
"""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


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					{'''vocab_file''': '''spiece.model'''}

_lowercase				 =					{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						def __init__(			self  :   Dict	,A_  :   List[Any]	,A_  :   Union[str, Any]=False	,A_  :   str=True	,A_  :   Tuple=False	,A_  :   Optional[int]="<s>"	,A_  :   Tuple="</s>"	,A_  :   Tuple="<unk>"	,A_  :   Dict="<sep>"	,A_  :   Optional[int]="<pad>"	,A_  :   Optional[int]="<cls>"	,A_  :   Any="<mask>"	,A_  :   Optional[Any]=["<eop>", "<eod>"]	,A_  :   Optional[Dict[str, Any]] = None	,**A_  :   int	,)  -> None:
							A     = AddedToken(A_	,lstrip=A_	,rstrip=A_   ) if isinstance(A_	,A_   ) else mask_token

							A     = {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    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_	,sp_model_kwargs=self.sp_model_kwargs	,**A_	,)

							A     = 3

							A     = do_lower_case
							A     = remove_space
							A     = keep_accents
							A     = vocab_file

							A     = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(A_   )

							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'   )


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Optional[Any]:
							A     = {self.convert_ids_to_tokens(A_   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab


						def __getstate__(			self  :   Union[str, Any]   )  -> Dict:
							A     = self.__dict__.copy()
							A     = None
							return state


						def __setstate__(			self  :   Optional[Any]	,A_  :   int   )  -> Any:
							A     = d

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

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   Any   )  -> Optional[Any]:
							if self.remove_space:
								A     = ' '.join(inputs.strip().split()   )
							else:
								A     = inputs
							A     = outputs.replace('``'	,'"'   ).replace('\'\''	,'"'   )

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

							return outputs


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,A_  :   str   )  -> List[str]:
							A     = self.preprocess_text(A_   )
							A     = self.sp_model.encode(A_	,out_type=A_   )
							A     = []
							for piece in pieces:
								if len(A_   ) > 1 and piece[-1] == str(','   ) and piece[-2].isdigit():
									A     = self.sp_model.EncodeAsPieces(piece[:-1].replace(A_	,''   )   )
									if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
										if len(cur_pieces[0]   ) == 1:
											A     = cur_pieces[1:]
										else:
											A     = cur_pieces[0][1:]
									cur_pieces.append(piece[-1]   )
									new_pieces.extend(A_   )
								else:
									new_pieces.append(A_   )

							return new_pieces


						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   Dict   )  -> Any:
							return self.sp_model.PieceToId(A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   int   )  -> Optional[Any]:
							return self.sp_model.IdToPiece(A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   Dict   )  -> Optional[Any]:
							A     = ''.join(A_   ).replace(A_	,' '   ).strip()
							return out_string


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   List[int]	,A_  :   Optional[List[int]] = None   )  -> List[int]:
							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     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,A_  :   List[int]	,A_  :   Optional[List[int]] = None	,A_  :   bool = False   )  -> List[int]:

							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=A_	,token_ids_a=A_	,already_has_special_tokens=A_   )

							if token_ids_a is not None:
								return ([0] * len(A_   )) + [1] + ([0] * len(A_   )) + [1, 1]
							return ([0] * len(A_   )) + [1, 1]


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   List[int]	,A_  :   Optional[List[int]] = None   )  -> List[int]:
							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     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]	,A_  :   str	,A_  :   Optional[str] = None   )  -> Tuple[str]:
							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_   ) and os.path.isfile(self.vocab_file   ):
								copyfile(self.vocab_file	,A_   )
							elif not os.path.isfile(self.vocab_file   ):
								with open(A_	,'wb'   ) as fi:
									A     = self.sp_model.serialized_model_proto()
									fi.write(A_   )

							return (out_vocab_file,)




						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,*A_  :   Optional[int]	,**A_  :   Tuple   )  -> Optional[int]:
							A     = super()._decode(*A_	,**A_   )
							A     = text.replace(' '	,''   ).replace('\u2582'	,' '   ).replace('\u2583'	,'\n'   )
							return text

"""simple docstring"""


from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     ['''image_processor''', '''tokenizer''']
						_lowerCamelCase:     Optional[int]   =     '''Pix2StructImageProcessor'''
						_lowerCamelCase:     Dict   =     ('''T5Tokenizer''', '''T5TokenizerFast''')


						def __init__(			self  :   Optional[int]	,A_  :   List[str]	,A_  :   Optional[int]   )  -> int:
							A     = False
							super().__init__(A_	,A_   )


						def __call__(			self  :   Any	,A_  :   List[str]=None	,A_  :   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,A_  :   bool = True	,A_  :   Union[bool, str, PaddingStrategy] = False	,A_  :   Union[bool, str, TruncationStrategy] = None	,A_  :   Optional[int] = None	,A_  :   Optional[int] = 2048	,A_  :   int = 0	,A_  :   Optional[int] = None	,A_  :   Optional[bool] = None	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = True	,A_  :   Optional[Union[str, TensorType]] = None	,**A_  :   Tuple	,)  -> BatchEncoding:
							if images is None and text is None:
								raise ValueError('You have to specify either images or text.'   )

							# Get only text
							if images is None and not self.image_processor.is_vqa:
								A     = self.tokenizer
								A     = self.tokenizer(
								    text=A_	,add_special_tokens=A_	,padding=A_	,truncation=A_	,max_length=A_	,stride=A_	,pad_to_multiple_of=A_	,return_attention_mask=A_	,return_overflowing_tokens=A_	,return_special_tokens_mask=A_	,return_offsets_mapping=A_	,return_token_type_ids=A_	,return_length=A_	,verbose=A_	,return_tensors=A_	,**A_	,)
								return text_encoding

							if not self.image_processor.is_vqa:
								# add pixel_values
								A     = self.image_processor(
								    A_	,return_tensors=A_	,max_patches=A_	,**A_   )
							else:
								# add pixel_values and bbox
								A     = self.image_processor(
								    A_	,return_tensors=A_	,max_patches=A_	,header_text=A_	,**A_   )

							if text is not None and not self.image_processor.is_vqa:
								A     = self.tokenizer(
								    text=A_	,add_special_tokens=A_	,padding=A_	,truncation=A_	,max_length=A_	,stride=A_	,pad_to_multiple_of=A_	,return_attention_mask=A_	,return_overflowing_tokens=A_	,return_special_tokens_mask=A_	,return_offsets_mapping=A_	,return_token_type_ids=A_	,return_length=A_	,verbose=A_	,return_tensors=A_	,**A_	,)

								if "attention_mask" in text_encoding:
									A     = text_encoding.pop('attention_mask'   )
								if "input_ids" in text_encoding:
									A     = text_encoding.pop('input_ids'   )
							else:
								A     = None

							if text_encoding is not None:
								encoding_image_processor.update(A_   )

							return encoding_image_processor


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,*A_  :   Optional[Any]	,**A_  :   Dict   )  -> Union[str, Any]:
							return self.tokenizer.batch_decode(*A_	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,*A_  :   Tuple	,**A_  :   List[str]   )  -> Any:
							return self.tokenizer.decode(*A_	,**A_   )




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Any:
							A     = self.tokenizer.model_input_names
							A     = self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )