





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


SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						logging.getLogger(__name__)

SCREAMING_SNAKE_CASE__       :			int			      =						"""pytorch_model.bin"""







@dataclasses.dataclass
class   __lowerCAmelCase			:
   _UpperCamelCase :						List[Any]     		=				dataclasses.field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""}  )
   _UpperCamelCase :						Optional[int]     		=				dataclasses.field(
       default=_UpperCamelCase		,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""}		,)







@dataclasses.dataclass
class   __lowerCAmelCase			:
   _UpperCamelCase :						Any     		=				dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""}  )
   _UpperCamelCase :						Dict     		=				dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""}  )
   _UpperCamelCase :						Optional[Any]     		=				dataclasses.field(
       default=_UpperCamelCase		,metadata={"""help""": """A csv or a json file containing the validation data."""}  )
   _UpperCamelCase :						List[str]     		=				dataclasses.field(
       default=_UpperCamelCase		,metadata={"""help""": """The name of the task to train on."""}		,)
   _UpperCamelCase :						Tuple     		=				dataclasses.field(
       default=_UpperCamelCase		,metadata={"""help""": """The list of labels for the task."""}  )







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







def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
   a__	:						Any      		=  datasets.concatenate_datasets([infer_input, infer_output]							, axis=1   )

   if args.do_filter_by_confidence:
      a__	:						Any      		=  dataset.filter(lambda lowerCamelCase   : example["probability"] > args.confidence_threshold   )

   if args.do_filter_by_val_performance:
      assert eval_result >= 0.0 and eval_result <= 1.0
      a__	:						Tuple      		=  int(eval_result * len(a__   )   )
      print(a__   )
      a__	:						Any      		=  dataset.sort("probability"							, reverse=a__   )
      a__	:						int      		=  dataset.select(range(a__   )   )

   a__	:						str      		=  dataset.remove_columns(["label", "probability"]   )
   a__	:						Tuple      		=  dataset.rename_column("prediction"							, "label"   )
   a__	:						List[Any]      		=  dataset.map(lambda lowerCamelCase   : {"label": idalabel[example["label"]]}   )
   a__	:						Any      		=  dataset.shuffle(seed=args.seed   )

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






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, **lowerCamelCase   ):
   a__	:						Tuple      		=  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()

   a__	:						List[str]      		=  STModelArguments(model_name_or_path=a__   )
   a__	:						List[str]      		=  STDataArguments(train_file=a__							, infer_file=a__   )
   a__	:						Optional[int]      		=  STTrainingArguments(output_dir=a__   )
   a__	:						Optional[Any]      		=  argparse.Namespace()

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

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

      # Sanity checks
   a__	:						str      		=  {}
   a__	:						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
   a__	:						Optional[int]      		=  args.train_file
   a__	:						Any      		=  args.infer_file

   if args.evaluation_strategy != IntervalStrategy.NO.value:
      assert args.eval_file is not None
      a__	:						List[str]      		=  args.eval_file

   for key in data_files:
      a__	:						Tuple      		=  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:
         a__	:						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..."   )
   a__	:						str      		=  F"""{args.output_dir}/self-train_iter-{{}}""".format
   a__	:						List[str]      		=  data_dir_format(0   )

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

   a__	:						Union[str, Any]      		=  None
   a__	:						Union[str, Any]      		=  None
   a__	:						Optional[int]      		=  0
   a__	:						str      		=  False
   # Show the progress bar
   a__	:						List[str]      		=  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   )   ):
      a__	:						Optional[Any]      		=  data_dir_format(a__   )
      assert os.path.exists(a__   )

      # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
      # iteration > 0
      a__	:						Union[str, Any]      		=  os.path.join(a__							, "stage-1"   )
      a__	:						int      		=  {
          "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(a__							, a__   ):
            arguments_dict.update({key: value}   )

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

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

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

      a__	:						Union[str, Any]      		=  iteration
      a__	:						List[Any]      		=  data_dir_format(iteration + 1   )

      a__	:						Optional[int]      		=  AutoConfig.from_pretrained(os.path.join(a__							, "best-checkpoint"   )   )
      a__	:						int      		=  config.idalabel
      a__	:						Optional[Any]      		=  os.path.join(a__							, "eval_results_best-checkpoint.json"   )
      a__	:						Optional[int]      		=  os.path.join(a__							, "test_results_best-checkpoint.json"   )
      assert os.path.exists(a__   )

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

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

      a__	:						Any      		=  os.path.join(a__							, F"""train_pseudo.{args.data_file_extension}"""   )

      if args.evaluation_strategy != IntervalStrategy.NO.value:
         a__	:						int      		=  eval_result

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

            if early_stopping_patience_counter >= args.early_stopping_patience:
               a__	:						Optional[Any]      		=  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"							, a__   )
      logger.info("Best evaluation result: %s = %f"							, args.eval_metric							, a__   )
      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
         shutil.copy(
             os.path.join(a__							, F"""eval_results_iter-{iteration}.json"""   )							, os.path.join(a__							, "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							, a__   )
      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
         shutil.copy(
             os.path.join(a__							, F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""   )							, os.path.join(a__							, "eval_results_best-iteration.json"   )							, )








from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean






def       _A  (      lowerCamelCase   ):

   # A local function to see if a dot lands in the circle.
   def is_in_circle(lowerCamelCase							, lowerCamelCase   ) -> bool:
      a__	:						Any      		=  sqrt((x**2) + (y**2)   )
      # Our circle has a radius of 1, so a distance
      # greater than 1 would land outside the circle.
      return distance_from_centre <= 1

   # The proportion of guesses that landed in the circle
   a__	:						Union[str, Any]      		=  mean(
       int(is_in_circle(uniform(-1.0							, 1.0   )							, uniform(-1.0							, 1.0   )   )   )
       for _ in range(lowerCamelCase   )   )
   # The ratio of the area for circle to square is pi/4.
   a__	:						Any      		=  proportion * 4
   print(F"""The estimated value of pi is {pi_estimate}"""   )
   print(F"""The numpy value of pi is {pi}"""   )
   print(F"""The total error is {abs(pi - pi_estimate   )}"""   )






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase = 0.0							, lowerCamelCase = 1.0							, ):
   return mean(
       function_to_integrate(uniform(lowerCamelCase							, lowerCamelCase   )   ) for _ in range(lowerCamelCase   )   ) * (max_value - min_value)






def       _A  (      lowerCamelCase							, lowerCamelCase = 0.0							, lowerCamelCase = 1.0   ):

   def identity_function(lowerCamelCase   ) -> float:
      return x

   a__	:						int      		=  area_under_curve_estimator(
       lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase   )
   a__	:						int      		=  (max_value * max_value - min_value * min_value) / 2

   print("******************"   )
   print(F"""Estimating area under y=x where x varies from {min_value} to {max_value}"""   )
   print(F"""Estimated value is {estimated_value}"""   )
   print(F"""Expected value is {expected_value}"""   )
   print(F"""Total error is {abs(estimated_value - expected_value   )}"""   )
   print("******************"   )






def       _A  (      lowerCamelCase   ):

   def function_to_integrate(lowerCamelCase   ) -> float:
      return sqrt(4.0 - x * x   )

   a__	:						int      		=  area_under_curve_estimator(
       lowerCamelCase							, lowerCamelCase							, 0.0							, 2.0   )

   print("******************"   )
   print("Estimating pi using area_under_curve_estimator"   )
   print(F"""Estimated value is {estimated_value}"""   )
   print(F"""Expected value is {pi}"""   )
   print(F"""Total error is {abs(estimated_value - pi   )}"""   )
   print("******************"   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

