




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


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

lowerCamelCase	:	Optional[Any]				=   'pytorch_model.bin'




@dataclasses.dataclass
class 						__lowercase		:




      """simple docstring"""





      _snake_case												=						dataclasses.field(
          metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""}							)
      _snake_case												=						dataclasses.field(
          default=UpperCamelCase__					,		metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""}					,		)




@dataclasses.dataclass
class 						__lowercase		:




      """simple docstring"""





      _snake_case												=						dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""}							)
      _snake_case												=						dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""}							)
      _snake_case												=						dataclasses.field(
          default=UpperCamelCase__					,		metadata={"""help""": """A csv or a json file containing the validation data."""}							)
      _snake_case												=						dataclasses.field(
          default=UpperCamelCase__					,		metadata={"""help""": """The name of the task to train on."""}					,		)
      _snake_case												=						dataclasses.field(
          default=UpperCamelCase__					,		metadata={"""help""": """The list of labels for the task."""}							)




@dataclasses.dataclass
class 						__lowercase		:




      """simple docstring"""





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




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase						,lowercase						,lowercase					)  ->       Union[str, Any]:
   snake_case				:    Dict           =	datasets.concatenate_datasets([infer_input, infer_output]						,axis=1					)

   if args.do_filter_by_confidence:
      snake_case				:    List[str]           =	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
      snake_case				:    Tuple           =	int(eval_result * len(lowercase					)					)
      print(lowercase					)
      snake_case				:    str           =	dataset.sort("""probability"""						,reverse=lowercase					)
      snake_case				:    List[str]           =	dataset.select(range(lowercase					)					)

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

   snake_case				:    Optional[Any]           =	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					)  ->       int:
   snake_case				:    Optional[int]           =	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()

   snake_case				:    Union[str, Any]           =	STModelArguments(model_name_or_path=lowercase					)
   snake_case				:    Dict           =	STDataArguments(train_file=lowercase						,infer_file=lowercase					)
   snake_case				:    Optional[int]           =	STTrainingArguments(output_dir=lowercase					)
   snake_case				:    Tuple           =	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
   snake_case				:    str           =	{}
   snake_case				:    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
   snake_case				:    Tuple           =	args.train_file
   snake_case				:    int           =	args.infer_file

   if args.evaluation_strategy != IntervalStrategy.NO.value:
      assert args.eval_file is not None
      snake_case				:    Any           =	args.eval_file

   for key in data_files:
      snake_case				:    List[str]           =	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:
         snake_case				:    Optional[int]           =	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..."""					)
   snake_case				:    Tuple           =	f"""{args.output_dir}/self-train_iter-{{}}""".format
   snake_case				:    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=lowercase					)
         os.makedirs(lowercase						,exist_ok=lowercase					)
   accelerator.wait_for_everyone()

   snake_case				:    Dict           =	None
   snake_case				:    Tuple           =	None
   snake_case				:    Optional[Any]           =	0
   snake_case				:    int           =	False
   # Show the progress bar
   snake_case				:    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					)					):
      snake_case				:    Tuple           =	data_dir_format(lowercase					)
      assert os.path.exists(lowercase					)

      # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
      # iteration > 0
      snake_case				:    int           =	os.path.join(lowercase						,"""stage-1"""					)
      snake_case				:    List[str]           =	{
          """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}					)

      snake_case				:    List[str]           =	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
         snake_case				:    str           =	os.path.join(lowercase						,"""best-checkpoint"""					)
         snake_case				:    Tuple           =	os.path.join(lowercase						,"""stage-2"""					)
         # Update arguments_dict
         snake_case				:    str           =	model_path
         snake_case				:    Any           =	data_files["""train"""]
         snake_case				:    List[str]           =	current_output_dir

         snake_case				:    Optional[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: 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					)

      snake_case				:    Tuple           =	iteration
      snake_case				:    Any           =	data_dir_format(iteration + 1					)

      snake_case				:    List[str]           =	AutoConfig.from_pretrained(os.path.join(lowercase						,"""best-checkpoint"""					)					)
      snake_case				:    List[Any]           =	config.idalabel
      snake_case				:    List[str]           =	os.path.join(lowercase						,"""eval_results_best-checkpoint.json"""					)
      snake_case				:    Dict           =	os.path.join(lowercase						,"""test_results_best-checkpoint.json"""					)
      assert os.path.exists(lowercase					)

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

      snake_case				:    Optional[Any]           =	os.path.join(lowercase						,f"""train_pseudo.{args.data_file_extension}"""					)

      if args.evaluation_strategy != IntervalStrategy.NO.value:
         snake_case				:    str           =	eval_result

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

            if early_stopping_patience_counter >= args.early_stopping_patience:
               snake_case				:    List[str]           =	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"""					)						,)











import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline




@pytest.mark.integration
def 						SCREAMING_SNAKE_CASE__ (   )  ->       List[Any]:
   with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT					):
      with pytest.raises(lowercase					):
         requests.request("""GET"""						,"""https://huggingface.co"""					)
      with pytest.raises(requests.exceptions.ConnectTimeout					):
         requests.request("""GET"""						,"""https://huggingface.co"""						,timeout=1.0					)




@pytest.mark.integration
def 						SCREAMING_SNAKE_CASE__ (   )  ->       Any:
   with offline(OfflineSimulationMode.CONNECTION_FAILS					):
      with pytest.raises(requests.exceptions.ConnectionError					):
         requests.request("""GET"""						,"""https://huggingface.co"""					)




def 						SCREAMING_SNAKE_CASE__ (   )  ->       int:
   with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1					):
      with pytest.raises(lowercase					):
         http_head("""https://huggingface.co"""					)





