



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__     :      Any             =			{
    'configuration_gpt2': ['GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPT2Config', 'GPT2OnnxConfig'],
    'tokenization_gpt2': ['GPT2Tokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      List[Any]             =			['GPT2TokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Union[str, Any]             =			[
							    'GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'GPT2DoubleHeadsModel',
							    'GPT2ForQuestionAnswering',
							    'GPT2ForSequenceClassification',
							    'GPT2ForTokenClassification',
							    'GPT2LMHeadModel',
							    'GPT2Model',
							    'GPT2PreTrainedModel',
							    'load_tf_weights_in_gpt2',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Union[str, Any]             =			[
							    'TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFGPT2DoubleHeadsModel',
							    'TFGPT2ForSequenceClassification',
							    'TFGPT2LMHeadModel',
							    'TFGPT2MainLayer',
							    'TFGPT2Model',
							    'TFGPT2PreTrainedModel',
							]

try:
							if not is_keras_nlp_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Optional[int]             =			['TFGPT2Tokenizer']

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      int             =			['FlaxGPT2LMHeadModel', 'FlaxGPT2Model', 'FlaxGPT2PreTrainedModel']

if TYPE_CHECKING:
							from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
							from .tokenization_gpta import GPTaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpta_fast import GPTaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpta import (
														    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTaDoubleHeadsModel,
														    GPTaForQuestionAnswering,
														    GPTaForSequenceClassification,
														    GPTaForTokenClassification,
														    GPTaLMHeadModel,
														    GPTaModel,
														    GPTaPreTrainedModel,
														    load_tf_weights_in_gpta,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gpta import (
														    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFGPTaDoubleHeadsModel,
														    TFGPTaForSequenceClassification,
														    TFGPTaLMHeadModel,
														    TFGPTaMainLayer,
														    TFGPTaModel,
														    TFGPTaPreTrainedModel,
														)

							try:
														if not is_keras_nlp_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpta_tf import TFGPTaTokenizer

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
							import sys

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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__     :      Dict             =			{'configuration_sew_d': ['SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWDConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Union[str, Any]             =			[
							    'SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SEWDForCTC',
							    'SEWDForSequenceClassification',
							    'SEWDModel',
							    'SEWDPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_sew_d import (
														    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SEWDForCTC,
														    SEWDForSequenceClassification,
														    SEWDModel,
														    SEWDPreTrainedModel,
														)


else:
							import sys

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




