
'''simple docstring'''





from typing import TYPE_CHECKING

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


a__		: int  							=							{
    'configuration_realm': ['REALM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RealmConfig'],
    'tokenization_realm': ['RealmTokenizer'],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__		: Any  							=							[
     'REALM_PRETRAINED_MODEL_ARCHIVE_LIST',
     'RealmEmbedder',
     'RealmForOpenQA',
     'RealmKnowledgeAugEncoder',
     'RealmPreTrainedModel',
     'RealmReader',
     'RealmScorer',
     'load_tf_weights_in_realm',
 ]
 a__		: str  							=							['RealmRetriever']


if TYPE_CHECKING:
 from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
 from .tokenization_realm import RealmTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_realm import RealmTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_realm import (
      REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
      RealmEmbedder,
      RealmForOpenQA,
      RealmKnowledgeAugEncoder,
      RealmPreTrainedModel,
      RealmReader,
      RealmScorer,
      load_tf_weights_in_realm,
  )
  from .retrieval_realm import RealmRetriever


else:
 import sys

 a__		: int  							=							_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_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_       :						Union[str, Any]					=    {'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						Optional[int]					=    [
     'GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GPTJForCausalLM',
     'GPTJForQuestionAnswering',
     'GPTJForSequenceClassification',
     'GPTJModel',
     'GPTJPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						List[str]					=    [
     'TFGPTJForCausalLM',
     'TFGPTJForQuestionAnswering',
     'TFGPTJForSequenceClassification',
     'TFGPTJModel',
     'TFGPTJPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						Tuple					=    [
     'FlaxGPTJForCausalLM',
     'FlaxGPTJModel',
     'FlaxGPTJPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gptj import (
      GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTJForCausalLM,
      GPTJForQuestionAnswering,
      GPTJForSequenceClassification,
      GPTJModel,
      GPTJPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_gptj import (
      TFGPTJForCausalLM,
      TFGPTJForQuestionAnswering,
      TFGPTJForSequenceClassification,
      TFGPTJModel,
      TFGPTJPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
 import sys

 SCREAMING_SNAKE_CASE_       :						List[str]					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






