




'''simple docstring'''


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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_snake_case : str  				=						logging.get_logger(__name__)

_snake_case : Tuple  				=						{'vocab_file': 'sentencepiece.model'}

_snake_case : List[Any]  				=						{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

_snake_case : str  				=						{
    'google/rembert': 256,
}

class      A       (						_a       ):
							lowercase_							     =							VOCAB_FILES_NAMES
							lowercase_							     =							PRETRAINED_VOCAB_FILES_MAP
							lowercase_							     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







							def __init__(    self			:    List[str]				,     lowerCAmelCase_			:    List[Any]				,     lowerCAmelCase_			:    Tuple=False				,     lowerCAmelCase_			:    Tuple=True				,     lowerCAmelCase_			:    Dict=True				,     lowerCAmelCase_			:    Optional[Any]="[CLS]"				,     lowerCAmelCase_			:    Any="[SEP]"				,     lowerCAmelCase_			:    Dict="[UNK]"				,     lowerCAmelCase_			:    Tuple="[SEP]"				,     lowerCAmelCase_			:    List[str]="[PAD]"				,     lowerCAmelCase_			:    Optional[int]="[CLS]"				,     lowerCAmelCase_			:    Optional[Any]="[MASK]"				,     **lowerCAmelCase_			:    List[Any]				,     )     ->				int:


														"""simple docstring"""


														super().__init__(
														    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_				,     **lowerCAmelCase_				,     )

														_a      	=							do_lower_case
														_a      	=							remove_space
														_a      	=							keep_accents
														_a      	=							vocab_file

														_a      	=							spm.SentencePieceProcessor()
														self.sp_model.Load(lowerCAmelCase_   )







							@property
							def   __lowerCAmelCase     (    self			:    str   )     ->				Any:


														"""simple docstring"""


														return len(self.sp_model   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							{self.convert_ids_to_tokens(lowerCAmelCase_   ): i for i in range(self.vocab_size   )}
														vocab.update(self.added_tokens_encoder   )
														return vocab







							def __getstate__(    self			:    List[Any]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.__dict__.copy()
														_a      	=							None
														return state







							def __setstate__(    self			:    Optional[Any]				,     lowerCAmelCase_			:    List[Any]   )     ->				int:


														"""simple docstring"""


														_a      	=							d
														_a      	=							spm.SentencePieceProcessor()
														self.sp_model.Load(self.vocab_file   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    Optional[Any]				,     lowerCAmelCase_			:    List[str]=False   )     ->				Tuple:


														"""simple docstring"""


														_a      	=							self.sp_model.EncodeAsPieces(lowerCAmelCase_   )
														return pieces







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    Dict   )     ->				Union[str, Any]:


														"""simple docstring"""


														return self.sp_model.PieceToId(lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    Optional[Any]   )     ->				int:


														"""simple docstring"""


														return self.sp_model.IdToPiece(lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    Any   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							self.sp_model.decode_pieces(lowerCAmelCase_   )
														return out_string







							def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    List[int]				,     lowerCAmelCase_			:    Optional[List[int]] = None   )     ->				List[int]:


														"""simple docstring"""


														_a      	=							[self.sep_token_id]
														_a      	=							[self.cls_token_id]
														if token_ids_a is None:
																					return cls + token_ids_a + sep
														return cls + token_ids_a + sep + token_ids_a + sep







							def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    List[int]				,     lowerCAmelCase_			:    Optional[List[int]] = None				,     lowerCAmelCase_			:    bool = False   )     ->				List[int]:


														"""simple docstring"""


														if already_has_special_tokens:
																					if token_ids_a is not None:
																												raise ValueError(
																												    '''You should not supply a second sequence if the provided sequence of '''
																												    '''ids is already formatted with special tokens for the model.'''   )
																					return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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







							def   __lowerCAmelCase     (    self			:    List[str]				,     lowerCAmelCase_			:    List[int]				,     lowerCAmelCase_			:    Optional[List[int]] = None   )     ->				List[int]:


														"""simple docstring"""


														_a      	=							[self.sep_token_id]
														_a      	=							[self.cls_token_id]

														if token_ids_a is None:
																					return len(cls + token_ids_a + sep   ) * [0]
														return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Optional[str] = None   )     ->				Tuple[str]:


														"""simple docstring"""


														if not os.path.isdir(lowerCAmelCase_   ):
																					logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCAmelCase_   )   )
																					return
														_a      	=							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,)







'''simple docstring'''


import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
				_snake_case : Tuple  				=						{
				    'linear': PIL.Image.Resampling.BILINEAR,
				    'bilinear': PIL.Image.Resampling.BILINEAR,
				    'bicubic': PIL.Image.Resampling.BICUBIC,
				    'lanczos': PIL.Image.Resampling.LANCZOS,
				    'nearest': PIL.Image.Resampling.NEAREST,
				}
else:
				_snake_case : Any  				=						{
				    'linear': PIL.Image.LINEAR,
				    'bilinear': PIL.Image.BILINEAR,
				    'bicubic': PIL.Image.BICUBIC,
				    'lanczos': PIL.Image.LANCZOS,
				    'nearest': PIL.Image.NEAREST,
				}
def 						snake_case_       (UpperCamelCase			:    Optional[int] ):





							'''simple docstring'''


							_a      	=							(images / 2 + 0.5).clamp(0					,		1 )
							_a      	=							images.cpu().permute(0					,		2					,		3					,		1 ).float().numpy()
							_a      	=							numpy_to_pil(UpperCamelCase )
							return images

def 						snake_case_       (UpperCamelCase			:    str ):





							'''simple docstring'''


							if images.ndim == 3:
														_a      	=							images[None, ...]
							_a      	=							(images * 255).round().astype('''uint8''' )
							if images.shape[-1] == 1:
														# special case for grayscale (single channel) images
														_a      	=							[Image.fromarray(image.squeeze()					,		mode='''L''' ) for image in images]
							else:
														_a      	=							[Image.fromarray(UpperCamelCase ) for image in images]

							return pil_images

