


from typing import TYPE_CHECKING

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


snake_case							:       Union[str, Any]										=	{
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case							:       Dict										=	['''BloomTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case							:       List[Any]										=	[
         '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''BloomForCausalLM''',
         '''BloomModel''',
         '''BloomPreTrainedModel''',
         '''BloomForSequenceClassification''',
         '''BloomForTokenClassification''',
         '''BloomForQuestionAnswering''',
     ]

if TYPE_CHECKING:
     from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_bloom_fast import BloomTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_bloom import (
              BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
              BloomForCausalLM,
              BloomForQuestionAnswering,
              BloomForSequenceClassification,
              BloomForTokenClassification,
              BloomModel,
              BloomPreTrainedModel,
          )

else:
     import sys

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









from typing import TYPE_CHECKING

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


snake_case							:       Any										=	{
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case							:       List[Any]										=	[
         '''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:
     snake_case							:       Any										=	[
         '''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

     snake_case							:       Optional[int]										=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





