

"""simple docstring"""

import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


_UpperCamelCase		     =    logging.getLogger(__name__)

_UpperCamelCase		     =    """pytorch_model.bin"""






@dataclasses.dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      str    =						dataclasses.field(
           metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'}       )
       __UpperCamelCase					:      Optional[str]    =						dataclasses.field(
           default=__magic_name__       ,	metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'}       ,	)






@dataclasses.dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      str    =						dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'}       )
       __UpperCamelCase					:      str    =						dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'}       )
       __UpperCamelCase					:      Optional[str]    =						dataclasses.field(
           default=__magic_name__       ,	metadata={'help': 'A csv or a json file containing the validation data.'}       )
       __UpperCamelCase					:      Optional[str]    =						dataclasses.field(
           default=__magic_name__       ,	metadata={'help': 'The name of the task to train on.'}       ,	)
       __UpperCamelCase					:      Optional[List[str]]    =						dataclasses.field(
           default=__magic_name__       ,	metadata={'help': 'The list of labels for the task.'}       )






@dataclasses.dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      str    =						dataclasses.field(
           metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'}       )
       __UpperCamelCase					:      Optional[str]    =						dataclasses.field(
           default='accuracy'       ,	metadata={'help': 'The evaluation metric used for the task.'}       )
       __UpperCamelCase					:      Optional[str]    =						dataclasses.field(
           default='no'       ,	metadata={
               'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
           }       ,	)
       __UpperCamelCase					:      Optional[int]    =						dataclasses.field(
           default=10       ,	metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}       ,	)
       __UpperCamelCase					:      Optional[float]    =						dataclasses.field(
           default=0.0       ,	metadata={
               'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
           }       ,	)
       __UpperCamelCase					:      Optional[bool]    =						dataclasses.field(
           default=__magic_name__       ,	metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}       ,	)
       __UpperCamelCase					:      Optional[bool]    =						dataclasses.field(
           default=__magic_name__       ,	metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}       ,	)
       __UpperCamelCase					:      Optional[bool]    =						dataclasses.field(
           default=__magic_name__       ,	metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'}       ,	)
       __UpperCamelCase					:      Optional[float]    =						dataclasses.field(
           default=0.0       ,	metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'}       ,	)
       __UpperCamelCase					:      Optional[int]    =						dataclasses.field(
           default=100       ,	metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}       ,	)
       __UpperCamelCase					:      Optional[int]    =						dataclasses.field(
           default=__magic_name__       ,	metadata={'help': 'Random seed for initialization.'}       ,	)




def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__   )			->						str:
 lowerCAmelCase__   :				Union[str, Any] 			=				datasets.concatenate_datasets([infer_input, infer_output]					,					axis=1   )

 if args.do_filter_by_confidence:
  lowerCAmelCase__   :				Union[str, Any] 			=				dataset.filter(lambda lowercase__   : example["probability"] > args.confidence_threshold   )

 if args.do_filter_by_val_performance:
  assert eval_result >= 0.0 and eval_result <= 1.0
  lowerCAmelCase__   :				Union[str, Any] 			=				int(eval_result * len(lowercase__   )   )
  print(lowercase__   )
  lowerCAmelCase__   :				Union[str, Any] 			=				dataset.sort("probability"					,					reverse=lowercase__   )
  lowerCAmelCase__   :				List[str] 			=				dataset.select(range(lowercase__   )   )

 lowerCAmelCase__   :				Any 			=				dataset.remove_columns(["label", "probability"]   )
 lowerCAmelCase__   :				Any 			=				dataset.rename_column("prediction"					,					"label"   )
 lowerCAmelCase__   :				Optional[Any] 			=				dataset.map(lambda lowercase__   : {"label": idalabel[example["label"]]}   )
 lowerCAmelCase__   :				Optional[int] 			=				dataset.shuffle(seed=args.seed   )

 lowerCAmelCase__   :				Tuple 			=				os.path.join(lowercase__					,					F"""train_pseudo.{args.data_file_extension}"""   )
 if args.data_file_extension == "csv":
  dataset.to_csv(lowercase__					,					index=lowercase__   )
 else:
  dataset.to_json(lowercase__   )






def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					**lowercase__   )			->						Optional[int]:
 lowerCAmelCase__   :				Optional[Any] 			=				Accelerator()
 # Make one log on every process with the configuration for debugging.
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,					datefmt="%m/%d/%Y %H:%M:%S"					,					level=logging.INFO					,					)
 logger.info(accelerator.state   )

 # Setup logging, we only want one process per machine to log things on the
 # screen. accelerator.is_local_main_process is only True for one process per
 # machine.
 logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR   )

 if accelerator.is_local_main_process:
  datasets.utils.logging.set_verbosity_warning()
  transformers.utils.logging.set_verbosity_info()
 else:
  datasets.utils.logging.set_verbosity_error()
  transformers.utils.logging.set_verbosity_error()

 lowerCAmelCase__   :				Dict 			=				STModelArguments(model_name_or_path=lowercase__   )
 lowerCAmelCase__   :				Tuple 			=				STDataArguments(train_file=lowercase__					,					infer_file=lowercase__   )
 lowerCAmelCase__   :				str 			=				STTrainingArguments(output_dir=lowercase__   )
 lowerCAmelCase__   :				str 			=				argparse.Namespace()

 for arg_class in (model_args, data_args, training_args):
  for key, value in vars(lowercase__   ).items():
   setattr(lowercase__					,					lowercase__					,					lowercase__   )

 for key, value in kwargs.items():
  if hasattr(lowercase__					,					lowercase__   ):
   setattr(lowercase__					,					lowercase__					,					lowercase__   )

    # Sanity checks
 lowerCAmelCase__   :				Optional[int] 			=				{}
 lowerCAmelCase__   :				Optional[int] 			=				None

 # You need to provide the training data and the data to predict on
 assert args.train_file is not None
 assert args.infer_file is not None
 lowerCAmelCase__   :				Tuple 			=				args.train_file
 lowerCAmelCase__   :				Optional[int] 			=				args.infer_file

 if args.evaluation_strategy != IntervalStrategy.NO.value:
  assert args.eval_file is not None
  lowerCAmelCase__   :				Optional[Any] 			=				args.eval_file

 for key in data_files:
  lowerCAmelCase__   :				Dict 			=				data_files[key].split("."   )[-1]
  assert extension in ["csv", "json"], F"""`{key}_file` should be a csv or a json file."""
  if args.data_file_extension is None:
   lowerCAmelCase__   :				Union[str, Any] 			=				extension
  else:
   assert extension == args.data_file_extension, F"""`{key}_file` should be a {args.data_file_extension} file`."""

 assert (
     args.eval_metric in datasets.list_metrics()
 ), F"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""

 # If passed along, set the training seed now.
 if args.seed is not None:
  set_seed(args.seed   )

 logger.info("Creating the initial data directory for self-training..."   )
 lowerCAmelCase__   :				Optional[int] 			=				F"""{args.output_dir}/self-train_iter-{{}}""".format
 lowerCAmelCase__   :				Optional[Any] 			=				data_dir_format(0   )

 if accelerator.is_main_process:
  if args.output_dir is not None:
   os.makedirs(args.output_dir					,					exist_ok=lowercase__   )
   os.makedirs(lowercase__					,					exist_ok=lowercase__   )
 accelerator.wait_for_everyone()

 lowerCAmelCase__   :				Any 			=				None
 lowerCAmelCase__   :				str 			=				None
 lowerCAmelCase__   :				Dict 			=				0
 lowerCAmelCase__   :				List[str] 			=				False
 # Show the progress bar
 lowerCAmelCase__   :				Tuple 			=				tqdm(range(args.max_selftrain_iterations   )					,					disable=not accelerator.is_local_main_process   )

 # Self-train
 for iteration in range(0					,					int(args.max_selftrain_iterations   )   ):
  lowerCAmelCase__   :				Tuple 			=				data_dir_format(lowercase__   )
  assert os.path.exists(lowercase__   )

  # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
  # iteration > 0
  lowerCAmelCase__   :				int 			=				os.path.join(lowercase__					,					"stage-1"   )
  lowerCAmelCase__   :				Optional[Any] 			=				{
      "accelerator": accelerator,
      "model_name_or_path": args.model_name_or_path,
      "cache_dir": args.cache_dir,
      "do_train": True,
      "train_file": data_files["train"] if iteration == 0 else data_files["train_pseudo"],
      "do_eval": True if args.eval_file is not None else False,
      "eval_file": data_files["eval"],
      "do_predict": True,
      "infer_file": data_files["infer"],
      "task_name": args.task_name,
      "label_list": args.label_list,
      "output_dir": current_output_dir,
      "eval_metric": args.eval_metric,
      "evaluation_strategy": args.evaluation_strategy,
      "early_stopping_patience": args.early_stopping_patience,
      "early_stopping_threshold": args.early_stopping_threshold,
      "seed": args.seed,
  }
  # Add additional training arguments
  for key, value in kwargs.items():
   if key not in arguments_dict and not hasattr(lowercase__					,					lowercase__   ):
    arguments_dict.update({key: value}   )

  lowerCAmelCase__   :				int 			=				os.path.join(lowercase__					,					"best-checkpoint"					,					lowercase__   )
  if os.path.exists(lowercase__   ):
   logger.info(
       "Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1."					,					lowercase__					,					lowercase__					,					)
  else:
   logger.info("***** Running self-training: iteration: %d, stage: 1 *****"					,					lowercase__   )
   finetune(**lowercase__   )
   accelerator.wait_for_everyone()
   assert os.path.exists(lowercase__   )
   logger.info("Self-training job completed: iteration: %d, stage: 1."					,					lowercase__   )

  if iteration > 0 and args.finetune_on_labeled_data:
   # Stage 2 (optional): fine-tuning on the original labeled data
   lowerCAmelCase__   :				Union[str, Any] 			=				os.path.join(lowercase__					,					"best-checkpoint"   )
   lowerCAmelCase__   :				int 			=				os.path.join(lowercase__					,					"stage-2"   )
   # Update arguments_dict
   lowerCAmelCase__   :				Tuple 			=				model_path
   lowerCAmelCase__   :				List[str] 			=				data_files["train"]
   lowerCAmelCase__   :				str 			=				current_output_dir

   lowerCAmelCase__   :				List[Any] 			=				os.path.join(lowercase__					,					"best-checkpoint"					,					lowercase__   )
   if os.path.exists(lowercase__   ):
    logger.info(
        "Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2."					,					lowercase__					,					lowercase__					,					)
   else:
    logger.info("***** Running self-training: iteration: %d, stage: 2 *****"					,					lowercase__   )
    finetune(**lowercase__   )
    accelerator.wait_for_everyone()
    assert os.path.exists(lowercase__   )
    logger.info("Self-training job completed: iteration: %d, stage: 2."					,					lowercase__   )

  lowerCAmelCase__   :				Optional[Any] 			=				iteration
  lowerCAmelCase__   :				Optional[int] 			=				data_dir_format(iteration + 1   )

  lowerCAmelCase__   :				int 			=				AutoConfig.from_pretrained(os.path.join(lowercase__					,					"best-checkpoint"   )   )
  lowerCAmelCase__   :				List[str] 			=				config.idalabel
  lowerCAmelCase__   :				str 			=				os.path.join(lowercase__					,					"eval_results_best-checkpoint.json"   )
  lowerCAmelCase__   :				int 			=				os.path.join(lowercase__					,					"test_results_best-checkpoint.json"   )
  assert os.path.exists(lowercase__   )

  with open(lowercase__					,					"r"   ) as f:
   lowerCAmelCase__   :				str 			=				float(json.load(lowercase__   )[args.eval_metric]   )
  lowerCAmelCase__   :				List[Any] 			=				os.path.join(lowercase__					,					"infer_output_best-checkpoint.csv"   )
  assert os.path.exists(lowercase__   )
  # Loading the dataset from local csv or json files.
  lowerCAmelCase__   :				Dict 			=				load_dataset(args.data_file_extension					,					data_files={"data": data_files["infer"]}   )["data"]
  lowerCAmelCase__   :				Union[str, Any] 			=				load_dataset("csv"					,					data_files={"data": infer_output_file}   )["data"]

  if accelerator.is_main_process:
   os.makedirs(lowercase__					,					exist_ok=lowercase__   )
   shutil.copy(lowercase__					,					os.path.join(lowercase__					,					F"""eval_results_iter-{iteration}.json"""   )   )
   if os.path.exists(lowercase__   ):
    shutil.copy(lowercase__					,					os.path.join(lowercase__					,					F"""test_results_iter-{iteration}.json"""   )   )
   create_pseudo_labeled_data(lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__   )
  accelerator.wait_for_everyone()

  lowerCAmelCase__   :				Dict 			=				os.path.join(lowercase__					,					F"""train_pseudo.{args.data_file_extension}"""   )

  if args.evaluation_strategy != IntervalStrategy.NO.value:
   lowerCAmelCase__   :				Any 			=				eval_result

   if best_iteration is None:
    lowerCAmelCase__   :				List[str] 			=				new_iteration
    lowerCAmelCase__   :				Tuple 			=				new_eval_result
   else:
    if new_eval_result - best_eval_result > args.early_stopping_threshold:
     lowerCAmelCase__   :				Dict 			=				new_iteration
     lowerCAmelCase__   :				List[str] 			=				new_eval_result
     lowerCAmelCase__   :				Union[str, Any] 			=				0
    else:
     if new_eval_result == best_eval_result:
      lowerCAmelCase__   :				List[Any] 			=				new_iteration
      lowerCAmelCase__   :				Optional[Any] 			=				new_eval_result
     early_stopping_patience_counter += 1

    if early_stopping_patience_counter >= args.early_stopping_patience:
     lowerCAmelCase__   :				Tuple 			=				True

  progress_bar.update(1   )

  if should_training_stop:
   break

 if best_iteration is not None:
  # Save the best iteration
  logger.info("Best iteration: %d"					,					lowercase__   )
  logger.info("Best evaluation result: %s = %f"					,					args.eval_metric					,					lowercase__   )
  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
   shutil.copy(
       os.path.join(lowercase__					,					F"""eval_results_iter-{iteration}.json"""   )					,					os.path.join(lowercase__					,					"eval_results_best-iteration.json"   )					,					)
 else:
  # Assume that the last iteration is the best
  logger.info("Best iteration: %d"					,					args.max_selftrain_iterations - 1   )
  logger.info("Best evaluation result: %s = %f"					,					args.eval_metric					,					lowercase__   )
  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
   shutil.copy(
       os.path.join(lowercase__					,					F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""   )					,					os.path.join(lowercase__					,					"eval_results_best-iteration.json"   )					,					)









"""simple docstring"""

from __future__ import annotations

_UpperCamelCase		     =    []



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						bool:
 for i in range(len(lowercase__   )   ):
  if board[row][i] == 1:
   return False
 for i in range(len(lowercase__   )   ):
  if board[i][column] == 1:
   return False
 for i, j in zip(range(lowercase__					,					-1					,					-1   )					,					range(lowercase__					,					-1					,					-1   )   ):
  if board[i][j] == 1:
   return False
 for i, j in zip(range(lowercase__					,					-1					,					-1   )					,					range(lowercase__					,					len(lowercase__   )   )   ):
  if board[i][j] == 1:
   return False
 return True



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						bool:
 if row >= len(lowercase__   ):
  solution.append(lowercase__   )
  printboard(lowercase__   )
  print()
  return True
 for i in range(len(lowercase__   )   ):
  if is_safe(lowercase__					,					lowercase__					,					lowercase__   ):
   lowerCAmelCase__   :				List[Any] 			=				1
   solve(lowercase__					,					row + 1   )
   lowerCAmelCase__   :				Dict 			=				0
 return False



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						None:
 for i in range(len(lowercase__   )   ):
  for j in range(len(lowercase__   )   ):
   if board[i][j] == 1:
    print("Q"					,					end=" "   )
   else:
    print("."					,					end=" "   )
  print()


# n=int(input("The no. of queens"))
_UpperCamelCase		     =    8
_UpperCamelCase		     =    [[0 for i in range(n)] for j in range(n)]
solve(board, 0)






print("""The total no. of solutions are :""", len(solution))






