





'''simple docstring'''

from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowerCAmelCase__      :   Any      	=			{
    "configuration_ernie_m": ["ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieMConfig"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   List[str]      	=			["ErnieMTokenizer"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   Tuple      	=			[
			    "ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "ErnieMForMultipleChoice",
			    "ErnieMForQuestionAnswering",
			    "ErnieMForSequenceClassification",
			    "ErnieMForTokenClassification",
			    "ErnieMModel",
			    "ErnieMPreTrainedModel",
			    "ErnieMForInformationExtraction",
			]


if TYPE_CHECKING:
			from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_ernie_m import ErnieMTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ernie_m import (
						    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ErnieMForInformationExtraction,
						    ErnieMForMultipleChoice,
						    ErnieMForQuestionAnswering,
						    ErnieMForSequenceClassification,
						    ErnieMForTokenClassification,
						    ErnieMModel,
						    ErnieMPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase__      :   Union[str, Any]      	=			_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_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__      :   Any      	=			{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   Optional[int]      	=			["LlamaTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			["LlamaTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			[
			    "OpenLlamaForCausalLM",
			    "OpenLlamaModel",
			    "OpenLlamaPreTrainedModel",
			    "OpenLlamaForSequenceClassification",
			]


if TYPE_CHECKING:
			from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_open_llama import (
						    OpenLlamaForCausalLM,
						    OpenLlamaForSequenceClassification,
						    OpenLlamaModel,
						    OpenLlamaPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase__      :   Dict      	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




