
"""simple docstring"""


from typing import TYPE_CHECKING

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


A_    							=					{
    """configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
    """convert_funnel_original_tf_checkpoint_to_pytorch""": [],
    """tokenization_funnel""": ["""FunnelTokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					["""FunnelTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """FunnelBaseModel""",
						    """FunnelForMaskedLM""",
						    """FunnelForMultipleChoice""",
						    """FunnelForPreTraining""",
						    """FunnelForQuestionAnswering""",
						    """FunnelForSequenceClassification""",
						    """FunnelForTokenClassification""",
						    """FunnelModel""",
						    """FunnelPreTrainedModel""",
						    """load_tf_weights_in_funnel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFFunnelBaseModel""",
						    """TFFunnelForMaskedLM""",
						    """TFFunnelForMultipleChoice""",
						    """TFFunnelForPreTraining""",
						    """TFFunnelForQuestionAnswering""",
						    """TFFunnelForSequenceClassification""",
						    """TFFunnelForTokenClassification""",
						    """TFFunnelModel""",
						    """TFFunnelPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
						from .tokenization_funnel import FunnelTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_funnel_fast import FunnelTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_funnel import (
												    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FunnelBaseModel,
												    FunnelForMaskedLM,
												    FunnelForMultipleChoice,
												    FunnelForPreTraining,
												    FunnelForQuestionAnswering,
												    FunnelForSequenceClassification,
												    FunnelForTokenClassification,
												    FunnelModel,
												    FunnelPreTrainedModel,
												    load_tf_weights_in_funnel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_funnel import (
												    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFFunnelBaseModel,
												    TFFunnelForMaskedLM,
												    TFFunnelForMultipleChoice,
												    TFFunnelForPreTraining,
												    TFFunnelForQuestionAnswering,
												    TFFunnelForSequenceClassification,
												    TFFunnelForTokenClassification,
												    TFFunnelModel,
												    TFFunnelPreTrainedModel,
												)

else:
						import sys

						A_    							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





"""simple docstring"""


from typing import TYPE_CHECKING

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


A_    							=					{
    """configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """GPTNeoForCausalLM""",
						    """GPTNeoForQuestionAnswering""",
						    """GPTNeoForSequenceClassification""",
						    """GPTNeoForTokenClassification""",
						    """GPTNeoModel""",
						    """GPTNeoPreTrainedModel""",
						    """load_tf_weights_in_gpt_neo""",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """FlaxGPTNeoForCausalLM""",
						    """FlaxGPTNeoModel""",
						    """FlaxGPTNeoPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gpt_neo import (
												    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTNeoForCausalLM,
												    GPTNeoForQuestionAnswering,
												    GPTNeoForSequenceClassification,
												    GPTNeoForTokenClassification,
												    GPTNeoModel,
												    GPTNeoPreTrainedModel,
												    load_tf_weights_in_gpt_neo,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
						import sys

						A_    							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



