

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


A__										=      logging.get_logger(__name__)

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

A__										=      {
    "vocab_file": {
        "microsoft/layoutlm-base-uncased": (
            "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt"
        ),
        "microsoft/layoutlm-large-uncased": (
            "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "microsoft/layoutlm-base-uncased": (
            "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json"
        ),
        "microsoft/layoutlm-large-uncased": (
            "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json"
        ),
    },
}

A__										=      {
    "microsoft/layoutlm-base-uncased": 512,
    "microsoft/layoutlm-large-uncased": 512,
}

A__										=      {
    "microsoft/layoutlm-base-uncased": {"do_lower_case": True},
    "microsoft/layoutlm-large-uncased": {"do_lower_case": True},
}







class  UpperCamelCase_      (	__UpperCAmelCase      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__					:			List[Any]					    =   PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__					:			List[str]					    =   PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE__					:			Optional[int]					    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__					:			Dict					    =   LayoutLMTokenizer



  def __init__(						self     :					str  ,	A_     :					List[str]=None  ,	A_     :					Tuple=None  ,	A_     :					str=True  ,	A_     :					Union[str, Any]="[UNK]"  ,	A_     :					List[Any]="[SEP]"  ,	A_     :					List[str]="[PAD]"  ,	A_     :					Optional[Any]="[CLS]"  ,	A_     :					Optional[Any]="[MASK]"  ,	A_     :					Union[str, Any]=True  ,	A_     :					int=None  ,	**A_     :					Tuple  ,	)       ->			Optional[Any]:
      super().__init__(
          UpperCAmelCase_  ,	tokenizer_file=UpperCAmelCase_  ,	do_lower_case=UpperCAmelCase_  ,	unk_token=UpperCAmelCase_  ,	sep_token=UpperCAmelCase_  ,	pad_token=UpperCAmelCase_  ,	cls_token=UpperCAmelCase_  ,	mask_token=UpperCAmelCase_  ,	tokenize_chinese_chars=UpperCAmelCase_  ,	strip_accents=UpperCAmelCase_  ,	**UpperCAmelCase_  ,	)

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

      SCREAMING_SNAKE_CASE__         =    do_lower_case



  def   _UpperCamelCase     (						self     :					Any  ,	A_     :					str  ,	A_     :					Dict=None						)       ->			Any:
      SCREAMING_SNAKE_CASE__         =    [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   _UpperCamelCase     (						self     :					Union[str, Any]  ,	A_     :					Dict  ,	A_     :					Tuple = None						)       ->			Tuple:
      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[int]  ,	A_     :					List[str]  ,	A_     :					Optional[int] = None						)       ->			Tuple:
      SCREAMING_SNAKE_CASE__         =    self._tokenizer.model.save(UpperCAmelCase_  ,	name=UpperCAmelCase_						)
      return tuple(UpperCAmelCase_						)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase_    ={
    """configuration_bark""": [
        """BARK_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BarkCoarseConfig""",
        """BarkConfig""",
        """BarkFineConfig""",
        """BarkSemanticConfig""",
    ],
    """processing_bark""": ["""BarkProcessor"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =[
		    """BARK_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """BarkFineModel""",
		    """BarkSemanticModel""",
		    """BarkCoarseModel""",
		    """BarkModel""",
		    """BarkPreTrainedModel""",
		    """BarkCausalModel""",
		]

if TYPE_CHECKING:
		from .configuration_bark import (
		    BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BarkCoarseConfig,
		    BarkConfig,
		    BarkFineConfig,
		    BarkSemanticConfig,
		)
		from .processing_bark import BarkProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bark import (
				    BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BarkCausalModel,
				    BarkCoarseModel,
				    BarkFineModel,
				    BarkModel,
				    BarkPreTrainedModel,
				    BarkSemanticModel,
				)

else:
		import sys

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



