





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case__   :				Optional[Any]						=	{'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				List[Any]						=	['DetrFeatureExtractor']
       snake_case__   :				Tuple						=	['DetrImageProcessor']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Optional[int]						=	[
           'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
           'DetrForObjectDetection',
           'DetrForSegmentation',
           'DetrModel',
           'DetrPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_detr import DetrFeatureExtractor
              from .image_processing_detr import DetrImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_detr import (
                  DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DetrForObjectDetection,
                  DetrForSegmentation,
                  DetrModel,
                  DetrPreTrainedModel,
              )

else:
       import sys

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












from typing import TYPE_CHECKING

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


snake_case__   :				Any						=	{
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				List[Any]						=	['CamembertTokenizer']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Dict						=	['CamembertTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           '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:
       snake_case__   :				Any						=	[
           '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

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





