



import qiskit






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Tuple       =	qiskit.Aer.get_backend("aer_simulator"     )

 # Create a Quantum Circuit acting on the q register
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	qiskit.QuantumCircuit(__lowerCamelCase		,	__lowerCamelCase     )

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

 # Execute the circuit on the simulator
 __SCREAMING_SNAKE_CASE       :    Dict       =	qiskit.execute(__lowerCamelCase		,	__lowerCamelCase		,	shots=1000     )

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


if __name__ == "__main__":
      print(f'''Total count for various states are: {single_qubit_measure(1, 1)}''')











import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_lowerCamelCase						    =  logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

_lowerCamelCase						    =  {
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_lowerCamelCase						    =  {
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_lowerCamelCase						    =  sorted(arg_to_scheduler.keys())
_lowerCamelCase						    =  """{""" + """, """.join(arg_to_scheduler_choices) + """}"""

class       _SCREAMING_SNAKE_CASE	(pl.LightningModule ):





     def __init__(       self :							Dict     ,  UpperCamelCase :							argparse.Namespace     ,  UpperCamelCase :							str=None     ,  UpperCamelCase :							Tuple="base"     ,  UpperCamelCase :							Optional[Any]=None     ,  UpperCamelCase :							List[Any]=None     ,  UpperCamelCase :							Optional[int]=None     ,  **UpperCamelCase :							int     ,  )->Dict:
      super().__init__()
      # TODO: move to self.save_hyperparameters()
      # self.save_hyperparameters()
      # can also expand arguments into trainer signature for easier reading

      self.save_hyperparameters(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    int       =	0
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	Path(self.hparams.output_dir				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	AutoConfig.from_pretrained(
           self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path     ,  **({"num_labels": num_labels} if num_labels is not None else {})     ,  cache_dir=UpperCamelCase     ,  **UpperCamelCase     ,  )
      else:
       __SCREAMING_SNAKE_CASE       :    PretrainedConfig       =	config

      __SCREAMING_SNAKE_CASE       :    Any       =	("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
      for p in extra_model_params:
       if getattr(self.hparams     ,  UpperCamelCase     ,  UpperCamelCase				):
        assert hasattr(self.config     ,  UpperCamelCase				), F"""model config doesn't have a `{p}` attribute"""
        setattr(self.config     ,  UpperCamelCase     ,  getattr(self.hparams     ,  UpperCamelCase				)				)

      if tokenizer is None:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	AutoTokenizer.from_pretrained(
           self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,  cache_dir=UpperCamelCase     ,  )
      else:
       __SCREAMING_SNAKE_CASE       :    PreTrainedTokenizer       =	tokenizer
      __SCREAMING_SNAKE_CASE       :    str       =	MODEL_MODES[mode]
      if model is None:
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.model_type.from_pretrained(
           self.hparams.model_name_or_path     ,  from_tf=bool(".ckpt" in self.hparams.model_name_or_path				)     ,  config=self.config     ,  cache_dir=UpperCamelCase     ,  )
      else:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	model





     def 	__snake_case    (       self :							Dict     ,  *UpperCamelCase :							Dict     ,  **UpperCamelCase :							Union[str, Any]				)->Optional[Any]:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.model_type.from_pretrained(*UpperCamelCase     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							Optional[Any]				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	arg_to_scheduler[self.hparams.lr_scheduler]
      __SCREAMING_SNAKE_CASE       :    List[str]       =	get_schedule_func(
          self.opt     ,  num_warmup_steps=self.hparams.warmup_steps     ,  num_training_steps=self.total_steps()				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	{"scheduler": scheduler, "interval": "step", "frequency": 1}
      return scheduler





     def 	__snake_case    (       self :							int				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.model
      __SCREAMING_SNAKE_CASE       :    Any       =	["bias", "LayerNorm.weight"]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[
          {
              "params": [
                  p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay				)
              ],  # check this named paramters
              "weight_decay": self.hparams.weight_decay,
          },
          {
              "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay				)],
              "weight_decay": 0.0,
          },
      ]
      if self.hparams.adafactor:
       __SCREAMING_SNAKE_CASE       :    Dict       =	Adafactor(
           UpperCamelCase     ,  lr=self.hparams.learning_rate     ,  scale_parameter=UpperCamelCase     ,  relative_step=UpperCamelCase				)

      else:
       __SCREAMING_SNAKE_CASE       :    Tuple       =	AdamW(
           UpperCamelCase     ,  lr=self.hparams.learning_rate     ,  eps=self.hparams.adam_epsilon				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	optimizer

      __SCREAMING_SNAKE_CASE       :    Any       =	self.get_lr_scheduler()

      return [optimizer], [scheduler]





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							Any     ,  UpperCamelCase :							Any				)->Optional[Any]:
      return self.validation_step(UpperCamelCase     ,  UpperCamelCase				)





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							Tuple				)->List[Any]:
      return self.validation_end(UpperCamelCase				)





     def 	__snake_case    (       self :							str				)->int:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	max(1     ,  self.hparams.gpus				)  # TODO: consider num_tpu_cores
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
      return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Tuple				)->Optional[Any]:
      if stage == "test":
       __SCREAMING_SNAKE_CASE       :    List[str]       =	len(self.test_dataloader().dataset				)
      else:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.get_dataloader("train"     ,  self.hparams.train_batch_size     ,  shuffle=UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    Tuple       =	len(self.train_dataloader().dataset				)





     def 	__snake_case    (       self :							Optional[int]     ,  UpperCamelCase :							str     ,  UpperCamelCase :							int     ,  UpperCamelCase :							bool = False				)->List[Any]:
      raise NotImplementedError("You must implement this for your task"				)





     def 	__snake_case    (       self :							List[Any]				)->Union[str, Any]:
      return self.train_loader





     def 	__snake_case    (       self :							Tuple				)->Union[str, Any]:
      return self.get_dataloader("dev"     ,  self.hparams.eval_batch_size     ,  shuffle=UpperCamelCase				)





     def 	__snake_case    (       self :							Optional[Any]				)->Any:
      return self.get_dataloader("test"     ,  self.hparams.eval_batch_size     ,  shuffle=UpperCamelCase				)





     def 	__snake_case    (       self :							int     ,  UpperCamelCase :							List[str]				)->int:
      return os.path.join(
          self.hparams.data_dir     ,  "cached_{}_{}_{}".format(
              UpperCamelCase     ,  list(filter(UpperCamelCase     ,  self.hparams.model_name_or_path.split("/"				)				)				).pop()     ,  str(self.hparams.max_seq_length				)     ,  )     ,  )





     @pl.utilities.rank_zero_only
     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Dict[str, Any]				)->None:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.output_dir.joinpath("best_tfmr"				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.step_count
      self.model.save_pretrained(UpperCamelCase				)
      self.tokenizer.save_pretrained(UpperCamelCase				)






     @staticmethod
     def 	__snake_case    (       UpperCamelCase :							Any     ,  UpperCamelCase :							Optional[Any]				)->List[str]:
      parser.add_argument(
          "--model_name_or_path"     ,  default=UpperCamelCase     ,  type=UpperCamelCase     ,  required=UpperCamelCase     ,  help="Path to pretrained model or model identifier from huggingface.co/models"     ,  )
      parser.add_argument(
          "--config_name"     ,  default=""     ,  type=UpperCamelCase     ,  help="Pretrained config name or path if not the same as model_name"				)
      parser.add_argument(
          "--tokenizer_name"     ,  default=UpperCamelCase     ,  type=UpperCamelCase     ,  help="Pretrained tokenizer name or path if not the same as model_name"     ,  )
      parser.add_argument(
          "--cache_dir"     ,  default=str(Path(UpperCamelCase				).parent / "test_run" / "cache"				)     ,  type=UpperCamelCase     ,  help="Where do you want to store the pre-trained models downloaded from huggingface.co"     ,  )
      parser.add_argument(
          "--encoder_layerdrop"     ,  type=UpperCamelCase     ,  help="Encoder layer dropout probability (Optional). Goes into model.config"     ,  )
      parser.add_argument(
          "--decoder_layerdrop"     ,  type=UpperCamelCase     ,  help="Decoder layer dropout probability (Optional). Goes into model.config"     ,  )
      parser.add_argument(
          "--dropout"     ,  type=UpperCamelCase     ,  help="Dropout probability (Optional). Goes into model.config"     ,  )
      parser.add_argument(
          "--attention_dropout"     ,  type=UpperCamelCase     ,  help="Attention dropout probability (Optional). Goes into model.config"     ,  )
      parser.add_argument("--learning_rate"     ,  default=5E-5     ,  type=UpperCamelCase     ,  help="The initial learning rate for Adam."				)
      parser.add_argument(
          "--lr_scheduler"     ,  default="linear"     ,  choices=UpperCamelCase     ,  metavar=UpperCamelCase     ,  type=UpperCamelCase     ,  help="Learning rate scheduler"     ,  )
      parser.add_argument("--weight_decay"     ,  default=0.0     ,  type=UpperCamelCase     ,  help="Weight decay if we apply some."				)
      parser.add_argument("--adam_epsilon"     ,  default=1E-8     ,  type=UpperCamelCase     ,  help="Epsilon for Adam optimizer."				)
      parser.add_argument("--warmup_steps"     ,  default=0     ,  type=UpperCamelCase     ,  help="Linear warmup over warmup_steps."				)
      parser.add_argument("--num_workers"     ,  default=4     ,  type=UpperCamelCase     ,  help="kwarg passed to DataLoader"				)
      parser.add_argument("--num_train_epochs"     ,  dest="max_epochs"     ,  default=3     ,  type=UpperCamelCase				)
      parser.add_argument("--train_batch_size"     ,  default=3_2     ,  type=UpperCamelCase				)
      parser.add_argument("--eval_batch_size"     ,  default=3_2     ,  type=UpperCamelCase				)
      parser.add_argument("--adafactor"     ,  action="store_true"				)






class       _SCREAMING_SNAKE_CASE	(pl.Callback ):





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Tuple				)->Tuple:
      if (
          trainer.is_global_zero and trainer.global_rank == 0
      ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
       pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class       _SCREAMING_SNAKE_CASE	(pl.Callback ):





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							Optional[Any]     ,  UpperCamelCase :							Optional[Any]				)->Tuple:
      # print(pl_module.model.rag)
      for name, param in pl_module.model.rag.named_parameters():
       if param.grad is None:
        print(UpperCamelCase				)






class       _SCREAMING_SNAKE_CASE	(pl.Callback ):





     def 	__snake_case    (       self :							Optional[int]     ,  UpperCamelCase :							Dict     ,  UpperCamelCase :							Dict				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	trainer.lr_schedulers[0]["scheduler"]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	{F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
      pl_module.logger.log_metrics(UpperCamelCase				)





     def 	__snake_case    (       self :							Tuple     ,  UpperCamelCase :							pl.Trainer     ,  UpperCamelCase :							pl.LightningModule				)->Union[str, Any]:
      rank_zero_info("***** Validation results *****"				)
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	trainer.callback_metrics
      # Log results
      for key in sorted(UpperCamelCase				):
       if key not in ["log", "progress_bar"]:
        rank_zero_info("{} = {}\n".format(UpperCamelCase     ,  str(metrics[key]				)				)				)






     def 	__snake_case    (       self :							str     ,  UpperCamelCase :							pl.Trainer     ,  UpperCamelCase :							pl.LightningModule				)->Optional[Any]:
      rank_zero_info("***** Test results *****"				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	trainer.callback_metrics
      # Log and save results to file
      __SCREAMING_SNAKE_CASE       :    List[str]       =	os.path.join(pl_module.hparams.output_dir     ,  "test_results.txt"				)
      with open(UpperCamelCase     ,  "w"				) as writer:
       for key in sorted(UpperCamelCase				):
        if key not in ["log", "progress_bar"]:
         rank_zero_info("{} = {}\n".format(UpperCamelCase     ,  str(metrics[key]				)				)				)
         writer.write("{} = {}\n".format(UpperCamelCase     ,  str(metrics[key]				)				)				)






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 parser.add_argument(
     "--output_dir"		,	default=str(Path(__lowerCamelCase     ).parent / "test_run" / "model_checkpoints"     )		,	type=__lowerCamelCase		,	help="The output directory where the model predictions and checkpoints will be written."		,	)
 parser.add_argument(
     "--fp16"		,	action="store_true"		,	help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"		,	)

 parser.add_argument(
     "--fp16_opt_level"		,	type=__lowerCamelCase		,	default="O2"		,	help=(
         "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
         "See details at https://nvidia.github.io/apex/amp.html"
     )		,	)
 parser.add_argument("--n_tpu_cores"		,	dest="tpu_cores"		,	type=__lowerCamelCase     )
 parser.add_argument("--max_grad_norm"		,	dest="gradient_clip_val"		,	default=1.0		,	type=__lowerCamelCase		,	help="Max gradient norm"     )
 parser.add_argument("--do_train"		,	action="store_true"		,	help="Whether to run training."     )
 parser.add_argument("--do_predict"		,	action="store_true"		,	help="Whether to run predictions on the test set."     )
 parser.add_argument(
     "--gradient_accumulation_steps"		,	dest="accumulate_grad_batches"		,	type=__lowerCamelCase		,	default=1		,	help="Number of updates steps to accumulate before performing a backward/update pass."		,	)
 parser.add_argument("--seed"		,	type=__lowerCamelCase		,	default=42		,	help="random seed for initialization"     )
 parser.add_argument(
     "--data_dir"		,	default=str(Path(__lowerCamelCase     ).parent / "test_run" / "dummy-train-data"     )		,	type=__lowerCamelCase		,	help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."		,	)






def 	_lowerCAmelCase						(				__lowerCamelCase   :     BaseTransformer		,	__lowerCamelCase   :     argparse.Namespace		,	__lowerCamelCase   :     Union[str, Any]=None		,	__lowerCamelCase   :     List[Any]=True		,	__lowerCamelCase   :     List[Any]=[]		,	__lowerCamelCase   :     Any=None		,	__lowerCamelCase   :     Optional[int]=None		,	**__lowerCamelCase   :     Any		,	):



 """simple docstring"""





 pl.seed_everything(args.seed     )

 # init model
 __SCREAMING_SNAKE_CASE       :    List[str]       =	Path(model.hparams.output_dir     )
 odir.mkdir(exist_ok=__lowerCamelCase     )

 # add custom checkpoints
 if checkpoint_callback is None:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	pl.callbacks.ModelCheckpoint(
      filepath=args.output_dir		,	prefix="checkpoint"		,	monitor="val_loss"		,	mode="min"		,	save_top_k=1     )
 if early_stopping_callback:
  extra_callbacks.append(__lowerCamelCase     )
 if logging_callback is None:
  __SCREAMING_SNAKE_CASE       :    List[str]       =	LoggingCallback()

 __SCREAMING_SNAKE_CASE       :    str       =	{}

 if args.fpaa:
  __SCREAMING_SNAKE_CASE       :    Any       =	16

 if args.gpus > 1:
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	"auto"
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	"ddp"

 __SCREAMING_SNAKE_CASE       :    List[str]       =	args.accumulate_grad_batches
 __SCREAMING_SNAKE_CASE       :    List[str]       =	None
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	"auto"

 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	pl.Trainer.from_argparse_args(
     __lowerCamelCase		,	weights_summary=__lowerCamelCase		,	callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]		,	logger=__lowerCamelCase		,	val_check_interval=1		,	num_sanity_val_steps=2		,	**__lowerCamelCase		,	)

 if args.do_train:
  trainer.fit(__lowerCamelCase     )

 else:
  print("RAG modeling tests with new set functions successfuly executed!"     )
 return trainer






