																raise ValueError('You have to specify a tokenizer.'		)

									super().__init__(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    self.image_processor
		def __call__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					**_SCREAMING_SNAKE_CASE		)				-> Dict:




									"""simple docstring"""





									if text is None and images is None:
																raise ValueError('You have to specify either text or images. Both cannot be none.'		)

									if text is not None:
																_UpperCAmelCase					=    self.tokenizer(_SCREAMING_SNAKE_CASE    ,					return_tensors=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)

									if images is not None:
																_UpperCAmelCase					=    self.image_processor(_SCREAMING_SNAKE_CASE    ,					return_tensors=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)

									if text is not None and images is not None:
																_UpperCAmelCase					=    image_features.pixel_values
																return encoding
									elif text is not None:
																return encoding
									else:
																return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE		)    ,					tensor_type=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)				-> int:




									"""simple docstring"""





									return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)				-> Tuple:




									"""simple docstring"""





									return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
		@property
		def 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    self.tokenizer.model_input_names
									_UpperCAmelCase					=    self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)
		@property
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									warnings.warn(
									    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
									return self.image_processor_class






		@property
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									warnings.warn(
									    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
									return self.image_processor
