





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,
)


__A             =       {
    "configuration_xlm_roberta": [
        "XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XLMRobertaConfig",
        "XLMRobertaOnnxConfig",
    ],
}

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

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A             =       [
         "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:
     __A             =       [
         "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:
     __A             =       [
         "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

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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A             =       {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A             =       [
         "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
         "JukeboxModel",
         "JukeboxPreTrainedModel",
         "JukeboxVQVAE",
         "JukeboxPrior",
     ]

if TYPE_CHECKING:
     from .configuration_jukebox import (
         JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
         JukeboxConfig,
         JukeboxPriorConfig,
         JukeboxVQVAEConfig,
     )
     from .tokenization_jukebox import JukeboxTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_jukebox import (
              JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
              JukeboxModel,
              JukeboxPreTrainedModel,
              JukeboxPrior,
              JukeboxVQVAE,
          )

else:
     import sys

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

