



"""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


lowerCamelCase  			=		logging.getLogger(__name__)

lowerCamelCase  			=		"""pytorch_model.bin"""
@dataclasses.dataclass
class  lowercase__  :







       '''simple docstring'''


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



@dataclasses.dataclass
class  lowercase__  :







       '''simple docstring'''


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



@dataclasses.dataclass
class  lowercase__  :







       '''simple docstring'''


       UpperCamelCase		=	dataclasses.field(
           metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''} )
       UpperCamelCase		=	dataclasses.field(
           default='''accuracy'''							,   metadata={'''help''': '''The evaluation metric used for the task.'''} )
       UpperCamelCase		=	dataclasses.field(
           default='''no'''							,   metadata={
               '''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
           }							,   )
       UpperCamelCase		=	dataclasses.field(
           default=10							,   metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}							,   )
       UpperCamelCase		=	dataclasses.field(
           default=0.0							,   metadata={
               '''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
           }							,   )
       UpperCamelCase		=	dataclasses.field(
           default=SCREAMING_SNAKE_CASE							,   metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''}							,   )
       UpperCamelCase		=	dataclasses.field(
           default=SCREAMING_SNAKE_CASE							,   metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''}							,   )
       UpperCamelCase		=	dataclasses.field(
           default=SCREAMING_SNAKE_CASE							,   metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''}							,   )
       UpperCamelCase		=	dataclasses.field(
           default=0.0							,   metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''}							,   )
       UpperCamelCase		=	dataclasses.field(
           default=1_00							,   metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}							,   )
       UpperCamelCase		=	dataclasses.field(
           default=SCREAMING_SNAKE_CASE							,   metadata={'''help''': '''Random seed for initialization.'''}							,   )



def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				datasets.concatenate_datasets([infer_input, infer_output]      ,     axis=1 )

      if args.do_filter_by_confidence:
            UpperCAmelCase_     					=				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
            UpperCAmelCase_     					=				int(eval_result * len(lowerCAmelCase__ ) )
            print(lowerCAmelCase__ )
            UpperCAmelCase_     					=				dataset.sort("probability"      ,     reverse=lowerCAmelCase__ )
            UpperCAmelCase_     					=				dataset.select(range(lowerCAmelCase__ ) )

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

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


def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     **lowerCAmelCase__ ):
      UpperCAmelCase_     					=				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()

      UpperCAmelCase_     					=				STModelArguments(model_name_or_path=lowerCAmelCase__ )
      UpperCAmelCase_     					=				STDataArguments(train_file=lowerCAmelCase__      ,     infer_file=lowerCAmelCase__ )
      UpperCAmelCase_     					=				STTrainingArguments(output_dir=lowerCAmelCase__ )
      UpperCAmelCase_     					=				argparse.Namespace()

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

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

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

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

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

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

      UpperCAmelCase_     					=				None
      UpperCAmelCase_     					=				None
      UpperCAmelCase_     					=				0
      UpperCAmelCase_     					=				False
      # Show the progress bar
      UpperCAmelCase_     					=				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 ) ):
            UpperCAmelCase_     					=				data_dir_format(lowerCAmelCase__ )
            assert os.path.exists(lowerCAmelCase__ )

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

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

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

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

            UpperCAmelCase_     					=				iteration
            UpperCAmelCase_     					=				data_dir_format(iteration + 1 )

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

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

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

            UpperCAmelCase_     					=				os.path.join(lowerCAmelCase__      ,     f"""train_pseudo.{args.data_file_extension}""" )

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

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

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












# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

lowerCamelCase      :       List[str]    =						'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
lowerCamelCase      :       str    =						[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
lowerCamelCase      :       List[Any]    =						dict(zip(vocab, range(len(vocab))))
lowerCamelCase      :       List[str]    =						['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
 lowerCamelCase      :       int    =						Path(tmpdirname)
 lowerCamelCase      :       Optional[int]    =						build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
 lowerCamelCase      :       List[str]    =						build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
 lowerCamelCase      :       int    =						build_dir / VOCAB_FILES_NAMES['''merges_file''']
 with open(src_vocab_file, '''w''') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(tgt_vocab_file, '''w''') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(merges_file, '''w''') as fp:
  fp.write('''\n'''.join(merges))

 lowerCamelCase      :       Dict    =						FSMTTokenizer(
     langs=['''en''', '''ru'''],
     src_vocab_size=len(vocab),
     tgt_vocab_size=len(vocab),
     src_vocab_file=src_vocab_file,
     tgt_vocab_file=tgt_vocab_file,
     merges_file=merges_file,
 )

lowerCamelCase      :       int    =						FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

lowerCamelCase      :       Any    =						FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
lowerCamelCase      :       int    =						tokenizer(['''Making tiny model'''], return_tensors='''pt''')
lowerCamelCase      :       Dict    =						tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru






