
"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCamelCase__ :       Any						     =			{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Tuple						     =			["DistilBertTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       List[str]						     =			[
					    "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DistilBertForMaskedLM",
					    "DistilBertForMultipleChoice",
					    "DistilBertForQuestionAnswering",
					    "DistilBertForSequenceClassification",
					    "DistilBertForTokenClassification",
					    "DistilBertModel",
					    "DistilBertPreTrainedModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Dict						     =			[
					    "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFDistilBertForMaskedLM",
					    "TFDistilBertForMultipleChoice",
					    "TFDistilBertForQuestionAnswering",
					    "TFDistilBertForSequenceClassification",
					    "TFDistilBertForTokenClassification",
					    "TFDistilBertMainLayer",
					    "TFDistilBertModel",
					    "TFDistilBertPreTrainedModel",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Dict						     =			[
					    "FlaxDistilBertForMaskedLM",
					    "FlaxDistilBertForMultipleChoice",
					    "FlaxDistilBertForQuestionAnswering",
					    "FlaxDistilBertForSequenceClassification",
					    "FlaxDistilBertForTokenClassification",
					    "FlaxDistilBertModel",
					    "FlaxDistilBertPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_distilbert import (
					    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DistilBertConfig,
					    DistilBertOnnxConfig,
					)
					from .tokenization_distilbert import DistilBertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_distilbert_fast import DistilBertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_distilbert import (
										    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DistilBertForMaskedLM,
										    DistilBertForMultipleChoice,
										    DistilBertForQuestionAnswering,
										    DistilBertForSequenceClassification,
										    DistilBertForTokenClassification,
										    DistilBertModel,
										    DistilBertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_distilbert import (
										    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDistilBertForMaskedLM,
										    TFDistilBertForMultipleChoice,
										    TFDistilBertForQuestionAnswering,
										    TFDistilBertForSequenceClassification,
										    TFDistilBertForTokenClassification,
										    TFDistilBertMainLayer,
										    TFDistilBertModel,
										    TFDistilBertPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_distilbert import (
										    FlaxDistilBertForMaskedLM,
										    FlaxDistilBertForMultipleChoice,
										    FlaxDistilBertForQuestionAnswering,
										    FlaxDistilBertForSequenceClassification,
										    FlaxDistilBertForTokenClassification,
										    FlaxDistilBertModel,
										    FlaxDistilBertPreTrainedModel,
										)

else:
					import sys

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







"""simple docstring"""






import os
import sys

lowerCamelCase__ :       List[Any]						     =			os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCamelCase__ :       str						     =			[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]





@add_start_docstrings(AutoConfig.__doc__     )
def __A						(   *a_ :							Any		,		**a_ :							Union[str, Any]     )->							Dict:




		'''simple docstring'''






		return AutoConfig.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoTokenizer.__doc__     )
def __A						(   *a_ :							str		,		**a_ :							Union[str, Any]     )->							Union[str, Any]:




		'''simple docstring'''






		return AutoTokenizer.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModel.__doc__     )
def __A						(   *a_ :							List[str]		,		**a_ :							int     )->							Dict:




		'''simple docstring'''






		return AutoModel.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForCausalLM.__doc__     )
def __A						(   *a_ :							Any		,		**a_ :							Tuple     )->							Dict:




		'''simple docstring'''






		return AutoModelForCausalLM.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForMaskedLM.__doc__     )
def __A						(   *a_ :							Dict		,		**a_ :							Optional[Any]     )->							Optional[int]:




		'''simple docstring'''






		return AutoModelForMaskedLM.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForSequenceClassification.__doc__     )
def __A						(   *a_ :							Optional[int]		,		**a_ :							str     )->							Optional[int]:




		'''simple docstring'''






		return AutoModelForSequenceClassification.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__     )
def __A						(   *a_ :							List[str]		,		**a_ :							int     )->							List[Any]:




		'''simple docstring'''






		return AutoModelForQuestionAnswering.from_pretrained(*a_		,		**a_     )





