



'''simple docstring'''





from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional

from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


try:
    from fastapi import Body, FastAPI, HTTPException
    from fastapi.routing import APIRoute
    from pydantic import BaseModel
    from starlette.responses import JSONResponse
    from uvicorn import run

    A__    :   List[Any]           =					True
except (ImportError, AttributeError):
    A__    :   Any           =					object





    def   a_   (		*_UpperCAmelCase    :      Union[str, Any] ,**_UpperCAmelCase    :      List[str]				)    ->  List[str]:
          pass





    A__    :   Dict           =					False


A__    :   Tuple           =					logging.get_logger('''transformers-cli/serving''')





def   a_   (		_UpperCAmelCase    :      Namespace				)    ->  Dict:
      __snake_case :   List[Any]													=    pipeline(
          task=args.task ,model=args.model if args.model else None ,config=args.config ,tokenizer=args.tokenizer ,device=args.device ,)
      return ServeCommand(_UpperCAmelCase ,args.host ,args.port ,args.workers				)



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= 42



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= 42
 A__				= 42



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= 42



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= 42



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 @staticmethod
 def A_   (   __a						:							ArgumentParser  )      ->   Any:







       '''simple docstring'''
       __snake_case :   Any													=    parser.add_parser(
           'serve'      ,     help='CLI tool to run inference requests through REST and GraphQL endpoints.'  )
       serve_parser.add_argument(
           '--task'      ,     type=__a      ,     choices=get_supported_tasks()      ,     help='The task to run the pipeline on'      ,     )
       serve_parser.add_argument('--host'      ,     type=__a      ,     default='localhost'      ,     help='Interface the server will listen on.'  )
       serve_parser.add_argument('--port'      ,     type=__a      ,     default=8888      ,     help='Port the serving will listen to.'  )
       serve_parser.add_argument('--workers'      ,     type=__a      ,     default=1      ,     help='Number of http workers'  )
       serve_parser.add_argument('--model'      ,     type=__a      ,     help='Model\'s name or path to stored model.'  )
       serve_parser.add_argument('--config'      ,     type=__a      ,     help='Model\'s config name or path to stored model.'  )
       serve_parser.add_argument('--tokenizer'      ,     type=__a      ,     help='Tokenizer name to use.'  )
       serve_parser.add_argument(
           '--device'      ,     type=__a      ,     default=-1      ,     help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'      ,     )
       serve_parser.set_defaults(func=__a  )
 def __init__(   self						:							str      ,     __a						:							Pipeline      ,     __a						:							str      ,     __a						:							int      ,     __a						:							int  )      ->   int:







       '''simple docstring'''
       __snake_case :   str													=    pipeline

       __snake_case :   Union[str, Any]													=    host
       __snake_case :   Dict													=    port
       __snake_case :   Dict													=    workers

       if not _serve_dependencies_installed:
             raise RuntimeError(
                 'Using serve command requires FastAPI and uvicorn. '
                 'Please install transformers with [serving]: pip install "transformers[serving]".'
                 'Or install FastAPI and uvicorn separately.'  )
       else:
             logger.info(f'''Serving model over {host}:{port}'''  )
             __snake_case :   Any													=    FastAPI(
                 routes=[
                     APIRoute(
                         '/'      ,     self.model_info      ,     response_model=__a      ,     response_class=__a      ,     methods=['GET']      ,     ),
                     APIRoute(
                         '/tokenize'      ,     self.tokenize      ,     response_model=__a      ,     response_class=__a      ,     methods=['POST']      ,     ),
                     APIRoute(
                         '/detokenize'      ,     self.detokenize      ,     response_model=__a      ,     response_class=__a      ,     methods=['POST']      ,     ),
                     APIRoute(
                         '/forward'      ,     self.forward      ,     response_model=__a      ,     response_class=__a      ,     methods=['POST']      ,     ),
                 ]      ,     timeout=600      ,     )
 def A_   (   self						:							List[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       run(self._app      ,     host=self.host      ,     port=self.port      ,     workers=self.workers  )
 def A_   (   self						:							List[Any]  )      ->   int:







       '''simple docstring'''
       return ServeModelInfoResult(infos=vars(self._pipeline.model.config  )  )
 def A_   (   self						:							List[str]      ,     __a						:							str = Body(__a      ,     embed=__a  )      ,     __a						:							bool = Body(__a      ,     embed=__a  )  )      ->   Optional[Any]:







       '''simple docstring'''
       try:
             __snake_case :   Dict													=    self._pipeline.tokenizer.tokenize(__a  )

             if return_ids:
                   __snake_case :   Tuple													=    self._pipeline.tokenizer.convert_tokens_to_ids(__a  )
                   return ServeTokenizeResult(tokens=__a      ,     tokens_ids=__a  )
             else:
                   return ServeTokenizeResult(tokens=__a  )

       except Exception as e:
             raise HTTPException(status_code=500      ,     detail={'model': '', 'error': str(__a  )}  )
 def A_   (   self						:							Any      ,     __a						:							List[int] = Body(__a      ,     embed=__a  )      ,     __a						:							bool = Body(__a      ,     embed=__a  )      ,     __a						:							bool = Body(__a      ,     embed=__a  )      ,     )      ->   Tuple:







       '''simple docstring'''
       try:
             __snake_case :   Any													=    self._pipeline.tokenizer.decode(__a      ,     __a      ,     __a  )
             return ServeDeTokenizeResult(model=''      ,     text=__a  )
       except Exception as e:
             raise HTTPException(status_code=500      ,     detail={'model': '', 'error': str(__a  )}  )





 async def A_   (   self						:							List[Any]      ,     __a						:							Optional[Any]=Body(__a      ,     embed=__a  )  )      ->   Dict:







       '''simple docstring'''

       # Check we don't have empty string
       if len(__a  ) == 0:
             return ServeForwardResult(output=[]      ,     attention=[]  )

       try:
             # Forward through the model
             __snake_case :   List[Any]													=    self._pipeline(__a  )
             return ServeForwardResult(output=__a  )
       except Exception as e:
             raise HTTPException(500      ,     {'error': str(__a  )}  )








'''simple docstring'''





import qiskit





def   a_   (		_UpperCAmelCase    :      int = 2				)    ->  qiskit.result.counts.Counts:
      __snake_case :   Union[str, Any]													=    qubits

      # Using Aer's simulator
      __snake_case :   List[Any]													=    qiskit.Aer.get_backend('aer_simulator'				)

      # Creating a Quantum Circuit acting on the q register
      __snake_case :   Dict													=    qiskit.QuantumCircuit(_UpperCAmelCase ,_UpperCAmelCase				)

      # Adding a H gate on qubit 0 (now q0 in superposition)
      circuit.h(0				)

      for i in range(1 ,_UpperCAmelCase				):
            # Adding CX (CNOT) gate
            circuit.cx(i - 1 ,_UpperCAmelCase				)

      # Mapping the quantum measurement to the classical bits
      circuit.measure(list(range(_UpperCAmelCase				)				) ,list(range(_UpperCAmelCase				)				)				)

      # Now measuring any one qubit would affect other qubits to collapse
      # their super position and have same state as the measured one.

      # Executing the circuit on the simulator
      __snake_case :   Optional[Any]													=    qiskit.execute(_UpperCAmelCase ,_UpperCAmelCase ,shots=10_00				)

      return job.result().get_counts(_UpperCAmelCase				)


if __name__ == "__main__":
    print(F"""Total count for various states are: {quantum_entanglement(3)}""")



