


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				:							int					=			{"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							Optional[Any]					=			["T5Tokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							List[Any]					=			["T5TokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							List[Any]					=			[
      "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
      "T5EncoderModel",
      "T5ForConditionalGeneration",
      "T5Model",
      "T5PreTrainedModel",
      "load_tf_weights_in_t5",
      "T5ForQuestionAnswering",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							Tuple					=			[
      "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFT5EncoderModel",
      "TFT5ForConditionalGeneration",
      "TFT5Model",
      "TFT5PreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							List[Any]					=			[
      "FlaxT5EncoderModel",
      "FlaxT5ForConditionalGeneration",
      "FlaxT5Model",
      "FlaxT5PreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_ta import TaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_ta_fast import TaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_ta import (
        T5_PRETRAINED_MODEL_ARCHIVE_LIST,
        TaEncoderModel,
        TaForConditionalGeneration,
        TaForQuestionAnswering,
        TaModel,
        TaPreTrainedModel,
        load_tf_weights_in_ta,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_ta import (
        TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFTaEncoderModel,
        TFTaForConditionalGeneration,
        TFTaModel,
        TFTaPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_ta import (
        FlaxTaEncoderModel,
        FlaxTaForConditionalGeneration,
        FlaxTaModel,
        FlaxTaPreTrainedModel,
    )


else:
  import sys

  a				:							Tuple					=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










from typing import TYPE_CHECKING

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


a				:							List[str]					=			{
    'configuration_donut_swin': ['DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DonutSwinConfig'],
    'processing_donut': ['DonutProcessor'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							Dict					=			[
      'DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DonutSwinModel',
      'DonutSwinPreTrainedModel',
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							Any					=			['DonutFeatureExtractor']
  a				:							Dict					=			['DonutImageProcessor']


if TYPE_CHECKING:
  from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
  from .processing_donut import DonutProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_donut_swin import (
        DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
        DonutSwinModel,
        DonutSwinPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_donut import DonutFeatureExtractor
    from .image_processing_donut import DonutImageProcessor

else:
  import sys

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






