

import qiskit







def 						lowerCamelCase__      ( __A    :int	,__A    :int		):


       """simple docstring"""




       __snake_case       	=							qiskit.Aer.get_backend("""aer_simulator"""		)

       # Create a Quantum Circuit acting on the q register
       __snake_case       	=							qiskit.QuantumCircuit(__A	,__A		)

       # Apply X (NOT) Gate to Qubits 0 & 1
       circuit.x(0		)
       circuit.x(1		)

       # Map the quantum measurement to the classical bits
       circuit.measure([0, 1]	,[0, 1]		)

       # Execute the circuit on the qasm simulator
       __snake_case       	=							qiskit.execute(__A	,__A	,shots=1_0_0_0		)

       # Return the histogram data of the results of the experiment.
       return job.result().get_counts(__A		)


if __name__ == "__main__":
    UpperCamelCase__		 =     single_qubit_measure(2, 2)
    print(F'Total count for various states are: {counts}')




import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCamelCase__		 =     logging.getLogger(__name__)







def 						lowerCamelCase__      ( __A    :Optional[int]	,__A    :str		):


       """simple docstring"""




       return (preds == labels).mean()

@dataclass
class     __snake_case    :
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}  )
       __SCREAMING_SNAKE_CASE				    =							field(
           default=snake_case__			,	metadata={"help": "Pretrained config name or path if not the same as model_name"}  )
       __SCREAMING_SNAKE_CASE				    =							field(
           default=snake_case__			,	metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}  )
       __SCREAMING_SNAKE_CASE				    =							field(
           default=snake_case__			,	metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,	)






@dataclass
class     __snake_case    :
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()  )}  )
       __SCREAMING_SNAKE_CASE				    =							field(metadata={"help": "Should contain the data files for the task."}  )
       __SCREAMING_SNAKE_CASE				    =							field(
           default=1_28			,	metadata={
               "help": (
                   "The maximum total input sequence length after tokenization. Sequences longer "
                   "than this will be truncated, sequences shorter will be padded."
               )
           }			,	)
       __SCREAMING_SNAKE_CASE				    =							field(
           default=snake_case__			,	metadata={"help": "Overwrite the cached training and evaluation sets"}  )







def 						lowerCamelCase__      ( ):


       """simple docstring"""




       __snake_case       	=							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
       __snake_case	,    __snake_case	,    __snake_case       	=							parser.parse_args_into_dataclasses()

       if (
           os.path.exists(training_args.output_dir		)
           and os.listdir(training_args.output_dir		)
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
                  """ --overwrite_output_dir to overcome."""		)

       # Setup logging
       logging.basicConfig(
           format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""	,datefmt="""%m/%d/%Y %H:%M:%S"""	,level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN	,)
       logger.warning(
           """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""	,training_args.local_rank	,training_args.device	,training_args.n_gpu	,bool(training_args.local_rank != -1		)	,training_args.fpaa	,)
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank		):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()
       logger.info("""Training/evaluation parameters %s"""	,__A		)

       # Set seed
       set_seed(training_args.seed		)

       try:
              __snake_case       	=							processors[data_args.task_name]()
              __snake_case       	=							processor.get_labels()
              __snake_case       	=							len(__A		)
       except KeyError:
              raise ValueError("""Task not found: %s""" % (data_args.task_name)		)

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       __snake_case       	=							AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path	,num_labels=__A	,finetuning_task=data_args.task_name	,cache_dir=model_args.cache_dir	,)
       __snake_case       	=							AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,cache_dir=model_args.cache_dir	,)
       __snake_case       	=							AutoModelForMultipleChoice.from_pretrained(
           model_args.model_name_or_path	,from_tf=bool(""".ckpt""" in model_args.model_name_or_path		)	,config=__A	,cache_dir=model_args.cache_dir	,)

       # Get datasets
       __snake_case       	=							(
           MultipleChoiceDataset(
               data_dir=data_args.data_dir	,tokenizer=__A	,task=data_args.task_name	,max_seq_length=data_args.max_seq_length	,overwrite_cache=data_args.overwrite_cache	,mode=Split.train	,)
           if training_args.do_train
           else None
       )
       __snake_case       	=							(
           MultipleChoiceDataset(
               data_dir=data_args.data_dir	,tokenizer=__A	,task=data_args.task_name	,max_seq_length=data_args.max_seq_length	,overwrite_cache=data_args.overwrite_cache	,mode=Split.dev	,)
           if training_args.do_eval
           else None
       )

       def compute_metrics(__A    :EvalPrediction		) -> Dict:
              __snake_case       	=							np.argmax(p.predictions	,axis=1		)
              return {"acc": simple_accuracy(__A	,p.label_ids		)}

       # Data collator
       __snake_case       	=							DataCollatorWithPadding(__A	,pad_to_multiple_of=8		) if training_args.fpaa else None

       # Initialize our Trainer
       __snake_case       	=							Trainer(
           model=__A	,args=__A	,train_dataset=__A	,eval_dataset=__A	,compute_metrics=__A	,data_collator=__A	,)

       # Training
       if training_args.do_train:
              trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
              trainer.save_model()
              # For convenience, we also re-save the tokenizer to the same directory,
              # so that you can share your model easily on huggingface.co/models =)
              if trainer.is_world_master():
                     tokenizer.save_pretrained(training_args.output_dir		)

    # Evaluation
       __snake_case       	=							{}
       if training_args.do_eval:
              logger.info("""*** Evaluate ***"""		)

              __snake_case       	=							trainer.evaluate()

              __snake_case       	=							os.path.join(training_args.output_dir	,"""eval_results.txt"""		)
              if trainer.is_world_master():
                     with open(__A	,"""w"""		) as writer:
                            logger.info("""***** Eval results *****"""		)
                            for key, value in result.items():
                                   logger.info("""  %s = %s"""	,__A	,__A		)
                                   writer.write("""%s = %s\n""" % (key, value)		)

                            results.update(__A		)

       return results







def 						lowerCamelCase__      ( __A    :str		):


       """simple docstring"""




       main()


if __name__ == "__main__":
    main()

