





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     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


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






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      {
           '''pad''': {'''id''': 0, '''token''': pad_token},
           '''eos''': {'''id''': 1, '''token''': eos_token},
           '''unk''': {'''id''': 2, '''token''': unk_token},
       }

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

       UpperCamelCase__							:				List[str]          =      Tokenizer(Unigram()							)

       UpperCamelCase__							:				Any          =      normalizers.Sequence(
           [
               normalizers.Nmt(),
               normalizers.NFKC(),
               normalizers.Replace(Regex(''' {2,}'''							),		''' '''							),
               normalizers.Lowercase(),
           ]							)
       UpperCamelCase__							:				int          =      pre_tokenizers.Sequence(
           [
               pre_tokenizers.Metaspace(replacement=__magic_name__,		add_prefix_space=__magic_name__							),
               pre_tokenizers.Digits(individual_digits=__magic_name__							),
               pre_tokenizers.Punctuation(),
           ]							)
       UpperCamelCase__							:				str          =      decoders.Metaspace(replacement=__magic_name__,		add_prefix_space=__magic_name__							)

       UpperCamelCase__							:				Optional[Any]          =      TemplateProcessing(
           single=f"$A {self.special_tokens['eos']['token']}",		special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])],		)

       UpperCamelCase__							:				Union[str, Any]          =      {
           '''model''': '''SentencePieceUnigram''',
           '''replacement''': replacement,
           '''add_prefix_space''': add_prefix_space,
       }

       super().__init__(__magic_name__,		__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = 8000,		__magic_name__ = True,		) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      trainers.UnigramTrainer(
           vocab_size=__magic_name__,		special_tokens=self.special_tokens_list,		show_progress=__magic_name__,		)

       if isinstance(__magic_name__,		__magic_name__							):
           UpperCamelCase__							:				Tuple          =      [files]
       self._tokenizer.train(__magic_name__,		trainer=__magic_name__							)

       self.add_unk_id()




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = 8000,		__magic_name__ = True,		) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      trainers.UnigramTrainer(
           vocab_size=__magic_name__,		special_tokens=self.special_tokens_list,		show_progress=__magic_name__,		)

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

       self.add_unk_id()




   def       UpperCamelCase__  (    self							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      json.loads(self._tokenizer.to_str()							)

       UpperCamelCase__							:				Union[str, Any]          =      self.special_tokens['''unk''']['''id''']

       UpperCamelCase__							:				List[str]          =      Tokenizer.from_str(json.dumps(__magic_name__							)							)








from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_     							= logging.get_logger(__name__)






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							str   		=		["pixel_values"]




   def __init__(    self,		__magic_name__ = True,		__magic_name__ = 32,		__magic_name__=PILImageResampling.BILINEAR,		__magic_name__ = True,		**__magic_name__,		) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				int          =      do_resize
       UpperCamelCase__							:				Tuple          =      do_rescale
       UpperCamelCase__							:				Any          =      size_divisor
       UpperCamelCase__							:				List[Any]          =      resample
       super().__init__(**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      get_image_size(__magic_name__							)
       # Rounds the height and width down to the closest multiple of size_divisor
       UpperCamelCase__							:				Any          =      height // size_divisor * size_divisor
       UpperCamelCase__							:				Optional[int]          =      width // size_divisor * size_divisor
       UpperCamelCase__							:				str          =      resize(__magic_name__,		(new_h, new_w),		resample=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)
       return image




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       return rescale(image=__magic_name__,		scale=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__=None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = ChannelDimension.FIRST,		**__magic_name__,		) ->      BatchFeature:






       """simple docstring"""
       UpperCamelCase__							:				str          =      do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__							:				Union[str, Any]          =      do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase__							:				Any          =      size_divisor if size_divisor is not None else self.size_divisor
       UpperCamelCase__							:				str          =      resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
           raise ValueError('''size_divisor is required for resizing'''							)

       UpperCamelCase__							:				Union[str, Any]          =      make_list_of_images(__magic_name__							)

       if not valid_images(__magic_name__							):
           raise ValueError('''Invalid image(s)'''							)

       # All transformations expect numpy arrays.
       UpperCamelCase__							:				Optional[Any]          =      [to_numpy_array(__magic_name__							) for img in images]

       if do_resize:
           UpperCamelCase__							:				str          =      [self.resize(__magic_name__,		size_divisor=__magic_name__,		resample=__magic_name__							) for image in images]

       if do_rescale:
           UpperCamelCase__							:				List[Any]          =      [self.rescale(__magic_name__,		scale=1 / 255							) for image in images]

       UpperCamelCase__							:				Optional[Any]          =      [to_channel_dimension_format(__magic_name__,		__magic_name__							) for image in images]

       UpperCamelCase__							:				Tuple          =      {'''pixel_values''': images}
       return BatchFeature(data=__magic_name__,		tensor_type=__magic_name__							)

