code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import datetime
  import json
  import time
  import warnings
  from logging import getLogger
  from pathlib import Path
  from typing import Dict, List
  
  import torch
  from tqdm import tqdm
  
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
  from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
  
  
  __A :		Any      							=    getLogger(__name__)
  
  
  __A :		Tuple      							=    """cuda""" if torch.cuda.is_available() else """cpu"""
  def 					A_			(		snake_case_     :       int						,snake_case_     :       Tuple						,snake_case_     :       Optional[int]						,snake_case_     :       List[Any] = 8						,snake_case_     :       Union[str, Any] = DEFAULT_DEVICE						,snake_case_     :       int=False						,snake_case_     :       str="summarization"						,snake_case_     :       Optional[Any]=None						,**snake_case_     :       int						,):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   List[Any]          =							Path(__lowerCamelCase    ).open("""w"""						,encoding="""utf-8"""    )
      UpperCamelCase				:   Union[str, Any]          =							str(__lowerCamelCase    )
      UpperCamelCase				:   Dict          =							AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase    ).to(__lowerCamelCase    )
      if fpaa:
          UpperCamelCase				:   List[Any]          =							model.half()
  
      UpperCamelCase				:   int          =							AutoTokenizer.from_pretrained(__lowerCamelCase    )
      logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'    )  # if this is wrong, check config.model_type.
  
      UpperCamelCase				:   List[Any]          =							time.time()
      # update config with task specific params
      use_task_specific_params(__lowerCamelCase						,__lowerCamelCase    )
      if prefix is None:
          UpperCamelCase				:   Optional[Any]          =							prefix or getattr(model.config						,"""prefix"""						,""""""    ) or """"""
      for examples_chunk in tqdm(list(chunks(__lowerCamelCase						,__lowerCamelCase    )    )    ):
          UpperCamelCase				:   str          =							[prefix + text for text in examples_chunk]
          UpperCamelCase				:   Any          =							tokenizer(__lowerCamelCase						,return_tensors="""pt"""						,truncation=__lowerCamelCase						,padding="""longest"""    ).to(__lowerCamelCase    )
          UpperCamelCase				:   Optional[Any]          =							model.generate(
              input_ids=batch.input_ids						,attention_mask=batch.attention_mask						,**__lowerCamelCase						,)
          UpperCamelCase				:   Any          =							tokenizer.batch_decode(__lowerCamelCase						,skip_special_tokens=__lowerCamelCase						,clean_up_tokenization_spaces=__lowerCamelCase    )
          for hypothesis in dec:
              fout.write(hypothesis + """\n"""    )
              fout.flush()
      fout.close()
      UpperCamelCase				:   Union[str, Any]          =							int(time.time() - start_time    )  # seconds
      UpperCamelCase				:   List[str]          =							len(__lowerCamelCase    )
      return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs						,4    )}
  def 					A_			(		):
  
  
  
  
  
  
  
      '''simple docstring'''
      return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S"""    )
  def 					A_			(		snake_case_     :       str=True    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   List[Any]          =							argparse.ArgumentParser()
      parser.add_argument("""model_name"""						,type=__lowerCamelCase						,help="""like facebook/bart-large-cnn,t5-base, etc."""    )
      parser.add_argument("""input_path"""						,type=__lowerCamelCase						,help="""like cnn_dm/test.source"""    )
      parser.add_argument("""save_path"""						,type=__lowerCamelCase						,help="""where to save summaries"""    )
      parser.add_argument("""--reference_path"""						,type=__lowerCamelCase						,required=__lowerCamelCase						,help="""like cnn_dm/test.target"""    )
      parser.add_argument("""--score_path"""						,type=__lowerCamelCase						,required=__lowerCamelCase						,default="""metrics.json"""						,help="""where to save metrics"""    )
      parser.add_argument("""--device"""						,type=__lowerCamelCase						,required=__lowerCamelCase						,default=__lowerCamelCase						,help="""cuda, cuda:1, cpu etc."""    )
      parser.add_argument(
          """--prefix"""						,type=__lowerCamelCase						,required=__lowerCamelCase						,default=__lowerCamelCase						,help="""will be added to the begininng of src examples"""    )
      parser.add_argument("""--task"""						,type=__lowerCamelCase						,default="""summarization"""						,help="""used for task_specific_params + metrics"""    )
      parser.add_argument("""--bs"""						,type=__lowerCamelCase						,default=8						,required=__lowerCamelCase						,help="""batch size"""    )
      parser.add_argument(
          """--n_obs"""						,type=__lowerCamelCase						,default=-1						,required=__lowerCamelCase						,help="""How many observations. Defaults to all."""    )
      parser.add_argument("""--fp16"""						,action="""store_true"""    )
      parser.add_argument("""--dump-args"""						,action="""store_true"""						,help="""print the custom hparams with the results"""    )
      parser.add_argument(
          """--info"""						,nargs="""?"""						,type=__lowerCamelCase						,const=datetime_now()						,help=(
              """use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."""
              """ lang=en-ru. If no value is passed, the current datetime string will be used."""
          )						,)
      # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
      UpperCamelCase				:   List[str]          =							parser.parse_known_args()
      UpperCamelCase				:   Any          =							parse_numeric_n_bool_cl_kwargs(__lowerCamelCase    )
      if parsed_args and verbose:
          print(f'parsed the following generate kwargs: {parsed_args}'    )
      with open(args.input_path    ) as f:
          UpperCamelCase				:   Any          =							[""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in f.readlines()]
      if args.n_obs > 0:
          UpperCamelCase				:   Tuple          =							examples[: args.n_obs]
      Path(args.save_path    ).parent.mkdir(exist_ok=__lowerCamelCase    )
      if args.reference_path is None and Path(args.score_path    ).exists():
          warnings.warn(f'score_path {args.score_path} will be overwritten unless you type ctrl-c.'    )
      UpperCamelCase				:   Union[str, Any]          =							generate_summaries_or_translations(
          __lowerCamelCase						,args.save_path						,args.model_name						,batch_size=args.bs						,device=args.device						,fpaa=args.fpaa						,task=args.task						,prefix=args.prefix						,**__lowerCamelCase						,)
  
      if args.reference_path is None:
          return {}
  
      # Compute scores
      UpperCamelCase				:   Optional[Any]          =							calculate_bleu if """translation""" in args.task else calculate_rouge
      UpperCamelCase				:   int          =							[x.rstrip() for x in open(args.save_path    ).readlines()]
      UpperCamelCase				:   Any          =							[x.rstrip() for x in open(args.reference_path    ).readlines()][: len(__lowerCamelCase    )]
      UpperCamelCase				:   dict          =							score_fn(__lowerCamelCase						,__lowerCamelCase    )
      scores.update(__lowerCamelCase    )
  
      if args.dump_args:
          scores.update(__lowerCamelCase    )
      if args.info:
          UpperCamelCase				:   Union[str, Any]          =							args.info
  
      if verbose:
          print(__lowerCamelCase    )
  
      if args.score_path is not None:
          json.dump(__lowerCamelCase						,open(args.score_path						,"""w"""    )    )
  
      return scores
  
  
  if __name__ == "__main__":
         # Usage for MT:
         # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
         run_generate(verbose=True)
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  import qiskit
  from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
  def 					A_			(		snake_case_     :       float = np.pi / 2						,snake_case_     :       float = np.pi / 2						,snake_case_     :       float = np.pi / 2    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Union[str, Any]          =							QuantumRegister(3						,"""qr"""    )  # Define the number of quantum bits
      UpperCamelCase				:   Any          =							ClassicalRegister(1						,"""cr"""    )  # Define the number of classical bits
  
      UpperCamelCase				:   Any          =							QuantumCircuit(snake_case_						,snake_case_    )  # Define the quantum circuit.
  
      # Build the circuit
      quantum_circuit.u(snake_case_						,snake_case_						,snake_case_						,0    )  # Quantum State to teleport
      quantum_circuit.h(1    )  # add hadamard gate
      quantum_circuit.cx(
          1						,2    )  # add control gate with qubit 1 as control and 2 as target.
      quantum_circuit.cx(0						,1    )
      quantum_circuit.h(0    )
      quantum_circuit.cz(0						,2    )  # add control z gate.
      quantum_circuit.cx(1						,2    )
  
      quantum_circuit.measure([2]						,[0]    )  # measure the qubit.
  
      # Simulate the circuit using qasm simulator
      UpperCamelCase				:   Tuple          =							Aer.get_backend("""aer_simulator"""    )
      UpperCamelCase				:   Any          =							execute(snake_case_						,snake_case_						,shots=1_0_0_0    )
  
      return job.result().get_counts(snake_case_    )
  
  
  if __name__ == "__main__":
    print(
        '''Total count for teleported state is: '''
        F'''{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'''
    )
  
  
  
  
  