





'''simple docstring'''

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

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


if is_sentencepiece_available():
  from .tokenization_xglm import XGLMTokenizer
else:
  __lowerCAmelCase							:			Optional[int]				=None


__lowerCAmelCase							:			List[str]				=logging.get_logger(__name__)

__lowerCAmelCase							:			Dict				={"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__lowerCAmelCase							:			str				={
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
    },
}

__lowerCAmelCase							:			int				={
    "facebook/xglm-564M": 2048,
}






class      UpperCAmelCase			( UpperCamelCase__       ):
 __lowercase     	=       VOCAB_FILES_NAMES
 __lowercase     	=       PRETRAINED_VOCAB_FILES_MAP
 __lowercase     	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase     	=       ["""input_ids""", """attention_mask"""]
 __lowercase     	=       XGLMTokenizer
 def __init__(       self   :int		, lowercase_   :Dict=None		, lowercase_   :List[Any]=None		, lowercase_   :Union[str, Any]="<s>"		, lowercase_   :Tuple="</s>"		, lowercase_   :Any="</s>"		, lowercase_   :int="<s>"		, lowercase_   :Any="<unk>"		, lowercase_   :Any="<pad>"		, **lowercase_   :Dict		, )->							Dict:
     # Compatibility with the original tokenizer
     A__       =    7
     A__       =    [F"<madeupword{i}>" for i in range(self.num_madeup_words							)]

     A__       =    kwargs.get("additional_special_tokens"		, []							)
     kwargs["additional_special_tokens"] += [
         word for word in madeup_words if word not in kwargs["additional_special_tokens"]
     ]

     super().__init__(
         lowercase_		, tokenizer_file=lowercase_		, bos_token=lowercase_		, eos_token=lowercase_		, sep_token=lowercase_		, cls_token=lowercase_		, unk_token=lowercase_		, pad_token=lowercase_		, **lowercase_		, )

     A__       =    vocab_file
     A__       =    False if not self.vocab_file else True
 def   UpperCAmelCase_							(       self   :Dict		, lowercase_   :List[int]		, lowercase_   :Optional[List[int]] = None							)->							List[int]:

     if token_ids_a is None:
         return [self.sep_token_id] + token_ids_a
     A__       =    [self.sep_token_id]
     return sep + token_ids_a + sep + sep + token_ids_a
 def   UpperCAmelCase_							(       self   :List[str]		, lowercase_   :List[int]		, lowercase_   :Optional[List[int]] = None							)->							List[int]:
     A__       =    [self.sep_token_id]

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



 def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :str		, lowercase_   :Optional[str] = None							)->							Tuple[str]:
     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(lowercase_							):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory."							)
         return
     A__       =    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_							):
         copyfile(self.vocab_file		, lowercase_							)

     return (out_vocab_file,)










'''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 )



