code: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _A							    =	{
      """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
  }
  
  try:
  							if not is_sentencepiece_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	["""CamembertTokenizer"""]
  
  try:
  							if not is_tokenizers_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	["""CamembertTokenizerFast"""]
  
  try:
  							if not is_torch_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """CamembertForCausalLM""",
  							    """CamembertForMaskedLM""",
  							    """CamembertForMultipleChoice""",
  							    """CamembertForQuestionAnswering""",
  							    """CamembertForSequenceClassification""",
  							    """CamembertForTokenClassification""",
  							    """CamembertModel""",
  							    """CamembertPreTrainedModel""",
  							]
  
  try:
  							if not is_tf_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """TFCamembertForCausalLM""",
  							    """TFCamembertForMaskedLM""",
  							    """TFCamembertForMultipleChoice""",
  							    """TFCamembertForQuestionAnswering""",
  							    """TFCamembertForSequenceClassification""",
  							    """TFCamembertForTokenClassification""",
  							    """TFCamembertModel""",
  							    """TFCamembertPreTrainedModel""",
  							]
  
  
  if TYPE_CHECKING:
  							from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig
  
  							try:
  														if not is_sentencepiece_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .tokenization_camembert import CamembertTokenizer
  
  							try:
  														if not is_tokenizers_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .tokenization_camembert_fast import CamembertTokenizerFast
  
  							try:
  														if not is_torch_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_camembert import (
  														    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    CamembertForCausalLM,
  														    CamembertForMaskedLM,
  														    CamembertForMultipleChoice,
  														    CamembertForQuestionAnswering,
  														    CamembertForSequenceClassification,
  														    CamembertForTokenClassification,
  														    CamembertModel,
  														    CamembertPreTrainedModel,
  														)
  
  							try:
  														if not is_tf_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_tf_camembert import (
  														    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    TFCamembertForCausalLM,
  														    TFCamembertForMaskedLM,
  														    TFCamembertForMultipleChoice,
  														    TFCamembertForQuestionAnswering,
  														    TFCamembertForSequenceClassification,
  														    TFCamembertForTokenClassification,
  														    TFCamembertModel,
  														    TFCamembertPreTrainedModel,
  														)
  
  else:
  							import sys
  
  							_A							    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  def 						lowerCAmelCase_       (	__a = "mytext.txt"	,	__a = False )       ->			None:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							import socket
  
  							SCREAMING_SNAKE_CASE			: int	     =12312  # Reserve a port for your service.
  							SCREAMING_SNAKE_CASE			: Dict	     =socket.socket()  # Create a socket object
  							SCREAMING_SNAKE_CASE			: Any	     =socket.gethostname()  # Get local machine name
  							sock.bind((host, port) )  # Bind to the port
  							sock.listen(5 )  # Now wait for client connection.
  
  							print('''Server listening....''' )
  
  							while True:
  														SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Optional[int]	     =sock.accept()  # Establish connection with client.
  														print(f'Got connection from {addr}' )
  														SCREAMING_SNAKE_CASE			: List[str]	     =conn.recv(1024 )
  														print(f'Server received: {data = }' )
  
  														with open(__a	,	'''rb''' ) as in_file:
  																					SCREAMING_SNAKE_CASE			: Dict	     =in_file.read(1024 )
  																					while data:
  																												conn.send(__a )
  																												print(f'Sent {data!r}' )
  																												SCREAMING_SNAKE_CASE			: str	     =in_file.read(1024 )
  
  														print('''Done sending''' )
  														conn.close()
  														if testing:  # Allow the test to complete
  																					break
  
  							sock.shutdown(1 )
  							sock.close()
  
  
  if __name__ == "__main__":
  							send_file()
  
  
  
  
  
  
  