from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE   						=    {
    """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizer"""]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE   						=    [
         """OpenLlamaForCausalLM""",
         """OpenLlamaModel""",
         """OpenLlamaPreTrainedModel""",
         """OpenLlamaForSequenceClassification""",
     ]


if TYPE_CHECKING:
     from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from transformers import LlamaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from transformers import LlamaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_open_llama import (
              OpenLlamaForCausalLM,
              OpenLlamaForSequenceClassification,
              OpenLlamaModel,
              OpenLlamaPreTrainedModel,
          )


else:
     import sys

     _SCREAMING_SNAKE_CASE   						=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


def 				__lowercase	(						lowerCamelCase__ = "mytext.txt" ,				lowerCamelCase__ = False					)				->None:

  '''simple docstring'''




  import socket

  a__     		=     1_23_12  # Reserve a port for your service.
  a__     		=     socket.socket()  # Create a socket object
  a__     		=     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:
    a__		,a__     		=     sock.accept()  # Establish connection with client.
    print(f"Got connection from {addr}"					)
    a__     		=     conn.recv(10_24					)
    print(f"Server received: {data = }"					)

    with open(lowerCamelCase__ ,				"rb"					) as in_file:
      a__     		=     in_file.read(10_24					)
      while data:
        conn.send(lowerCamelCase__					)
        print(f"Sent {data!r}"					)
        a__     		=     in_file.read(10_24					)

    print("Done sending"					)
    conn.close()
    if testing:  # Allow the test to complete
      break

  sock.shutdown(1					)
  sock.close()


if __name__ == "__main__":
     send_file()

