
"""simple docstring"""




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,
)


snake_case					  =						{'configuration_t5': ['T5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'T5Config', 'T5OnnxConfig']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case					  =						['T5Tokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case					  =						['T5TokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case					  =						[
	    '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:
	snake_case					  =						[
	    'TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFT5EncoderModel',
	    'TFT5ForConditionalGeneration',
	    'TFT5Model',
	    'TFT5PreTrainedModel',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case					  =						[
	    '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

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


"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case					  =						{'configuration_qdqbert': ['QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'QDQBertConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case					  =						[
	    'QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'QDQBertForMaskedLM',
	    'QDQBertForMultipleChoice',
	    'QDQBertForNextSentencePrediction',
	    'QDQBertForQuestionAnswering',
	    'QDQBertForSequenceClassification',
	    'QDQBertForTokenClassification',
	    'QDQBertLayer',
	    'QDQBertLMHeadModel',
	    'QDQBertModel',
	    'QDQBertPreTrainedModel',
	    'load_tf_weights_in_qdqbert',
	]


if TYPE_CHECKING:
	from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_qdqbert import (
		    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    QDQBertForMaskedLM,
		    QDQBertForMultipleChoice,
		    QDQBertForNextSentencePrediction,
		    QDQBertForQuestionAnswering,
		    QDQBertForSequenceClassification,
		    QDQBertForTokenClassification,
		    QDQBertLayer,
		    QDQBertLMHeadModel,
		    QDQBertModel,
		    QDQBertPreTrainedModel,
		    load_tf_weights_in_qdqbert,
		)


else:
	import sys

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