



'''simple docstring'''





import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing






class __A    (						A		):



 '''simple docstring'''







 def __init__(self				,     A = "▁"				,     A = True				,     A = "<unk>"				,     A = "</s>"				,     A = "<pad>"				,     )					->  int:


      """simple docstring"""



      _a											=						{
          '''pad''': {'''id''': 0, '''token''': pad_token},
          '''eos''': {'''id''': 1, '''token''': eos_token},
          '''unk''': {'''id''': 2, '''token''': unk_token},
      }

      _a											=						[None] * len(self.special_tokens )
      for token_dict in self.special_tokens.values():
           _a											=						token_dict['''token''']

      _a											=						Tokenizer(Unigram() )

      _a											=						normalizers.Sequence(
          [
              normalizers.Nmt(),
              normalizers.NFKC(),
              normalizers.Replace(Regex(''' {2,}''' )				,     ''' ''' ),
              normalizers.Lowercase(),
          ] )
      _a											=						pre_tokenizers.Sequence(
          [
              pre_tokenizers.Metaspace(replacement=A				,     add_prefix_space=A ),
              pre_tokenizers.Digits(individual_digits=A ),
              pre_tokenizers.Punctuation(),
          ] )
      _a											=						decoders.Metaspace(replacement=A				,     add_prefix_space=A )

      _a											=						TemplateProcessing(
          single=f'''$A {self.special_tokens['eos']['token']}'''				,     special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])]				,     )

      _a											=						{
          '''model''': '''SentencePieceUnigram''',
          '''replacement''': replacement,
          '''add_prefix_space''': add_prefix_space,
      }

      super().__init__(A				,     A )



 def       a__						(self				,     A				,     A = 8_000				,     A = True				,     )					->  Optional[int]:


      """simple docstring"""



      _a											=						trainers.UnigramTrainer(
          vocab_size=A				,     special_tokens=self.special_tokens_list				,     show_progress=A				,     )

      if isinstance(A				,     A ):
           _a											=						[files]
      self._tokenizer.train(A				,     trainer=A )

      self.add_unk_id()



 def       a__						(self				,     A				,     A = 8_000				,     A = True				,     )					->  Optional[Any]:


      """simple docstring"""



      _a											=						trainers.UnigramTrainer(
          vocab_size=A				,     special_tokens=self.special_tokens_list				,     show_progress=A				,     )

      self._tokenizer.train_from_iterator(A				,     trainer=A )

      self.add_unk_id()



 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						json.loads(self._tokenizer.to_str() )

      _a											=						self.special_tokens['''unk''']['''id''']

      _a											=						Tokenizer.from_str(json.dumps(A ) )








'''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"):
  lowercase_	 =						{
      "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:
  lowercase_	 =						{
      "linear": PIL.Image.LINEAR,
      "bilinear": PIL.Image.BILINEAR,
      "bicubic": PIL.Image.BICUBIC,
      "lanczos": PIL.Image.LANCZOS,
      "nearest": PIL.Image.NEAREST,
  }




def lowerCAmelCase						(__A):



     """simple docstring"""



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




def lowerCAmelCase						(__A):



     """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(__A) for image in images]

     return pil_images



