"""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,
)


_a				:		int	   =       {
    'configuration_xlm_roberta': [
        'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaConfig',
        'XLMRobertaOnnxConfig',
    ],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a				:		List[Any]	   =       ['XLMRobertaTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a				:		Union[str, Any]	   =       ['XLMRobertaTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a				:		List[str]	   =       [
         'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
         'XLMRobertaForCausalLM',
         'XLMRobertaForMaskedLM',
         'XLMRobertaForMultipleChoice',
         'XLMRobertaForQuestionAnswering',
         'XLMRobertaForSequenceClassification',
         'XLMRobertaForTokenClassification',
         'XLMRobertaModel',
         'XLMRobertaPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a				:		Optional[int]	   =       [
         'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFXLMRobertaForCausalLM',
         'TFXLMRobertaForMaskedLM',
         'TFXLMRobertaForMultipleChoice',
         'TFXLMRobertaForQuestionAnswering',
         'TFXLMRobertaForSequenceClassification',
         'TFXLMRobertaForTokenClassification',
         'TFXLMRobertaModel',
         'TFXLMRobertaPreTrainedModel',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a				:		Union[str, Any]	   =       [
         'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
         'FlaxXLMRobertaForMaskedLM',
         'FlaxXLMRobertaForCausalLM',
         'FlaxXLMRobertaForMultipleChoice',
         'FlaxXLMRobertaForQuestionAnswering',
         'FlaxXLMRobertaForSequenceClassification',
         'FlaxXLMRobertaForTokenClassification',
         'FlaxXLMRobertaModel',
         'FlaxXLMRobertaPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_xlm_roberta import (
         XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
         XLMRobertaConfig,
         XLMRobertaOnnxConfig,
     )

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlm_roberta import XLMRobertaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlm_roberta import (
              XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLMRobertaForCausalLM,
              XLMRobertaForMaskedLM,
              XLMRobertaForMultipleChoice,
              XLMRobertaForQuestionAnswering,
              XLMRobertaForSequenceClassification,
              XLMRobertaForTokenClassification,
              XLMRobertaModel,
              XLMRobertaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xlm_roberta import (
              TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXLMRobertaForCausalLM,
              TFXLMRobertaForMaskedLM,
              TFXLMRobertaForMultipleChoice,
              TFXLMRobertaForQuestionAnswering,
              TFXLMRobertaForSequenceClassification,
              TFXLMRobertaForTokenClassification,
              TFXLMRobertaModel,
              TFXLMRobertaPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_xlm_roberta import (
              FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              FlaxXLMRobertaForCausalLM,
              FlaxXLMRobertaForMaskedLM,
              FlaxXLMRobertaForMultipleChoice,
              FlaxXLMRobertaForQuestionAnswering,
              FlaxXLMRobertaForSequenceClassification,
              FlaxXLMRobertaForTokenClassification,
              FlaxXLMRobertaModel,
              FlaxXLMRobertaPreTrainedModel,
          )

else:
     import sys

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



"""simple docstring"""




import socket





def 							SCREAMING_SNAKE_CASE					(      )	->				str:
  _lowerCAmelCase						:	Optional[int]        =					socket.socket(socket.AF_INET      ,socket.SOCK_STREAM     )
  _lowerCAmelCase						:	Optional[int]        =					socket.gethostname()
  _lowerCAmelCase						:	Tuple        =					12312

  sock.connect((host, port)     )
  sock.send(b"""Hello server!"""     )

  with open("""Received_file"""      ,"""wb"""     ) as out_file:
    print("""File opened"""     )
    print("""Receiving data..."""     )
    while True:
      _lowerCAmelCase						:	List[Any]        =					sock.recv(1024     )
      if not data:
        break
      out_file.write(_lowerCamelCase     )

  print("""Successfully received the file"""     )
  sock.close()
  print("""Connection closed"""     )


if __name__ == "__main__":
     main()




