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

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

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






def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     str			,							UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     List[str]			,							UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     Dict  ):






 """simple docstring"""


 __lowerCAmelCase         =							datasets.concatenate_datasets([infer_input, infer_output]			,							axis=1  )

 if args.do_filter_by_confidence:
  __lowerCAmelCase         =							dataset.filter(lambda UpperCamelCase  : example["probability"] > args.confidence_threshold  )

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

 __lowerCAmelCase         =							dataset.remove_columns(["label", "probability"]  )
 __lowerCAmelCase         =							dataset.rename_column("prediction"			,							"label"  )
 __lowerCAmelCase         =							dataset.map(lambda UpperCamelCase  : {"label": idalabel[example["label"]]}  )
 __lowerCAmelCase         =							dataset.shuffle(seed=args.seed  )

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







def 		_UpperCAmelCase   (	UpperCamelCase:     List[Any]			,							UpperCamelCase:     List[str]			,							UpperCamelCase:     List[str]			,							UpperCamelCase:     Any			,							**UpperCamelCase:     str  ):






 """simple docstring"""


 __lowerCAmelCase         =							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         =							STModelArguments(model_name_or_path=UpperCamelCase  )
 __lowerCAmelCase         =							STDataArguments(train_file=UpperCamelCase			,							infer_file=UpperCamelCase  )
 __lowerCAmelCase         =							STTrainingArguments(output_dir=UpperCamelCase  )
 __lowerCAmelCase         =							argparse.Namespace()

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

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

    # Sanity checks
 __lowerCAmelCase         =							{}
 __lowerCAmelCase         =							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         =							args.train_file
 __lowerCAmelCase         =							args.infer_file

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

 for key in data_files:
  __lowerCAmelCase         =							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         =							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         =							F"{args.output_dir}/self-train_iter-{{}}".format
 __lowerCAmelCase         =							data_dir_format(0  )

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

 __lowerCAmelCase         =							None
 __lowerCAmelCase         =							None
 __lowerCAmelCase         =							0
 __lowerCAmelCase         =							False
 # Show the progress bar
 __lowerCAmelCase         =							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         =							data_dir_format(UpperCamelCase  )
  assert os.path.exists(UpperCamelCase  )

  # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
  # iteration > 0
  __lowerCAmelCase         =							os.path.join(UpperCamelCase			,							"stage-1"  )
  __lowerCAmelCase         =							{
      "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(UpperCamelCase			,							UpperCamelCase  ):
    arguments_dict.update({key: value}  )

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

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

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

  __lowerCAmelCase         =							iteration
  __lowerCAmelCase         =							data_dir_format(iteration + 1  )

  __lowerCAmelCase         =							AutoConfig.from_pretrained(os.path.join(UpperCamelCase			,							"best-checkpoint"  )  )
  __lowerCAmelCase         =							config.idalabel
  __lowerCAmelCase         =							os.path.join(UpperCamelCase			,							"eval_results_best-checkpoint.json"  )
  __lowerCAmelCase         =							os.path.join(UpperCamelCase			,							"test_results_best-checkpoint.json"  )
  assert os.path.exists(UpperCamelCase  )

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

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

  __lowerCAmelCase         =							os.path.join(UpperCamelCase			,							F"train_pseudo.{args.data_file_extension}"  )

  if args.evaluation_strategy != IntervalStrategy.NO.value:
   __lowerCAmelCase         =							eval_result

   if best_iteration is None:
    __lowerCAmelCase         =							new_iteration
    __lowerCAmelCase         =							new_eval_result
   else:
    if new_eval_result - best_eval_result > args.early_stopping_threshold:
     __lowerCAmelCase         =							new_iteration
     __lowerCAmelCase         =							new_eval_result
     __lowerCAmelCase         =							0
    else:
     if new_eval_result == best_eval_result:
      __lowerCAmelCase         =							new_iteration
      __lowerCAmelCase         =							new_eval_result
     early_stopping_patience_counter += 1

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








# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    "pipelines_utils",
    "0.22.0",
    "Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
    standard_warn=False,
    stacklevel=3,
)






