




"""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)










"""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)}'''
  )




