





'''simple docstring'''




from typing import TYPE_CHECKING

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


lowercase__       : int         =       {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__       : Dict         =       ['''XGLMTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__       : int         =       ['''XGLMTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__       : List[Any]         =       [
      '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''XGLMForCausalLM''',
      '''XGLMModel''',
      '''XGLMPreTrainedModel''',
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__       : Optional[int]         =       [
      '''FlaxXGLMForCausalLM''',
      '''FlaxXGLMModel''',
      '''FlaxXGLMPreTrainedModel''',
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__       : Optional[int]         =       [
      '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFXGLMForCausalLM''',
      '''TFXGLMModel''',
      '''TFXGLMPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xglm import XGLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xglm_fast import XGLMTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xglm import (
        TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXGLMForCausalLM,
        TFXGLMModel,
        TFXGLMPreTrainedModel,
    )


else:
  import sys

  lowercase__       : Any         =       _LazyModule(__name__, globals()['''__file__'''], _import_structure)
'''simple docstring'''






from typing import TYPE_CHECKING

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


_UpperCAmelCase    :     List[Any]         =       {
    '''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:
   _UpperCAmelCase    :     str         =       ['''LayoutLMv2TokenizerFast''']

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     int         =       ['''LayoutLMv2FeatureExtractor''']
   _UpperCAmelCase    :     str         =       ['''LayoutLMv2ImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     int         =       [
       '''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

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



