

'''simple docstring'''

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							:				Optional[Any]     	=			logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE							:				Optional[int]     	=			{
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    }
}






class    snake_case     (      lowercase_		):






				"""simple docstring"""



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

											SCREAMING_SNAKE_CASE_				=				{} 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,     )

											SCREAMING_SNAKE_CASE_				=				3

											SCREAMING_SNAKE_CASE_				=				do_lower_case
											SCREAMING_SNAKE_CASE_				=				remove_space
											SCREAMING_SNAKE_CASE_				=				keep_accents
											SCREAMING_SNAKE_CASE_				=				vocab_file

											SCREAMING_SNAKE_CASE_				=				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.'       )
											SCREAMING_SNAKE_CASE_				=				jieba
											SCREAMING_SNAKE_CASE_				=				str.maketrans(' \n',     '\u2582\u2583'       )



				@property
				# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
				def   a__					(    self       )     ->							Tuple:
											return len(self.sp_model       )



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				{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       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				self.__dict__.copy()
											SCREAMING_SNAKE_CASE_				=				None
											return state



				def __setstate__(    self,     _lowercase       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				=				d

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

											SCREAMING_SNAKE_CASE_				=				spm.SentencePieceProcessor(**self.sp_model_kwargs       )
											self.sp_model.Load(self.vocab_file       )



				def   a__					(    self,     _lowercase       )     ->							Dict:
											if self.remove_space:
																		SCREAMING_SNAKE_CASE_				=				' '.join(inputs.strip().split()       )
											else:
																		SCREAMING_SNAKE_CASE_				=				inputs
											SCREAMING_SNAKE_CASE_				=				outputs.replace('``',     '"'       ).replace('\'\'',     '"'       )

											if not self.keep_accents:
																		SCREAMING_SNAKE_CASE_				=				unicodedata.normalize('NFKD',     _lowercase       )
																		SCREAMING_SNAKE_CASE_				=				''.join([c for c in outputs if not unicodedata.combining(_lowercase       )]       )
											if self.do_lower_case:
																		SCREAMING_SNAKE_CASE_				=				outputs.lower()

											return outputs



				def   a__					(    self,     _lowercase       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				self.preprocess_text(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				self.sp_model.encode(_lowercase,     out_type=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				[]
											for piece in pieces:
																		if len(_lowercase       ) > 1 and piece[-1] == str(','       ) and piece[-2].isdigit():
																									SCREAMING_SNAKE_CASE_				=				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:
																																							SCREAMING_SNAKE_CASE_				=				cur_pieces[1:]
																																else:
																																							SCREAMING_SNAKE_CASE_				=				cur_pieces[0][1:]
																									cur_pieces.append(piece[-1]       )
																									new_pieces.extend(_lowercase       )
																		else:
																									new_pieces.append(_lowercase       )

											return new_pieces



				def   a__					(    self,     _lowercase       )     ->							str:
											return self.sp_model.PieceToId(_lowercase       )



				def   a__					(    self,     _lowercase       )     ->							int:
											return self.sp_model.IdToPiece(_lowercase       )



				def   a__					(    self,     _lowercase       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				''.join(_lowercase       ).replace(_lowercase,     ' '       ).strip()
											return out_string



				def   a__					(    self,     _lowercase,     _lowercase = None       )     ->							List[int]:
											SCREAMING_SNAKE_CASE_				=				[self.sep_token_id]
											SCREAMING_SNAKE_CASE_				=				[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,     _lowercase,     _lowercase = None,     _lowercase = False       )     ->							List[int]:

											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   a__					(    self,     _lowercase,     _lowercase = None       )     ->							List[int]:
											SCREAMING_SNAKE_CASE_				=				[self.sep_token_id]
											SCREAMING_SNAKE_CASE_				=				[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,     _lowercase,     _lowercase = None       )     ->							Tuple[str]:
											if not os.path.isdir(_lowercase       ):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""       )
																		return
											SCREAMING_SNAKE_CASE_				=				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:
																									SCREAMING_SNAKE_CASE_				=				self.sp_model.serialized_model_proto()
																									fi.write(_lowercase       )

											return (out_vocab_file,)





				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				super()._decode(*_lowercase,     **_lowercase       )
											SCREAMING_SNAKE_CASE_				=				text.replace(' ',     ''       ).replace('\u2582',     ' '       ).replace('\u2583',     '\n'       )
											return text








'''simple docstring'''

import warnings

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






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     ["""image_processor""", """tokenizer"""]
				_a    	=     """CLIPImageProcessor"""
				_a    	=     ("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")



				def __init__(    self,     _lowercase=None,     _lowercase=None,     **_lowercase       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.',     _lowercase,     )
																		SCREAMING_SNAKE_CASE_				=				kwargs.pop('feature_extractor'       )

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

											super().__init__(_lowercase,     _lowercase       )



				def __call__(    self,     _lowercase=None,     _lowercase=None,     _lowercase=None,     **_lowercase       )     ->							str:

											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:
																		SCREAMING_SNAKE_CASE_				=				self.tokenizer(_lowercase,     return_tensors=_lowercase,     **_lowercase       )

											if images is not None:
																		SCREAMING_SNAKE_CASE_				=				self.image_processor(_lowercase,     return_tensors=_lowercase,     **_lowercase       )

											if text is not None and images is not None:
																		SCREAMING_SNAKE_CASE_				=				image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**_lowercase       ),     tensor_type=_lowercase       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[int]:
											return self.tokenizer.batch_decode(*_lowercase,     **_lowercase       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[Any]:
											return self.tokenizer.decode(*_lowercase,     **_lowercase       )





				@property
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				self.tokenizer.model_input_names
											SCREAMING_SNAKE_CASE_				=				self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )





