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_				:		List[str]       						=       {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_				:		Tuple       						=       ["""T5Tokenizer"""]

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_				:		List[str]       						=       [
	    """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:
	lowerCamelCase_				:		int       						=       [
	    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFT5EncoderModel""",
	    """TFT5ForConditionalGeneration""",
	    """TFT5Model""",
	    """TFT5PreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_				:		Union[str, 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

	lowerCamelCase_				:		int       						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase							= {'''configuration_nat''': ['''NAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NatConfig''']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase							= [
     '''NAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''NatForImageClassification''',
     '''NatModel''',
     '''NatPreTrainedModel''',
     '''NatBackbone''',
 ]

if TYPE_CHECKING:
 from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nat import (
      NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      NatBackbone,
      NatForImageClassification,
      NatModel,
      NatPreTrainedModel,
  )

else:
 import sys

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



