

'''simple docstring'''



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

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


if is_sentencepiece_available():
  from .tokenization_albert import AlbertTokenizer
else:
  UpperCAmelCase :       Any			   =     None

UpperCAmelCase :       str			   =     logging.get_logger(__name__)
UpperCAmelCase :       Union[str, Any]			   =     {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase :       List[Any]			   =     {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase :       Optional[int]			   =     {
    'albert-base-v1': 5_1_2,
    'albert-large-v1': 5_1_2,
    'albert-xlarge-v1': 5_1_2,
    'albert-xxlarge-v1': 5_1_2,
    'albert-base-v2': 5_1_2,
    'albert-large-v2': 5_1_2,
    'albert-xlarge-v2': 5_1_2,
    'albert-xxlarge-v2': 5_1_2,
}

UpperCAmelCase :       List[str]			   =     '▁'







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      VOCAB_FILES_NAMES
 lowerCAmelCase__  					=      PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase__  					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCAmelCase__  					=      AlbertTokenizer
 def __init__(		self					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       int=None					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=None					,							__SCREAMING_SNAKE_CASE					:       List[Any]=True					,							__SCREAMING_SNAKE_CASE					:       Dict=True					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=False					,							__SCREAMING_SNAKE_CASE					:       Any="[CLS]"					,							__SCREAMING_SNAKE_CASE					:       str="[SEP]"					,							__SCREAMING_SNAKE_CASE					:       Dict="<unk>"					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]="[SEP]"					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]="<pad>"					,							__SCREAMING_SNAKE_CASE					:       Any="[CLS]"					,							__SCREAMING_SNAKE_CASE					:       Dict="[MASK]"					,							**__SCREAMING_SNAKE_CASE					:       str					,							)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      (
          AddedToken(__SCREAMING_SNAKE_CASE					,							lstrip=__SCREAMING_SNAKE_CASE					,							rstrip=__SCREAMING_SNAKE_CASE					,							normalized=__SCREAMING_SNAKE_CASE			)
          if isinstance(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
          else mask_token
      )

      super().__init__(
          __SCREAMING_SNAKE_CASE					,							tokenizer_file=__SCREAMING_SNAKE_CASE					,							do_lower_case=__SCREAMING_SNAKE_CASE					,							remove_space=__SCREAMING_SNAKE_CASE					,							keep_accents=__SCREAMING_SNAKE_CASE					,							bos_token=__SCREAMING_SNAKE_CASE					,							eos_token=__SCREAMING_SNAKE_CASE					,							unk_token=__SCREAMING_SNAKE_CASE					,							sep_token=__SCREAMING_SNAKE_CASE					,							pad_token=__SCREAMING_SNAKE_CASE					,							cls_token=__SCREAMING_SNAKE_CASE					,							mask_token=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE					,							)

      __SCREAMING_SNAKE_CASE     =      do_lower_case
      __SCREAMING_SNAKE_CASE     =      remove_space
      __SCREAMING_SNAKE_CASE     =      keep_accents
      __SCREAMING_SNAKE_CASE     =      vocab_file
      __SCREAMING_SNAKE_CASE     =      False if not self.vocab_file else True
 def       UpperCAmelCase__		(		self					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       List[int]					,							__SCREAMING_SNAKE_CASE					:       Optional[List[int]] = None			)  -> List[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      [self.sep_token_id]
      __SCREAMING_SNAKE_CASE     =      [self.cls_token_id]
      if token_ids_a is None:
           return cls + token_ids_a + sep
      return cls + token_ids_a + sep + token_ids_a + sep
 def       UpperCAmelCase__		(		self					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       List[int]					,							__SCREAMING_SNAKE_CASE					:       Optional[List[int]] = None			)  -> List[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      [self.sep_token_id]
      __SCREAMING_SNAKE_CASE     =      [self.cls_token_id]

      if token_ids_a is None:
           return len(cls + token_ids_a + sep			) * [0]
      return len(cls + token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]


 def       UpperCAmelCase__		(		self					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       Optional[str] = None			)  -> Tuple[str]:


      """simple docstring"""




      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(__SCREAMING_SNAKE_CASE			):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'			)
           return
      __SCREAMING_SNAKE_CASE     =      os.path.join(
          __SCREAMING_SNAKE_CASE					,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

      if os.path.abspath(self.vocab_file			) != os.path.abspath(__SCREAMING_SNAKE_CASE			):
           copyfile(self.vocab_file					,							__SCREAMING_SNAKE_CASE			)

      return (out_vocab_file,)









'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


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







class 						lowerCAmelCase__   (      a ,					a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "maskformer-swin"

 lowerCAmelCase__  					=      {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }
 def __init__(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Tuple=224					,							__SCREAMING_SNAKE_CASE					:       str=4					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=3					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=96					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=[2, 2, 6, 2]					,							__SCREAMING_SNAKE_CASE					:       Any=[3, 6, 12, 24]					,							__SCREAMING_SNAKE_CASE					:       Dict=7					,							__SCREAMING_SNAKE_CASE					:       Dict=4.0					,							__SCREAMING_SNAKE_CASE					:       Dict=True					,							__SCREAMING_SNAKE_CASE					:       str=0.0					,							__SCREAMING_SNAKE_CASE					:       int=0.0					,							__SCREAMING_SNAKE_CASE					:       str=0.1					,							__SCREAMING_SNAKE_CASE					:       List[Any]="gelu"					,							__SCREAMING_SNAKE_CASE					:       str=False					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=0.02					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=1E-5					,							__SCREAMING_SNAKE_CASE					:       Dict=None					,							__SCREAMING_SNAKE_CASE					:       Dict=None					,							**__SCREAMING_SNAKE_CASE					:       Tuple					,							)  -> Tuple:


      """simple docstring"""




      super().__init__(**__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      image_size
      __SCREAMING_SNAKE_CASE     =      patch_size
      __SCREAMING_SNAKE_CASE     =      num_channels
      __SCREAMING_SNAKE_CASE     =      embed_dim
      __SCREAMING_SNAKE_CASE     =      depths
      __SCREAMING_SNAKE_CASE     =      len(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      num_heads
      __SCREAMING_SNAKE_CASE     =      window_size
      __SCREAMING_SNAKE_CASE     =      mlp_ratio
      __SCREAMING_SNAKE_CASE     =      qkv_bias
      __SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
      __SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE     =      drop_path_rate
      __SCREAMING_SNAKE_CASE     =      hidden_act
      __SCREAMING_SNAKE_CASE     =      use_absolute_embeddings
      __SCREAMING_SNAKE_CASE     =      layer_norm_eps
      __SCREAMING_SNAKE_CASE     =      initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      __SCREAMING_SNAKE_CASE     =      int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE			) - 1)			)
      __SCREAMING_SNAKE_CASE     =      ["""stem"""] + [f'stage{idx}' for idx in range(1					,							len(__SCREAMING_SNAKE_CASE			) + 1			)]
      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE					,							out_indices=__SCREAMING_SNAKE_CASE					,							stage_names=self.stage_names			)






