
"""simple docstring"""





from typing import TYPE_CHECKING

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


_a														=		{"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """OPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """OPTForCausalLM""",
				    """OPTModel""",
				    """OPTPreTrainedModel""",
				    """OPTForSequenceClassification""",
				    """OPTForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		["""TFOPTForCausalLM""", """TFOPTModel""", """TFOPTPreTrainedModel"""]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """FlaxOPTForCausalLM""",
				    """FlaxOPTModel""",
				    """FlaxOPTPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_opt import (
								    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    OPTForCausalLM,
								    OPTForQuestionAnswering,
								    OPTForSequenceClassification,
								    OPTModel,
								    OPTPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

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_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_a														=		{
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TrOCRForCausalLM""",
				    """TrOCRPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
				from .processing_trocr import TrOCRProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
				import sys

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






