



snake_case    =	"""
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

snake_case    =	[{"""type""": """code""", """content""": INSTALL_CONTENT}]
snake_case    =	{
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}





from typing import TYPE_CHECKING

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


a__    =	{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__    =	[
      """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CanineForMultipleChoice""",
      """CanineForQuestionAnswering""",
      """CanineForSequenceClassification""",
      """CanineForTokenClassification""",
      """CanineLayer""",
      """CanineModel""",
      """CaninePreTrainedModel""",
      """load_tf_weights_in_canine""",
  ]


if TYPE_CHECKING:
  from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
  from .tokenization_canine import CanineTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_canine import (
        CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
        CanineForMultipleChoice,
        CanineForQuestionAnswering,
        CanineForSequenceClassification,
        CanineForTokenClassification,
        CanineLayer,
        CanineModel,
        CaninePreTrainedModel,
        load_tf_weights_in_canine,
    )


else:
  import sys

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