




import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json


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







     """simple docstring"""


     UpperCAmelCase_          =							filter(lambda __A       : p.requires_grad   ,				model.parameters()       )
     UpperCAmelCase_          =							sum([np.prod(p.size()       ) for p in model_parameters]       )
     return params


snake_case_ : List[str]    		=       logging.getLogger(__name__)


def    A						(__A   :    Union[str, Any]   ,				__A   :    List[str]       )   ->      Union[str, Any]:







     """simple docstring"""


     if metric == "rouge2":
          UpperCAmelCase_          =							'''{val_avg_rouge2:.4f}-{step_count}'''
     elif metric == "bleu":
          UpperCAmelCase_          =							'''{val_avg_bleu:.4f}-{step_count}'''
     elif metric == "em":
          UpperCAmelCase_          =							'''{val_avg_em:.4f}-{step_count}'''
     elif metric == "loss":
          UpperCAmelCase_          =							'''{val_avg_loss:.4f}-{step_count}'''
     else:
          raise NotImplementedError(
              F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
              ''' function.'''       )

     UpperCAmelCase_          =							ModelCheckpoint(
         dirpath=__lowerCamelCase   ,				filename=__lowerCamelCase   ,				monitor=F"""val_{metric}"""   ,				mode='''max'''   ,				save_top_k=1   ,				every_n_epochs=1   ,				)

     return checkpoint_callback


def    A						(__A   :    List[str]   ,				__A   :    Optional[Any]       )   ->      List[Any]:







     """simple docstring"""


     return EarlyStopping(
         monitor=F"""val_{metric}"""   ,				mode='''min''' if '''loss''' in metric else '''max'''   ,				patience=__lowerCamelCase   ,				verbose=__lowerCamelCase   ,				)







class __snake_case			(			pl.Callback		):







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							{F"""lr_group_{i}""": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
        pl_module.logger.log_metrics(_lowerCamelCase)







   @rank_zero_only
   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						pl.Trainer					,						_snake_case		:						pl.LightningModule					,						_snake_case		:						str					,						_snake_case		:						str=True):

        """simple docstring"""


        logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****""")
        UpperCAmelCase_          =							trainer.callback_metrics
        trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']})
        # Log results
        UpperCAmelCase_          =							Path(pl_module.hparams.output_dir)
        if type_path == "test":
             UpperCAmelCase_          =							od / '''test_results.txt'''
             UpperCAmelCase_          =							od / '''test_generations.txt'''
        else:
             # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
             # If people want this it will be easy enough to add back.
             UpperCAmelCase_          =							od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
             UpperCAmelCase_          =							od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
             results_file.parent.mkdir(exist_ok=_lowerCamelCase)
             generations_file.parent.mkdir(exist_ok=_lowerCamelCase)
        with open(_lowerCamelCase					,						'''a+''') as writer:
             for key in sorted(_lowerCamelCase):
                  if key in ["log", "progress_bar", "preds"]:
                       continue
                  UpperCAmelCase_          =							metrics[key]
                  if isinstance(_lowerCamelCase					,						torch.Tensor):
                       UpperCAmelCase_          =							val.item()
                  UpperCAmelCase_          =							F"""{key}: {val:.6f}\n"""
                  writer.write(_lowerCamelCase)

        if not save_generations:
             return

        if "preds" in metrics:
             UpperCAmelCase_          =							'''\n'''.join(metrics['''preds'''])
             generations_file.open('''w+''').write(_lowerCamelCase)







   @rank_zero_only
   def lowerCamelCase						(      self		:						str					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						Dict):

        """simple docstring"""


        try:
             UpperCAmelCase_          =							pl_module.model.model.num_parameters()
        except AttributeError:
             UpperCAmelCase_          =							pl_module.model.num_parameters()

        UpperCAmelCase_          =							count_trainable_parameters(_lowerCamelCase)
        # mp stands for million parameters
        trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1e6, '''grad_mp''': n_trainable_pars / 1e6})







   @rank_zero_only
   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						pl.Trainer					,						_snake_case		:						pl.LightningModule):

        """simple docstring"""


        save_json(pl_module.metrics					,						pl_module.metrics_save_path)
        return self._write_logs(_lowerCamelCase					,						_lowerCamelCase					,						'''test''')







   @rank_zero_only
   def lowerCamelCase						(      self		:						int					,						_snake_case		:						pl.Trainer					,						_snake_case		:						Any):

        """simple docstring"""


        save_json(pl_module.metrics					,						pl_module.metrics_save_path)
        # Uncommenting this will save val generations
        # return self._write_logs(trainer, pl_module, "valid")








"""simple docstring"""



import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int = 3		)     ->					qiskit.result.counts.Counts:
  if isinstance(__lowerCamelCase ,					__lowerCamelCase		):
    raise TypeError('''number of qubits must be a integer.'''		)
  if number_of_qubits <= 0:
    raise ValueError('''number of qubits must be > 0.'''		)
  if math.floor(__lowerCamelCase		) != number_of_qubits:
    raise ValueError('''number of qubits must be exact integer.'''		)
  if number_of_qubits > 10:
    raise ValueError('''number of qubits too large to simulate(>10).'''		)

  _snake_case					     =       QuantumRegister(__lowerCamelCase ,					'''qr'''		)
  _snake_case					     =       ClassicalRegister(__lowerCamelCase ,					'''cr'''		)

  _snake_case					     =       QuantumCircuit(__lowerCamelCase ,					__lowerCamelCase		)

  _snake_case					     =       number_of_qubits

  for i in range(__lowerCamelCase		):
    quantum_circuit.h(number_of_qubits - i - 1		)
    counter -= 1
    for j in range(__lowerCamelCase		):
      quantum_circuit.cp(np.pi / 2 ** (counter - j) ,					__lowerCamelCase ,					__lowerCamelCase		)

  for k in range(number_of_qubits // 2		):
    quantum_circuit.swap(__lowerCamelCase ,					number_of_qubits - k - 1		)

  # measure all the qubits
  quantum_circuit.measure(__lowerCamelCase ,					__lowerCamelCase		)
  # simulate with 10000 shots
  _snake_case					     =       Aer.get_backend('''qasm_simulator'''		)
  _snake_case					     =       execute(__lowerCamelCase ,					__lowerCamelCase ,					shots=1_00_00		)

  return job.result().get_counts(__lowerCamelCase		)


if __name__ == "__main__":
 print(
     F"Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"
 )



