


"""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_			=      getLogger(__name__)


a_			=      """cuda""" if torch.cuda.is_available() else """cpu"""







def 		__lowercase					(       snake_case_  :  List[str]    ,snake_case_  :  str    ,snake_case_  :  str    ,snake_case_  :  int = 8    ,snake_case_  :  str = DEFAULT_DEVICE    ,snake_case_  :  Any=False    ,snake_case_  :  Tuple="summarization"    ,snake_case_  :  Any=None    ,**snake_case_  :  Optional[int]    ,)       ->Dict:



    '''simple docstring'''







    __A   :						str									=					Path(snake_case_						).open('''w'''    ,encoding='''utf-8'''						)
    __A   :						Optional[int]									=					str(snake_case_						)
    __A   :						List[str]									=					AutoModelForSeqaSeqLM.from_pretrained(snake_case_						).to(snake_case_						)
    if fpaa:
        __A   :						Optional[int]									=					model.half()

    __A   :						Optional[Any]									=					AutoTokenizer.from_pretrained(snake_case_						)
    logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}"""						)  # if this is wrong, check config.model_type.

    __A   :						List[Any]									=					time.time()
    # update config with task specific params
    use_task_specific_params(snake_case_    ,snake_case_						)
    if prefix is None:
        __A   :						List[Any]									=					prefix or getattr(model.config    ,'''prefix'''    ,''''''						) or ''''''
    for examples_chunk in tqdm(list(chunks(snake_case_    ,snake_case_						)						)						):
        __A   :						int									=					[prefix + text for text in examples_chunk]
        __A   :						Dict									=					tokenizer(snake_case_    ,return_tensors='''pt'''    ,truncation=snake_case_    ,padding='''longest'''						).to(snake_case_						)
        __A   :						List[Any]									=					model.generate(
            input_ids=batch.input_ids    ,attention_mask=batch.attention_mask    ,**snake_case_    ,)
        __A   :						List[Any]									=					tokenizer.batch_decode(snake_case_    ,skip_special_tokens=snake_case_    ,clean_up_tokenization_spaces=snake_case_						)
        for hypothesis in dec:
            fout.write(hypothesis + '''\n'''						)
            fout.flush()
    fout.close()
    __A   :						Dict									=					int(time.time() - start_time						)  # seconds
    __A   :						str									=					len(snake_case_						)
    return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs    ,4						)}







def 		__lowercase					(       )       ->List[str]:



    '''simple docstring'''







    return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''						)







def 		__lowercase					(       snake_case_  :  Dict=True						)       ->Optional[int]:



    '''simple docstring'''







    __A   :						Any									=					argparse.ArgumentParser()
    parser.add_argument('''model_name'''    ,type=snake_case_    ,help='''like facebook/bart-large-cnn,t5-base, etc.'''						)
    parser.add_argument('''input_path'''    ,type=snake_case_    ,help='''like cnn_dm/test.source'''						)
    parser.add_argument('''save_path'''    ,type=snake_case_    ,help='''where to save summaries'''						)
    parser.add_argument('''--reference_path'''    ,type=snake_case_    ,required=snake_case_    ,help='''like cnn_dm/test.target'''						)
    parser.add_argument('''--score_path'''    ,type=snake_case_    ,required=snake_case_    ,default='''metrics.json'''    ,help='''where to save metrics'''						)
    parser.add_argument('''--device'''    ,type=snake_case_    ,required=snake_case_    ,default=snake_case_    ,help='''cuda, cuda:1, cpu etc.'''						)
    parser.add_argument(
        '''--prefix'''    ,type=snake_case_    ,required=snake_case_    ,default=snake_case_    ,help='''will be added to the begininng of src examples'''						)
    parser.add_argument('''--task'''    ,type=snake_case_    ,default='''summarization'''    ,help='''used for task_specific_params + metrics'''						)
    parser.add_argument('''--bs'''    ,type=snake_case_    ,default=8    ,required=snake_case_    ,help='''batch size'''						)
    parser.add_argument(
        '''--n_obs'''    ,type=snake_case_    ,default=-1    ,required=snake_case_    ,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=snake_case_    ,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
    __A   :						List[Any]									=					parser.parse_known_args()
    __A   :						Union[str, Any]									=					parse_numeric_n_bool_cl_kwargs(snake_case_						)
    if parsed_args and verbose:
        print(F"""parsed the following generate kwargs: {parsed_args}"""						)
    __A   :						List[str]									=					[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
    if args.n_obs > 0:
        __A   :						Dict									=					examples[: args.n_obs]
    Path(args.save_path						).parent.mkdir(exist_ok=snake_case_						)

    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."""						)

    if args.device == "cpu" and args.fpaa:
        # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
        raise ValueError('''Can\'t mix --fp16 and --device cpu'''						)

    __A   :						Optional[Any]									=					generate_summaries_or_translations(
        snake_case_    ,args.save_path    ,args.model_name    ,batch_size=args.bs    ,device=args.device    ,fpaa=args.fpaa    ,task=args.task    ,prefix=args.prefix    ,**snake_case_    ,)

    if args.reference_path is None:
        return {}

    # Compute scores
    __A   :						Any									=					calculate_bleu if '''translation''' in args.task else calculate_rouge
    __A   :						Any									=					[x.rstrip() for x in open(args.save_path						).readlines()]
    __A   :						List[Any]									=					[x.rstrip() for x in open(args.reference_path						).readlines()][: len(snake_case_						)]
    __A   :						dict									=					score_fn(snake_case_    ,snake_case_						)
    scores.update(snake_case_						)

    if args.dump_args:
        scores.update(snake_case_						)
    if args.info:
        __A   :						Union[str, Any]									=					args.info

    if verbose:
        print(snake_case_						)

    if args.score_path is not None:
        json.dump(snake_case_    ,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







def 		__lowercase					(       snake_case_  :  int = 8    ,snake_case_  :  int | None = None						)       ->str:



    '''simple docstring'''







    __A   :						str									=					np.random.default_rng(seed=snake_case_						)

    # Roughly 25% of the qubits will contribute to the key.
    # So we take more than we need.
    __A   :						str									=					6 * key_len
    # Measurement basis for Alice's qubits.
    __A   :						Any									=					rng.integers(2    ,size=snake_case_						)
    # The set of states Alice will prepare.
    __A   :						Any									=					rng.integers(2    ,size=snake_case_						)
    # Measurement basis for Bob's qubits.
    __A   :						str									=					rng.integers(2    ,size=snake_case_						)

    # Quantum Circuit to simulate BB84
    __A   :						Dict									=					qiskit.QuantumCircuit(snake_case_    ,name='''BB84'''						)

    # Alice prepares her qubits according to rules above.
    for index, _ in enumerate(snake_case_						):
        if alice_state[index] == 1:
            bbaa_circ.x(snake_case_						)
        if alice_basis[index] == 1:
            bbaa_circ.h(snake_case_						)
    bbaa_circ.barrier()

    # Bob measures the received qubits according to rules above.
    for index, _ in enumerate(snake_case_						):
        if bob_basis[index] == 1:
            bbaa_circ.h(snake_case_						)

    bbaa_circ.barrier()
    bbaa_circ.measure_all()

    # Simulate the quantum circuit.
    __A   :						List[str]									=					qiskit.Aer.get_backend('''aer_simulator'''						)
    # We only need to run one shot because the key is unique.
    # Multiple shots will produce the same key.
    __A   :						List[str]									=					qiskit.execute(snake_case_    ,snake_case_    ,shots=1    ,seed_simulator=snake_case_						)
    # Returns the result of measurement.
    __A   :						Union[str, Any]									=					job.result().get_counts(snake_case_						).most_frequent()

    # Extracting the generated key from the simulation results.
    # Only keep measurement results where Alice and Bob chose the same basis.
    __A   :						int									=					''''''.join(
        [
            result_bit
            for alice_basis_bit, bob_basis_bit, result_bit in zip(
                snake_case_    ,snake_case_    ,snake_case_						)
            if alice_basis_bit == bob_basis_bit
        ]						)

    # Get final key. Pad with 0 if too short, otherwise truncate.
    __A   :						Union[str, Any]									=					gen_key[:key_len] if len(snake_case_						) >= key_len else gen_key.ljust(snake_case_    ,'''0'''						)
    return key


if __name__ == "__main__":
       print(f'''The generated key is : {bbaa(8, seed=0)}''')
       from doctest import testmod

       testmod()




