"""simple docstring"""







from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE	:		List[Any]   =   {
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

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

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE	:		Tuple   =   [
         "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:
     _SCREAMING_SNAKE_CASE	:		Optional[int]   =   [
         "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

     _SCREAMING_SNAKE_CASE	:		Optional[Any]   =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







'''simple docstring'''




_lowercase		:					List[str]													=		"\n# Diffusers installation\n! pip install diffusers transformers datasets accelerate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/diffusers.git\n"

_lowercase		:					Union[str, Any]													=		[{"type": "code", "content": INSTALL_CONTENT}]





