



"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowercase : Tuple      						=						{
    "configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : Tuple      						=						[
		    "FALCON_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "FalconForCausalLM",
		    "FalconModel",
		    "FalconPreTrainedModel",
		    "FalconForSequenceClassification",
		    "FalconForTokenClassification",
		    "FalconForQuestionAnswering",
		]


if TYPE_CHECKING:
		from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_falcon import (
				    FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FalconForCausalLM,
				    FalconForQuestionAnswering,
				    FalconForSequenceClassification,
				    FalconForTokenClassification,
				    FalconModel,
				    FalconPreTrainedModel,
				)


else:
		import sys

		_lowercase : List[str]      						=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


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

__lowerCamelCase							=       {
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

__lowerCamelCase							=       {
    """unc-nlp/lxmert-base-uncased""": 5_12,
}

__lowerCamelCase							=       {
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}






class   UpperCAmelCase		(						A_ ):
   A__					:		Any						     =     VOCAB_FILES_NAMES
   A__					:		Optional[int]						     =     PRETRAINED_VOCAB_FILES_MAP
   A__					:		Tuple						     =     PRETRAINED_INIT_CONFIGURATION
   A__					:		Tuple						     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__					:		List[Any]						     =     LxmertTokenizer



   def __init__(self		:					Dict    ,						snake_case__		:					Tuple=None    ,						snake_case__		:					Optional[Any]=None    ,						snake_case__		:					Optional[Any]=True    ,						snake_case__		:					Tuple="[UNK]"    ,						snake_case__		:					Optional[Any]="[SEP]"    ,						snake_case__		:					Optional[Any]="[PAD]"    ,						snake_case__		:					List[Any]="[CLS]"    ,						snake_case__		:					Tuple="[MASK]"    ,						snake_case__		:					Dict=True    ,						snake_case__		:					Union[str, Any]=None    ,						**snake_case__		:					Dict    ,						)		->	Optional[int]:


          '''simple docstring'''


          super().__init__(
              snake_case__    ,						tokenizer_file=snake_case__    ,						do_lower_case=snake_case__    ,						unk_token=snake_case__    ,						sep_token=snake_case__    ,						pad_token=snake_case__    ,						cls_token=snake_case__    ,						mask_token=snake_case__    ,						tokenize_chinese_chars=snake_case__    ,						strip_accents=snake_case__    ,						**snake_case__    ,						)

          snake_case       :   int							=      json.loads(self.backend_tokenizer.normalizer.__getstate__() )
          if (
              normalizer_state.get("lowercase"    ,						snake_case__ ) != do_lower_case
              or normalizer_state.get("strip_accents"    ,						snake_case__ ) != strip_accents
              or normalizer_state.get("handle_chinese_chars"    ,						snake_case__ ) != tokenize_chinese_chars
          ):
                 snake_case       :   Union[str, Any]							=      getattr(snake_case__    ,						normalizer_state.pop("type" ) )
                 snake_case       :   str							=      do_lower_case
                 snake_case       :   List[Any]							=      strip_accents
                 snake_case       :   Optional[int]							=      tokenize_chinese_chars
                 snake_case       :   int							=      normalizer_class(**snake_case__ )

          snake_case       :   Optional[Any]							=      do_lower_case



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Dict=None )		->	Any:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      [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 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					List[int]    ,						snake_case__		:					Optional[List[int]] = None )		->	List[int]:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      [self.sep_token_id]
          snake_case       :   Optional[Any]							=      [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 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					str    ,						snake_case__		:					Optional[str] = None )		->	Tuple[str]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self._tokenizer.model.save(snake_case__    ,						name=snake_case__ )
          return tuple(snake_case__ )





