




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,
)


if is_sentencepiece_available():
  from ..ta.tokenization_ta import TaTokenizer
else:
  from ...utils.dummy_sentencepiece_objects import TaTokenizer

_lowerCamelCase     :		Union[str, Any]								=       TaTokenizer

if is_tokenizers_available():
  from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
  from ...utils.dummy_tokenizers_objects import TaTokenizerFast

_lowerCamelCase     :		int								=       TaTokenizerFast

_lowerCamelCase     :		int								=       {'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Optional[Any]								=       [
      '''MT5EncoderModel''',
      '''MT5ForConditionalGeneration''',
      '''MT5ForQuestionAnswering''',
      '''MT5Model''',
      '''MT5PreTrainedModel''',
      '''MT5Stack''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Union[str, Any]								=       ['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		List[Any]								=       ['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
  from .configuration_mta import MTaConfig, MTaOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mta import (
        MTaEncoderModel,
        MTaForConditionalGeneration,
        MTaForQuestionAnswering,
        MTaModel,
        MTaPreTrainedModel,
        MTaStack,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
  import sys

  _lowerCamelCase     :		List[str]								=       _LazyModule(
      __name__,
      globals()["""__file__"""],
      _import_structure,
      extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
      module_spec=__spec__,
  )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowerCamelCase     :		Union[str, Any]								=       {
    """configuration_swiftformer""": [
        """SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SwiftFormerConfig""",
        """SwiftFormerOnnxConfig""",
    ]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Union[str, Any]								=       [
      """SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """SwiftFormerForImageClassification""",
      """SwiftFormerModel""",
      """SwiftFormerPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_swiftformer import (
      SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SwiftFormerConfig,
      SwiftFormerOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_swiftformer import (
        SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SwiftFormerForImageClassification,
        SwiftFormerModel,
        SwiftFormerPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase     :		str								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


