

'''simple docstring'''




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


_lowerCAmelCase         =						logging.get_logger(__name__)

_lowerCAmelCase         =						{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase         =						[
    "small",
    "small-base",
    "medium",
    "medium-base",
    "intermediate",
    "intermediate-base",
    "large",
    "large-base",
    "xlarge",
    "xlarge-base",
]

_lowerCAmelCase         =						{
    "vocab_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
        "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
        "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
        "funnel-transformer/small-base": (
            "https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
        "funnel-transformer/large-base": (
            "https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"
        ),
    },
}
_lowerCAmelCase         =						{F'''funnel-transformer/{name}''': 512 for name in _model_names}
_lowerCAmelCase         =						{F'''funnel-transformer/{name}''': {"do_lower_case": True} for name in _model_names}






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE					:Any      	=					PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					PRETRAINED_INIT_CONFIGURATION
 __SCREAMING_SNAKE_CASE					:str      	=					FunnelTokenizer
 __SCREAMING_SNAKE_CASE					:Dict      	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE					:int      	=					2



 def __init__(   self		:   List[str]			,				a__		:   List[str]=None			,				a__		:   str=None			,				a__		:   Tuple=True			,				a__		:   Any="<unk>"			,				a__		:   List[str]="<sep>"			,				a__		:   Optional[int]="<pad>"			,				a__		:   Dict="<cls>"			,				a__		:   Union[str, Any]="<mask>"			,				a__		:   Optional[Any]="<s>"			,				a__		:   List[str]="</s>"			,				a__		:   Union[str, Any]=True			,				a__		:   str=True			,				a__		:   int=None			,				a__		:   Tuple="##"			,				**a__		:   str			,				):
      super().__init__(
          a__			,				tokenizer_file=a__			,				do_lower_case=a__			,				unk_token=a__			,				sep_token=a__			,				pad_token=a__			,				cls_token=a__			,				mask_token=a__			,				bos_token=a__			,				eos_token=a__			,				clean_text=a__			,				tokenize_chinese_chars=a__			,				strip_accents=a__			,				wordpieces_prefix=a__			,				**a__			,				)

      __magic_name__						       =    json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
      if (
          normalizer_state.get('''lowercase'''			,				a__      ) != do_lower_case
          or normalizer_state.get('''strip_accents'''			,				a__      ) != strip_accents
          or normalizer_state.get('''handle_chinese_chars'''			,				a__      ) != tokenize_chinese_chars
      ):
           __magic_name__						       =    getattr(a__			,				normalizer_state.pop('''type'''      )      )
           __magic_name__						       =    do_lower_case
           __magic_name__						       =    strip_accents
           __magic_name__						       =    tokenize_chinese_chars
           __magic_name__						       =    normalizer_class(**a__      )

      __magic_name__						       =    do_lower_case



 def 			snake_case__  (   self		:   List[str]			,				a__		:   Any			,				a__		:   List[str]=None      ):
      __magic_name__						       =    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
           output += token_ids_a + [self.sep_token_id]

      return output



 def 			snake_case__  (   self		:   List[str]			,				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      ) * [self.cls_token_type_id] + len(token_ids_a + sep      ) * [0]
      return len(cls      ) * [self.cls_token_type_id] + len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   str			,				a__		:   Optional[str] = None      ):
      __magic_name__						       =    self._tokenizer.model.save(a__			,				name=a__      )
      return tuple(a__      )









'''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__)






