




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__)












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()






