





"""simple docstring"""




import numpy as np
import qiskit





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   int      =       qiskit.QuantumCircuit(num_qubits + 1  )

						# First, let's deal with the case in which oracle is balanced
						if case == "balanced":
												# First generate a random number that tells us which CNOTs to
												# wrap in X-gates:
												_UpperCamelCase     :   List[str]      =       np.random.randint(1			,		2**num_qubits  )
												# Next, format 'b' as a binary string of length 'n', padded with zeros:
												_UpperCamelCase     :   int      =       format(_lowercase			,		F"""0{num_qubits}b"""  )
												# Next, we place the first X-gates. Each digit in our binary string
												# corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
												# we apply an X-gate to that qubit:
												for index, bit in enumerate(_lowercase  ):
																		if bit == "1":
																								oracle_qc.x(_lowercase  )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
												for index in range(_lowercase  ):
																		oracle_qc.cx(_lowercase			,		_lowercase  )
												# Next, place the final X-gates
												for index, bit in enumerate(_lowercase  ):
																		if bit == "1":
																								oracle_qc.x(_lowercase  )

    # Case in which oracle is constant
						if case == "constant":
												# First decide what the fixed output of the oracle will be
												# (either always 0 or always 1)
												_UpperCamelCase     :   str      =       np.random.randint(2  )
												if output == 1:
																		oracle_qc.x(_lowercase  )

						_UpperCamelCase     :   Union[str, Any]      =       oracle_qc.to_gate()
						_UpperCamelCase     :   Dict      =       '''Oracle'''  # To show when we display the circuit
						return oracle_gate





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Union[str, Any]      =       qiskit.QuantumCircuit(num_qubits + 1			,		_lowercase  )
						# Set up the output qubit:
						dj_circuit.x(_lowercase  )
						dj_circuit.h(_lowercase  )
						# And set up the input register:
						for qubit in range(_lowercase  ):
												dj_circuit.h(_lowercase  )
						# Let's append the oracle gate to our circuit:
						dj_circuit.append(_lowercase			,		range(num_qubits + 1  )  )
						# Finally, perform the H-gates again and measure:
						for qubit in range(_lowercase  ):
												dj_circuit.h(_lowercase  )

						for i in range(_lowercase  ):
												dj_circuit.measure(_lowercase			,		_lowercase  )

						return dj_circuit





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   str      =       qiskit.Aer.get_backend('''aer_simulator'''  )

						_UpperCamelCase     :   Tuple      =       dj_oracle(_lowercase			,		_lowercase  )
						_UpperCamelCase     :   Optional[Any]      =       dj_algorithm(_lowercase			,		_lowercase  )

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

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


if __name__ == "__main__":
						print(F"Deutsch Jozsa - Constant Oracle: {deutsch_jozsa('constant', 3)}")
						print(F"Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa('balanced', 3)}")







'''simple docstring'''




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


__lowercase	:   str   		=					logging.getLogger(__name__)

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

__lowercase	:   List[str]   		=					{
    """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
__lowercase	:   List[str]   		=					{
    """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
}
__lowercase	:   Optional[Any]   		=					sorted(arg_to_scheduler.keys())
__lowercase	:   Union[str, Any]   		=					"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""





class _snake_case (			pl.LightningModule ):
							"""simple docstring"""





							def __init__( self      ,					__A      ,					__A=None      ,					__A="base"      ,					__A=None      ,					__A=None      ,					__A=None      ,					**__A      ,					)  ->Any:



											"""simple docstring"""







											super().__init__()
											# TODO: move to self.save_hyperparameters()
											# self.save_hyperparameters()
											# can also expand arguments into trainer signature for easier reading

											self.save_hyperparameters(__A						)
											__lowerCamelCase		:   str           =       0
											__lowerCamelCase		:   int           =       Path(self.hparams.output_dir						)
											__lowerCamelCase		:   Tuple           =       self.hparams.cache_dir if self.hparams.cache_dir else None
											if config is None:
															__lowerCamelCase		:   str           =       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=__A      ,					**__A      ,					)
											else:
															__lowerCamelCase		:   PretrainedConfig           =       config

											__lowerCamelCase		:   Optional[int]           =       ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
											for p in extra_model_params:
															if getattr(self.hparams      ,					__A      ,					__A						):
																			assert hasattr(self.config      ,					__A						), f'model config doesn\'t have a `{p}` attribute'
																			setattr(self.config      ,					__A      ,					getattr(self.hparams      ,					__A						)						)

											if tokenizer is None:
															__lowerCamelCase		:   Optional[int]           =       AutoTokenizer.from_pretrained(
															    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path      ,					cache_dir=__A      ,					)
											else:
															__lowerCamelCase		:   PreTrainedTokenizer           =       tokenizer
											__lowerCamelCase		:   List[str]           =       MODEL_MODES[mode]
											if model is None:
															__lowerCamelCase		:   int           =       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=__A      ,					)
											else:
															__lowerCamelCase		:   Tuple           =       model







							def 		__SCREAMING_SNAKE_CASE( self      ,					*__A      ,					**__A						)  ->Any:



											"""simple docstring"""







											__lowerCamelCase		:   str           =       self.model_type.from_pretrained(*__A      ,					**__A						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       arg_to_scheduler[self.hparams.lr_scheduler]
											__lowerCamelCase		:   Optional[Any]           =       get_schedule_func(
											    self.opt      ,					num_warmup_steps=self.hparams.warmup_steps      ,					num_training_steps=self.total_steps()						)
											__lowerCamelCase		:   List[Any]           =       {"scheduler": scheduler, "interval": "step", "frequency": 1}
											return scheduler







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Optional[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   Dict           =       self.model
											__lowerCamelCase		:   List[Any]           =       ["bias", "LayerNorm.weight"]
											__lowerCamelCase		:   int           =       [
											    {
											        "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay						)],
											        "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:
															__lowerCamelCase		:   Any           =       Adafactor(
															    __A      ,					lr=self.hparams.learning_rate      ,					scale_parameter=__A      ,					relative_step=__A						)

											else:
															__lowerCamelCase		:   str           =       AdamW(
															    __A      ,					lr=self.hparams.learning_rate      ,					eps=self.hparams.adam_epsilon						)
											__lowerCamelCase		:   Optional[int]           =       optimizer

											__lowerCamelCase		:   List[str]           =       self.get_lr_scheduler()

											return [optimizer], [scheduler]







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->Optional[int]:



											"""simple docstring"""







											return self.validation_step(__A      ,					__A						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->Optional[int]:



											"""simple docstring"""







											return self.validation_end(__A						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Union[str, Any]           =       max(1      ,					self.hparams.gpus						)  # TODO: consider num_tpu_cores
											__lowerCamelCase		:   Optional[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 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->Union[str, Any]:



											"""simple docstring"""







											if mode == "test":
															__lowerCamelCase		:   str           =       len(self.test_dataloader().dataset						)
											else:
															__lowerCamelCase		:   List[str]           =       self.get_dataloader("train"      ,					self.hparams.train_batch_size      ,					shuffle=__A						)
															__lowerCamelCase		:   List[str]           =       len(self.train_dataloader().dataset						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A = False						)  ->Any:



											"""simple docstring"""







											raise NotImplementedError("You must implement this for your task"						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Optional[Any]:



											"""simple docstring"""







											return self.train_loader







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Optional[Any]:



											"""simple docstring"""







											return self.get_dataloader("dev"      ,					self.hparams.eval_batch_size      ,					shuffle=__A						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Dict:



											"""simple docstring"""







											return self.get_dataloader("test"      ,					self.hparams.eval_batch_size      ,					shuffle=__A						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->Dict:



											"""simple docstring"""







											return os.path.join(
											    self.hparams.data_dir      ,					"cached_{}_{}_{}".format(
											        __A      ,					list(filter(__A      ,					self.hparams.model_name_or_path.split("/"						)						)						).pop()      ,					str(self.hparams.max_seq_length						)      ,					)      ,					)







							@pl.utilities.rank_zero_only
							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->None:



											"""simple docstring"""







											__lowerCamelCase		:   int           =       self.output_dir.joinpath("best_tfmr"						)
											__lowerCamelCase		:   str           =       self.step_count
											self.model.save_pretrained(__A						)
											self.tokenizer.save_pretrained(__A						)







							@staticmethod
							def 		__SCREAMING_SNAKE_CASE( __A      ,					__A						)  ->str:



											"""simple docstring"""







											parser.add_argument(
											    "--model_name_or_path"      ,					default=__A      ,					type=__A      ,					required=__A      ,					help="Path to pretrained model or model identifier from huggingface.co/models"      ,					)
											parser.add_argument(
											    "--config_name"      ,					default=""      ,					type=__A      ,					help="Pretrained config name or path if not the same as model_name"						)
											parser.add_argument(
											    "--tokenizer_name"      ,					default=__A      ,					type=__A      ,					help="Pretrained tokenizer name or path if not the same as model_name"      ,					)
											parser.add_argument(
											    "--cache_dir"      ,					default=""      ,					type=__A      ,					help="Where do you want to store the pre-trained models downloaded from huggingface.co"      ,					)
											parser.add_argument(
											    "--encoder_layerdrop"      ,					type=__A      ,					help="Encoder layer dropout probability (Optional). Goes into model.config"      ,					)
											parser.add_argument(
											    "--decoder_layerdrop"      ,					type=__A      ,					help="Decoder layer dropout probability (Optional). Goes into model.config"      ,					)
											parser.add_argument(
											    "--dropout"      ,					type=__A      ,					help="Dropout probability (Optional). Goes into model.config"      ,					)
											parser.add_argument(
											    "--attention_dropout"      ,					type=__A      ,					help="Attention dropout probability (Optional). Goes into model.config"      ,					)
											parser.add_argument("--learning_rate"      ,					default=5E-5      ,					type=__A      ,					help="The initial learning rate for Adam."						)
											parser.add_argument(
											    "--lr_scheduler"      ,					default="linear"      ,					choices=__A      ,					metavar=__A      ,					type=__A      ,					help="Learning rate scheduler"      ,					)
											parser.add_argument("--weight_decay"      ,					default=0.0      ,					type=__A      ,					help="Weight decay if we apply some."						)
											parser.add_argument("--adam_epsilon"      ,					default=1E-8      ,					type=__A      ,					help="Epsilon for Adam optimizer."						)
											parser.add_argument("--warmup_steps"      ,					default=0      ,					type=__A      ,					help="Linear warmup over warmup_steps."						)
											parser.add_argument("--num_workers"      ,					default=4      ,					type=__A      ,					help="kwarg passed to DataLoader"						)
											parser.add_argument("--num_train_epochs"      ,					dest="max_epochs"      ,					default=3      ,					type=__A						)
											parser.add_argument("--train_batch_size"      ,					default=32      ,					type=__A						)
											parser.add_argument("--eval_batch_size"      ,					default=32      ,					type=__A						)
											parser.add_argument("--adafactor"      ,					action="store_true"						)





class _snake_case (			pl.Callback ):
							"""simple docstring"""





							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->Union[str, Any]:



											"""simple docstring"""







											__lowerCamelCase		:   List[Any]           =       trainer.lr_schedulers[0]["scheduler"]
											__lowerCamelCase		:   str           =       {f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
											pl_module.logger.log_metrics(__A						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->List[Any]:



											"""simple docstring"""







											rank_zero_info("***** Validation results *****"						)
											__lowerCamelCase		:   Dict           =       trainer.callback_metrics
											# Log results
											for key in sorted(__A						):
															if key not in ["log", "progress_bar"]:
																			rank_zero_info("{} = {}\n".format(__A      ,					str(metrics[key]						)						)						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->Optional[Any]:



											"""simple docstring"""







											rank_zero_info("***** Test results *****"						)
											__lowerCamelCase		:   str           =       trainer.callback_metrics
											# Log and save results to file
											__lowerCamelCase		:   Union[str, Any]           =       os.path.join(pl_module.hparams.output_dir      ,					"test_results.txt"						)
											with open(__A      ,					"w"						) as writer:
															for key in sorted(__A						):
																			if key not in ["log", "progress_bar"]:
																							rank_zero_info("{} = {}\n".format(__A      ,					str(metrics[key]						)						)						)
																							writer.write("{} = {}\n".format(__A      ,					str(metrics[key]						)						)						)





def 				a_							(    lowercase				,    lowercase     )      ->None:






				'''simple docstring'''

				parser.add_argument(
				    "--output_dir"				,    default=lowercase				,    type=lowercase				,    required=lowercase				,    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=lowercase				,    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=lowercase     )
				parser.add_argument("--max_grad_norm"				,    dest="gradient_clip_val"				,    default=1.0				,    type=lowercase				,    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=lowercase				,    default=1				,    help="Number of updates steps to accumulate before performing a backward/update pass."				,    )
				parser.add_argument("--seed"				,    type=lowercase				,    default=42				,    help="random seed for initialization"     )
				parser.add_argument(
				    "--data_dir"				,    default=lowercase				,    type=lowercase				,    required=lowercase				,    help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."				,    )





def 				a_							(    lowercase				,    lowercase				,    lowercase=None				,    lowercase=True				,    lowercase=[]				,    lowercase=None				,    lowercase=None				,    **lowercase				,    )      ->List[Any]:






				'''simple docstring'''

				pl.seed_everything(args.seed     )

				# init model
				__lowerCamelCase		:   str           =       Path(model.hparams.output_dir     )
				odir.mkdir(exist_ok=lowercase     )

				# add custom checkpoints
				if checkpoint_callback is None:
								__lowerCamelCase		:   Optional[Any]           =       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(lowercase     )
				if logging_callback is None:
								__lowerCamelCase		:   List[Any]           =       LoggingCallback()

				__lowerCamelCase		:   int           =       {}

				# TODO: remove with PyTorch 1.6 since pl uses native amp
				if args.fpaa:
								__lowerCamelCase		:   List[Any]           =       16
								__lowerCamelCase		:   Optional[int]           =       args.fpaa_opt_level

				if args.gpus > 1:
								__lowerCamelCase		:   Tuple           =       "ddp"

				__lowerCamelCase		:   str           =       args.accumulate_grad_batches
				__lowerCamelCase		:   Any           =       extra_train_kwargs.get("accelerator"				,    lowercase     )
				__lowerCamelCase		:   Tuple           =       extra_train_kwargs.get("profiler"				,    lowercase     )

				__lowerCamelCase		:   Any           =       pl.Trainer.from_argparse_args(
				    lowercase				,    weights_summary=lowercase				,    callbacks=[logging_callback] + extra_callbacks				,    logger=lowercase				,    checkpoint_callback=lowercase				,    **lowercase				,    )

				if args.do_train:
								trainer.fit(lowercase     )

				return trainer





