

'''simple docstring'''

import numpy as np





def 				lowercase (					__magic_name__ :    Optional[int]			,   __magic_name__ :    str="utf-8"			,   __magic_name__ :    Union[str, Any]="surrogatepass"							):



					'''simple docstring'''







					UpperCAmelCase		:							Union[str, Any]        =       bin(int.from_bytes(text.encode(_A			,   _A							)			,   "big"							)							)[2:]
					return bits.zfill(8 * ((len(_A							) + 7) // 8)							)





def 				lowercase (					__magic_name__ :    List[str]			,   __magic_name__ :    List[Any]="utf-8"			,   __magic_name__ :    Tuple="surrogatepass"							):



					'''simple docstring'''







					UpperCAmelCase		:							List[str]        =       int(_A			,   2							)
					return n.to_bytes((n.bit_length() + 7) // 8			,   "big"							).decode(_A			,   _A							) or "\0"





def 				lowercase (					__magic_name__ :    Tuple			,   __magic_name__ :    Union[str, Any]							):



					'''simple docstring'''







					if size_par + len(_A							) <= 2**size_par - (len(_A							) - 1):
										raise ValueError("size of parity don't match with size of data"							)

					UpperCAmelCase		:							List[str]        =       []
					UpperCAmelCase		:							List[str]        =       []
					UpperCAmelCase		:							Optional[Any]        =       [bin(_A							)[2:] for x in range(1			,   size_par + len(_A							) + 1							)]

					# sorted information data for the size of the output data
					UpperCAmelCase		:							Union[str, Any]        =       []
					# data position template + parity
					UpperCAmelCase		:							Tuple        =       []
					# parity bit counter
					UpperCAmelCase		:							Dict        =       0
					# counter position of data bits
					UpperCAmelCase		:							List[str]        =       0

					for x in range(1			,   size_par + len(_A							) + 1							):
										# Performs a template of bit positions - who should be given,
										# and who should be parity
										if qtd_bp < size_par:
															if (np.log(_A							) / np.log(2							)).is_integer():
																				data_out_gab.append("P"							)
																				UpperCAmelCase		:							Union[str, Any]        =       qtd_bp + 1
															else:
																				data_out_gab.append("D"							)
										else:
															data_out_gab.append("D"							)

										# Sorts the data to the new output size
										if data_out_gab[-1] == "D":
															data_ord.append(data[cont_data]							)
															cont_data += 1
										else:
															data_ord.append(_A							)

    # Calculates parity
					UpperCAmelCase		:							Any        =       0  # parity bit counter
					for bp in range(1			,   size_par + 1							):
										# Bit counter one for a given parity
										UpperCAmelCase		:							Tuple        =       0
										# counter to control the loop reading
										UpperCAmelCase		:							str        =       0
										for x in data_ord:
															if x is not None:
																				try:
																									UpperCAmelCase		:							Optional[int]        =       (bin_pos[cont_loop])[-1 * (bp)]
																				except IndexError:
																									UpperCAmelCase		:							Optional[Any]        =       "0"
																				if aux == "1" and x == "1":
																									cont_bo += 1
															cont_loop += 1
										parity.append(cont_bo % 2							)

										qtd_bp += 1

					# Mount the message
					UpperCAmelCase		:							Optional[int]        =       0  # parity bit counter
					for x in range(0			,   size_par + len(_A							)							):
										if data_ord[x] is None:
															data_out.append(str(parity[cont_bp]							)							)
															cont_bp += 1
										else:
															data_out.append(data_ord[x]							)

					return data_out





def 				lowercase (					__magic_name__ :    Union[str, Any]			,   __magic_name__ :    Optional[Any]							):



					'''simple docstring'''







					UpperCAmelCase		:							int        =       []
					# Parity bit counter
					UpperCAmelCase		:							str        =       0
					# Counter p data bit reading
					UpperCAmelCase		:							Dict        =       0
					# list of parity received
					UpperCAmelCase		:							int        =       []
					UpperCAmelCase		:							List[Any]        =       []

					for x in range(1			,   len(_A							) + 1							):
										# Performs a template of bit positions - who should be given,
										#  and who should be parity
										if qtd_bp < size_par and (np.log(_A							) / np.log(2							)).is_integer():
															data_out_gab.append("P"							)
															UpperCAmelCase		:							Optional[Any]        =       qtd_bp + 1
										else:
															data_out_gab.append("D"							)

										# Sorts the data to the new output size
										if data_out_gab[-1] == "D":
															data_output.append(data[cont_data]							)
										else:
															parity_received.append(data[cont_data]							)
										cont_data += 1

					# -----------calculates the parity with the data
					UpperCAmelCase		:							Optional[Any]        =       []
					UpperCAmelCase		:							Any        =       []
					UpperCAmelCase		:							Any        =       [bin(_A							)[2:] for x in range(1			,   size_par + len(_A							) + 1							)]

					#  sorted information data for the size of the output data
					UpperCAmelCase		:							Dict        =       []
					# Data position feedback + parity
					UpperCAmelCase		:							List[str]        =       []
					# Parity bit counter
					UpperCAmelCase		:							Dict        =       0
					# Counter p data bit reading
					UpperCAmelCase		:							List[Any]        =       0

					for x in range(1			,   size_par + len(_A							) + 1							):
										# Performs a template position of bits - who should be given,
										# and who should be parity
										if qtd_bp < size_par and (np.log(_A							) / np.log(2							)).is_integer():
															data_out_gab.append("P"							)
															UpperCAmelCase		:							Optional[int]        =       qtd_bp + 1
										else:
															data_out_gab.append("D"							)

										# Sorts the data to the new output size
										if data_out_gab[-1] == "D":
															data_ord.append(data_output[cont_data]							)
															cont_data += 1
										else:
															data_ord.append(_A							)

    # Calculates parity
					UpperCAmelCase		:							Any        =       0  # parity bit counter
					for bp in range(1			,   size_par + 1							):
										# Bit counter one for a certain parity
										UpperCAmelCase		:							str        =       0
										# Counter to control loop reading
										UpperCAmelCase		:							Union[str, Any]        =       0
										for x in data_ord:
															if x is not None:
																				try:
																									UpperCAmelCase		:							List[Any]        =       (bin_pos[cont_loop])[-1 * (bp)]
																				except IndexError:
																									UpperCAmelCase		:							Optional[Any]        =       "0"
																				if aux == "1" and x == "1":
																									cont_bo += 1
															cont_loop += 1
										parity.append(str(cont_bo % 2							)							)

										qtd_bp += 1

					# Mount the message
					UpperCAmelCase		:							Optional[int]        =       0  # Parity bit counter
					for x in range(0			,   size_par + len(_A							)							):
										if data_ord[x] is None:
															data_out.append(str(parity[cont_bp]							)							)
															cont_bp += 1
										else:
															data_out.append(data_ord[x]							)

					UpperCAmelCase		:							Any        =       parity_received == parity
					return data_output, ack



'''simple docstring'''

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 import save_json





def 				lowercase (					__magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Any        =       filter(lambda __magic_name__							: p.requires_grad			,   model.parameters()							)
					UpperCAmelCase		:							Union[str, Any]        =       sum([np.prod(p.size()							) for p in model_parameters]							)
					return params


a       :      int  	=			logging.getLogger(__name__)







class   UpperCamelCase__   (       pl.Callback       ):


		"""simple docstring"""

		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Union[str, Any]        =       {f"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups      )}
							pl_module.logger.log_metrics(snake_case      )







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case   ,      snake_case=True      ):


							'''simple docstring'''

							logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"      )
							UpperCAmelCase		:							Optional[Any]        =       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		:							List[str]        =       Path(pl_module.hparams.output_dir      )
							if type_path == "test":
												UpperCAmelCase		:							Dict        =       od / "test_results.txt"
												UpperCAmelCase		:							Dict        =       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		:							Union[str, Any]        =       od / f"{type_path}_results/{trainer.global_step:05d}.txt"
												UpperCAmelCase		:							int        =       od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
												results_file.parent.mkdir(exist_ok=snake_case      )
												generations_file.parent.mkdir(exist_ok=snake_case      )
							with open(snake_case   ,      "a+"      ) as writer:
												for key in sorted(snake_case      ):
																	if key in ["log", "progress_bar", "preds"]:
																						continue
																	UpperCAmelCase		:							Optional[Any]        =       metrics[key]
																	if isinstance(snake_case   ,      torch.Tensor      ):
																						UpperCAmelCase		:							Union[str, Any]        =       val.item()
																	UpperCAmelCase		:							List[str]        =       f"{key}: {val:.6f}\n"
																	writer.write(snake_case      )

							if not save_generations:
												return

							if "preds" in metrics:
												UpperCAmelCase		:							List[str]        =       "\n".join(metrics["preds"]      )
												generations_file.open("w+"      ).write(snake_case      )







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							try:
												UpperCAmelCase		:							Tuple        =       pl_module.model.model.num_parameters()
							except AttributeError:
												UpperCAmelCase		:							int        =       pl_module.model.num_parameters()

							UpperCAmelCase		:							List[Any]        =       count_trainable_parameters(snake_case      )
							# 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 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							save_json(pl_module.metrics   ,      pl_module.metrics_save_path      )
							return self._write_logs(snake_case   ,      snake_case   ,      "test"      )







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''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")





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__=1			,   __magic_name__=False							):



					'''simple docstring'''







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

					UpperCAmelCase		:							int        =       ModelCheckpoint(
					    dirpath=__magic_name__			,   filename=__magic_name__			,   monitor=F"val_{metric}"			,   mode="min" if "loss" in metric else "max"			,   save_top_k=__magic_name__			,   )
					return checkpoint_callback





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







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