


'''simple docstring'''







from typing import TYPE_CHECKING

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


A__:    str					    =       {
    '''configuration_lxmert''': ['''LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LxmertConfig'''],
    '''tokenization_lxmert''': ['''LxmertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Optional[int]					    =       ['''LxmertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Dict					    =       [
       '''LxmertEncoder''',
       '''LxmertForPreTraining''',
       '''LxmertForQuestionAnswering''',
       '''LxmertModel''',
       '''LxmertPreTrainedModel''',
       '''LxmertVisualFeatureEncoder''',
       '''LxmertXLayer''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Optional[Any]					    =       [
       '''TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFLxmertForPreTraining''',
       '''TFLxmertMainLayer''',
       '''TFLxmertModel''',
       '''TFLxmertPreTrainedModel''',
       '''TFLxmertVisualFeatureEncoder''',
   ]


if TYPE_CHECKING:
   from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
   from .tokenization_lxmert import LxmertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_lxmert_fast import LxmertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_lxmert import (
          LxmertEncoder,
          LxmertForPreTraining,
          LxmertForQuestionAnswering,
          LxmertModel,
          LxmertPreTrainedModel,
          LxmertVisualFeatureEncoder,
          LxmertXLayer,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_lxmert import (
          TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLxmertForPreTraining,
          TFLxmertMainLayer,
          TFLxmertModel,
          TFLxmertPreTrainedModel,
          TFLxmertVisualFeatureEncoder,
      )

else:
   import sys

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






'''simple docstring'''







A__:    Tuple					    =       '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

A__:    Tuple					    =       [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
A__:    Any					    =       {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}


