


from typing import TYPE_CHECKING

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


__lowerCamelCase        =     {
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     ['''XLMRobertaTokenizer''']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     ['''XLMRobertaTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''XLMRobertaForCausalLM''',
           '''XLMRobertaForMaskedLM''',
           '''XLMRobertaForMultipleChoice''',
           '''XLMRobertaForQuestionAnswering''',
           '''XLMRobertaForSequenceClassification''',
           '''XLMRobertaForTokenClassification''',
           '''XLMRobertaModel''',
           '''XLMRobertaPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFXLMRobertaForCausalLM''',
           '''TFXLMRobertaForMaskedLM''',
           '''TFXLMRobertaForMultipleChoice''',
           '''TFXLMRobertaForQuestionAnswering''',
           '''TFXLMRobertaForSequenceClassification''',
           '''TFXLMRobertaForTokenClassification''',
           '''TFXLMRobertaModel''',
           '''TFXLMRobertaPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''FlaxXLMRobertaForMaskedLM''',
           '''FlaxXLMRobertaForCausalLM''',
           '''FlaxXLMRobertaForMultipleChoice''',
           '''FlaxXLMRobertaForQuestionAnswering''',
           '''FlaxXLMRobertaForSequenceClassification''',
           '''FlaxXLMRobertaForTokenClassification''',
           '''FlaxXLMRobertaModel''',
           '''FlaxXLMRobertaPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_xlm_roberta import (
           XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
           XLMRobertaConfig,
           XLMRobertaOnnxConfig,
       )

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xlm_roberta import XLMRobertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xlm_roberta import (
                  XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLMRobertaForCausalLM,
                  XLMRobertaForMaskedLM,
                  XLMRobertaForMultipleChoice,
                  XLMRobertaForQuestionAnswering,
                  XLMRobertaForSequenceClassification,
                  XLMRobertaForTokenClassification,
                  XLMRobertaModel,
                  XLMRobertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xlm_roberta import (
                  TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXLMRobertaForCausalLM,
                  TFXLMRobertaForMaskedLM,
                  TFXLMRobertaForMultipleChoice,
                  TFXLMRobertaForQuestionAnswering,
                  TFXLMRobertaForSequenceClassification,
                  TFXLMRobertaForTokenClassification,
                  TFXLMRobertaModel,
                  TFXLMRobertaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_xlm_roberta import (
                  FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxXLMRobertaForCausalLM,
                  FlaxXLMRobertaForMaskedLM,
                  FlaxXLMRobertaForMultipleChoice,
                  FlaxXLMRobertaForQuestionAnswering,
                  FlaxXLMRobertaForSequenceClassification,
                  FlaxXLMRobertaForTokenClassification,
                  FlaxXLMRobertaModel,
                  FlaxXLMRobertaPreTrainedModel,
              )

else:
       import sys

       __lowerCamelCase        =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCamelCase        =     {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     ['''ViTFeatureExtractor''']
       __lowerCamelCase        =     ['''ViTImageProcessor''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''ViTForImageClassification''',
           '''ViTForMaskedImageModeling''',
           '''ViTModel''',
           '''ViTPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''TFViTForImageClassification''',
           '''TFViTModel''',
           '''TFViTPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''FlaxViTForImageClassification''',
           '''FlaxViTModel''',
           '''FlaxViTPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_vit import ViTFeatureExtractor
              from .image_processing_vit import ViTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vit import (
                  VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViTForImageClassification,
                  ViTForMaskedImageModeling,
                  ViTModel,
                  ViTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
       import sys

       __lowerCamelCase        =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



