

'''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_fnet import FNetTokenizer
else:
      _lowerCAmelCase         =						None

_lowerCAmelCase         =						logging.get_logger(__name__)
_lowerCAmelCase         =						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase         =						{
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

_lowerCAmelCase         =						{
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

_lowerCAmelCase         =						"▁"






class   _SCREAMING_SNAKE_CASE   (   lowerCamelCase_     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE					:List[str]      	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE					:Any      	=					["""input_ids""", """token_type_ids"""]
 __SCREAMING_SNAKE_CASE					:str      	=					FNetTokenizer



 def __init__(   self		:   List[Any]			,				a__		:   int=None			,				a__		:   Dict=None			,				a__		:   int=False			,				a__		:   Dict=True			,				a__		:   int=True			,				a__		:   Optional[Any]="<unk>"			,				a__		:   int="[SEP]"			,				a__		:   Union[str, Any]="<pad>"			,				a__		:   Optional[int]="[CLS]"			,				a__		:   Optional[int]="[MASK]"			,				**a__		:   Tuple			,				):
      __magic_name__						       =    (
          AddedToken(_UpperCAmelCase			,				lstrip=_UpperCAmelCase			,				rstrip=_UpperCAmelCase			,				normalized=_UpperCAmelCase      )
          if isinstance(_UpperCAmelCase			,				_UpperCAmelCase      )
          else mask_token
      )

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

      __magic_name__						       =    do_lower_case
      __magic_name__						       =    remove_space
      __magic_name__						       =    keep_accents
      __magic_name__						       =    vocab_file
      __magic_name__						       =    False if not self.vocab_file else True



 def 			snake_case__  (   self		:   Dict			,				a__		:   List[int]			,				a__		:   Optional[List[int]] = None      ):
      __magic_name__						       =    [self.sep_token_id]
      __magic_name__						       =    [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 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   List[int]			,				a__		:   Optional[List[int]] = None      ):
      __magic_name__						       =    [self.sep_token_id]
      __magic_name__						       =    [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 			snake_case__  (   self		:   str			,				a__		:   str			,				a__		:   Optional[str] = None      ):






      if not os.path.isdir(_UpperCAmelCase      ):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
           return
      __magic_name__						       =    os.path.join(
          _UpperCAmelCase			,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

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

      return (out_vocab_file,)









'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase         =						{"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase         =						["GLPNFeatureExtractor"]
      _lowerCAmelCase         =						["GLPNImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase         =						[
          "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
          "GLPNForDepthEstimation",
          "GLPNLayer",
          "GLPNModel",
          "GLPNPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_glpn import GLPNFeatureExtractor
            from .image_processing_glpn import GLPNImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_glpn import (
                GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
                GLPNForDepthEstimation,
                GLPNLayer,
                GLPNModel,
                GLPNPreTrainedModel,
            )


else:
      import sys

      _lowerCAmelCase         =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






