





"""simple docstring"""



from typing import TYPE_CHECKING

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


a_			=      {
    """configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
    """tokenization_ctrl""": ["""CTRLTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_			=      [
							    """CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CTRLForSequenceClassification""",
							    """CTRLLMHeadModel""",
							    """CTRLModel""",
							    """CTRLPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_			=      [
							    """TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFCTRLForSequenceClassification""",
							    """TFCTRLLMHeadModel""",
							    """TFCTRLModel""",
							    """TFCTRLPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
							from .tokenization_ctrl import CTRLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ctrl import (
														    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CTRLForSequenceClassification,
														    CTRLLMHeadModel,
														    CTRLModel,
														    CTRLPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_ctrl import (
														    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCTRLForSequenceClassification,
														    TFCTRLLMHeadModel,
														    TFCTRLModel,
														    TFCTRLPreTrainedModel,
														)

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_tokenizers_available, is_torch_available


a_			=      {
    """configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""BloomTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """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

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




