from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase							=       {
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase							=       ["CamembertTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase							=       ["CamembertTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase							=       [
         "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "CamembertForCausalLM",
         "CamembertForMaskedLM",
         "CamembertForMultipleChoice",
         "CamembertForQuestionAnswering",
         "CamembertForSequenceClassification",
         "CamembertForTokenClassification",
         "CamembertModel",
         "CamembertPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase							=       [
         "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFCamembertForCausalLM",
         "TFCamembertForMaskedLM",
         "TFCamembertForMultipleChoice",
         "TFCamembertForQuestionAnswering",
         "TFCamembertForSequenceClassification",
         "TFCamembertForTokenClassification",
         "TFCamembertModel",
         "TFCamembertPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_camembert import CamembertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_camembert_fast import CamembertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_camembert import (
              CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              CamembertForCausalLM,
              CamembertForMaskedLM,
              CamembertForMultipleChoice,
              CamembertForQuestionAnswering,
              CamembertForSequenceClassification,
              CamembertForTokenClassification,
              CamembertModel,
              CamembertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_camembert import (
              TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFCamembertForCausalLM,
              TFCamembertForMaskedLM,
              TFCamembertForMultipleChoice,
              TFCamembertForQuestionAnswering,
              TFCamembertForSequenceClassification,
              TFCamembertForTokenClassification,
              TFCamembertModel,
              TFCamembertPreTrainedModel,
          )

else:
     import sys

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




from __future__ import annotations

UpperCAmelCase							=       "Alexander Joslin"






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int     ):

      if number <= 0:
            snake_case_     :			Dict       =       f"n must be greater than 0. Got n = {number}"
            raise ValueError(lowerCAmelCase_     )

      snake_case_     :			Tuple       =       [number + 1] * (number + 1)

      # starting position
      snake_case_     :			Union[str, Any]       =       0
      for i in range(1 ,	lowerCAmelCase_     ):
            snake_case_     :			int       =       min(table[i + 1] ,	table[i] + 1     )
            # check if out of bounds
            if i * 2 <= number:
                  snake_case_     :			Tuple       =       min(table[i * 2] ,	table[i] + 1     )
            # check if out of bounds
            if i * 3 <= number:
                  snake_case_     :			Dict       =       min(table[i * 3] ,	table[i] + 1     )
      return table[number]


if __name__ == "__main__":
     import doctest

     doctest.testmod()



