



'''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>" ,   )    ->     List[str]:


      """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 ,   )    ->     str:


      """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 ,   )    ->     Dict:


      """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'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str          =       'glpn'



 def __init__(self ,   A=3 ,   A=4 ,   A=[2, 2, 2, 2] ,   A=[8, 4, 2, 1] ,   A=[32, 64, 160, 256] ,   A=[7, 3, 3, 3] ,   A=[4, 2, 2, 2] ,   A=[1, 2, 5, 8] ,   A=[4, 4, 4, 4] ,   A="gelu" ,   A=0.0 ,   A=0.0 ,   A=0.02 ,   A=0.1 ,   A=1E-6 ,   A=64 ,   A=10 ,   A=-1 ,   **A ,   )    ->     Any:


      """simple docstring"""



      super().__init__(**A       )

      _a      =  num_channels
      _a      =  num_encoder_blocks
      _a      =  depths
      _a      =  sr_ratios
      _a      =  hidden_sizes
      _a      =  patch_sizes
      _a      =  strides
      _a      =  mlp_ratios
      _a      =  num_attention_heads
      _a      =  hidden_act
      _a      =  hidden_dropout_prob
      _a      =  attention_probs_dropout_prob
      _a      =  initializer_range
      _a      =  drop_path_rate
      _a      =  layer_norm_eps
      _a      =  decoder_hidden_size
      _a      =  max_depth
      _a      =  head_in_index



