





"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_			=      {
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_			=      ["""LayoutLMv2TokenizerFast"""]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_			=      ["""LayoutLMv2FeatureExtractor"""]
							a_			=      ["""LayoutLMv2ImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_			=      [
							    """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """LayoutLMv2ForQuestionAnswering""",
							    """LayoutLMv2ForSequenceClassification""",
							    """LayoutLMv2ForTokenClassification""",
							    """LayoutLMv2Layer""",
							    """LayoutLMv2Model""",
							    """LayoutLMv2PreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
							from .processing_layoutlmva import LayoutLMvaProcessor
							from .tokenization_layoutlmva import LayoutLMvaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_layoutlmva import (
														    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LayoutLMvaForQuestionAnswering,
														    LayoutLMvaForSequenceClassification,
														    LayoutLMvaForTokenClassification,
														    LayoutLMvaLayer,
														    LayoutLMvaModel,
														    LayoutLMvaPreTrainedModel,
														)
else:
							import sys

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











"""simple docstring"""



from typing import TYPE_CHECKING

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


a_			=      {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""GPTNeoXTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTNeoXForCausalLM""",
           """GPTNeoXForQuestionAnswering""",
           """GPTNeoXForSequenceClassification""",
           """GPTNeoXForTokenClassification""",
           """GPTNeoXLayer""",
           """GPTNeoXModel""",
           """GPTNeoXPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neox import (
                  GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoXForCausalLM,
                  GPTNeoXForQuestionAnswering,
                  GPTNeoXForSequenceClassification,
                  GPTNeoXForTokenClassification,
                  GPTNeoXLayer,
                  GPTNeoXModel,
                  GPTNeoXPreTrainedModel,
              )


else:
       import sys

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




