





'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


__lowerCAmelCase							:			Any				=logging.get_logger(__name__)

__lowerCAmelCase							:			Dict				={"vocab_file": "vocab.txt"}

__lowerCAmelCase							:			int				={
    "vocab_file": {
        "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt",
        "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt",
        "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt",
        "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt",
    }
}

__lowerCAmelCase							:			List[Any]				={
    "tau/splinter-base": 512,
    "tau/splinter-base-qass": 512,
    "tau/splinter-large": 512,
    "tau/splinter-large-qass": 512,
}

__lowerCAmelCase							:			Optional[int]				={
    "tau/splinter-base": {"do_lower_case": False},
    "tau/splinter-base-qass": {"do_lower_case": False},
    "tau/splinter-large": {"do_lower_case": False},
    "tau/splinter-large-qass": {"do_lower_case": False},
}






class      UpperCAmelCase			( UpperCamelCase__       ):
 __lowercase     	=       VOCAB_FILES_NAMES
 __lowercase     	=       PRETRAINED_VOCAB_FILES_MAP
 __lowercase     	=       PRETRAINED_INIT_CONFIGURATION
 __lowercase     	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase     	=       SplinterTokenizer
 def __init__(       self   :List[str]		, lowercase_   :str=None		, lowercase_   :str=None		, lowercase_   :List[str]=True		, lowercase_   :Union[str, Any]="[UNK]"		, lowercase_   :List[str]="[SEP]"		, lowercase_   :Tuple="[PAD]"		, lowercase_   :Any="[CLS]"		, lowercase_   :str="[MASK]"		, lowercase_   :Optional[int]="[QUESTION]"		, lowercase_   :Optional[Any]=True		, lowercase_   :Any=None		, **lowercase_   :int		, )->							int:
     super().__init__(
         lowercase_		, tokenizer_file=lowercase_		, do_lower_case=lowercase_		, unk_token=lowercase_		, sep_token=lowercase_		, pad_token=lowercase_		, cls_token=lowercase_		, mask_token=lowercase_		, tokenize_chinese_chars=lowercase_		, strip_accents=lowercase_		, additional_special_tokens=(question_token,)		, **lowercase_		, )

     A__       =    json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
     if (
         pre_tok_state.get("lowercase"		, lowercase_							) != do_lower_case
         or pre_tok_state.get("strip_accents"		, lowercase_							) != strip_accents
     ):
         A__       =    getattr(lowercase_		, pre_tok_state.pop("type"							)							)
         A__       =    do_lower_case
         A__       =    strip_accents
         A__       =    pre_tok_class(**lowercase_							)

     A__       =    do_lower_case
 @property
 def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							List[Any]:
     return self.convert_tokens_to_ids(self.question_token							)
 def   UpperCAmelCase_							(       self   :Any		, lowercase_   :List[int]		, lowercase_   :Optional[List[int]] = None							)->							List[int]:
     if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

     A__       =    [self.cls_token_id]
     A__       =    [self.sep_token_id]
     A__       =    [self.question_token_id] + [self.convert_tokens_to_ids("."							)]
     if self.padding_side == "right":
         # Input is question-then-context
         return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
     else:
         # Input is context-then-question
         return cls + token_ids_a + sep + token_ids_a + question_suffix + sep
 def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :List[int]		, lowercase_   :Optional[List[int]] = None							)->							List[int]:
     A__       =    [self.sep_token_id]
     A__       =    [self.cls_token_id]
     A__       =    [self.question_token_id] + [self.convert_tokens_to_ids("."							)]
     if token_ids_a is None:
         return len(cls + token_ids_a + sep							) * [0]

     if self.padding_side == "right":
         # Input is question-then-context
         return len(cls + token_ids_a + question_suffix + sep							) * [0] + len(token_ids_a + sep							) * [1]
     else:
         # Input is context-then-question
         return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + question_suffix + sep							) * [1]



 def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :str		, lowercase_   :Optional[str] = None							)->							Tuple[str]:
     A__       =    self._tokenizer.model.save(lowercase_		, name=lowercase_							)
     return tuple(lowercase_							)













'''simple docstring'''

import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image





def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Union[str, Any]=2_56 ):
    A__       =    min(img.size )

    if s < target_image_size:
        raise ValueError(F"min dim for image {s} < {target_image_size}" )

    A__       =    target_image_size / s
    A__       =    (round(r * img.size[1] ), round(r * img.size[0] ))
    A__       =    TF.resize(_lowerCamelCase		, _lowerCamelCase		, interpolation=PIL.Image.LANCZOS )
    A__       =    TF.center_crop(_lowerCamelCase		, output_size=2 * [target_image_size] )
    A__       =    torch.unsqueeze(T.ToTensor()(_lowerCamelCase )		, 0 )
    return img





def 		UpperCamelCase       (							_lowerCamelCase      :      int ):
    A__       =    2.0 * x - 1.0
    return x





def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Tuple=True		, _lowerCamelCase      :      int="RGB" ):
    A__       =    x.detach().cpu()
    if process:
        A__       =    post_process_tensor(_lowerCamelCase )
    A__       =    x.numpy()
    if process:
        A__       =    (2_55 * x).astype(np.uinta )
    A__       =    Image.fromarray(_lowerCamelCase )
    if not x.mode == mode:
        A__       =    x.convert(_lowerCamelCase )
    return x





def 		UpperCamelCase       (							_lowerCamelCase      :      Any ):
    A__       =    torch.clamp(_lowerCamelCase		, -1.0		, 1.0 )
    A__       =    (x + 1.0) / 2.0
    A__       =    x.permute(1		, 2		, 0 )
    return x





def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any] ):
    A__       =    post_process_tensor(x.squeeze() )
    return x.permute(2		, 0		, 1 ).unsqueeze(0 )



