




import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


lowerCAmelCase_		      = '''sshleifer/mar_enro_6_3_student'''
class 						_A					(  a__      ):






  def 					__a			(  self		:	List[Any]      )		->    Optional[Any]:






       """simple docstring"""







       super().setUp()

       lowercase					:   Optional[Any]								= cached_path(
           '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''      ,						extract_compressed_file=UpperCamelCase_      ,						)
       lowercase					:   List[Any]								= f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""






  @slow
  @require_torch_gpu
  def 					__a			(  self		:	Any      )		->    List[str]:






       """simple docstring"""







       MarianMTModel.from_pretrained(UpperCamelCase_      )






  @slow
  @require_torch_gpu
  def 					__a			(  self		:	Union[str, Any]      )		->    int:






       """simple docstring"""







       lowercase					:   List[str]								= {
           '''$MAX_LEN''': 64,
           '''$BS''': 64,
           '''$GAS''': 1,
           '''$ENRO_DIR''': self.data_dir,
           '''facebook/mbart-large-cc25''': MARIAN_MODEL,
           # "val_check_interval=0.25": "val_check_interval=1.0",
           '''--learning_rate=3e-5''': '''--learning_rate 3e-4''',
           '''--num_train_epochs 6''': '''--num_train_epochs 1''',
       }

       # Clean up bash script
       lowercase					:   Tuple								= (self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''      )[1].strip()
       lowercase					:   Any								= bash_script.replace('''\\\n'''      ,						''''''      ).strip().replace('''"$@"'''      ,						''''''      )
       for k, v in env_vars_to_replace.items():
            lowercase					:   Any								= bash_script.replace(UpperCamelCase_      ,						str(UpperCamelCase_      )      )
       lowercase					:   Optional[Any]								= self.get_auto_remove_tmp_dir()

       # bash_script = bash_script.replace("--fp16 ", "")
       lowercase					:   List[Any]								= f"""
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        """.split()
       # XXX: args.gpus > 1 : handle multi_gpu in the future

       lowercase					:   Union[str, Any]								= ['''finetune.py'''] + bash_script.split() + args
       with patch.object(UpperCamelCase_      ,						'''argv'''      ,						UpperCamelCase_      ):
            lowercase					:   Dict								= argparse.ArgumentParser()
            lowercase					:   Dict								= pl.Trainer.add_argparse_args(UpperCamelCase_      )
            lowercase					:   Optional[int]								= SummarizationModule.add_model_specific_args(UpperCamelCase_      ,						os.getcwd()      )
            lowercase					:   List[Any]								= parser.parse_args()
            lowercase					:   Union[str, Any]								= main(UpperCamelCase_      )

       # Check metrics
       lowercase					:   str								= load_json(model.metrics_save_path      )
       lowercase					:   Any								= metrics['''val'''][0]
       lowercase					:   Optional[Any]								= metrics['''val'''][-1]
       self.assertEqual(len(metrics['''val''']      )      ,						(args.max_epochs / args.val_check_interval)      )
       assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]      ,						UpperCamelCase_      )

       self.assertGreater(last_step_stats['''val_avg_gen_time''']      ,						0.01      )
       # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
       self.assertLessEqual(last_step_stats['''val_avg_gen_time''']      ,						1.0      )

       # test learning requirements:

       # 1. BLEU improves over the course of training by more than 2 pts
       self.assertGreater(last_step_stats['''val_avg_bleu'''] - first_step_stats['''val_avg_bleu''']      ,						2      )

       # 2. BLEU finishes above 17
       self.assertGreater(last_step_stats['''val_avg_bleu''']      ,						17      )

       # 3. test BLEU and val BLEU within ~1.1 pt.
       self.assertLess(abs(metrics['''val'''][-1]['''val_avg_bleu'''] - metrics['''test'''][-1]['''test_avg_bleu''']      )      ,						1.1      )

       # check lightning ckpt can be loaded and has a reasonable statedict
       lowercase					:   List[Any]								= os.listdir(UpperCamelCase_      )
       lowercase					:   Any								= [x for x in contents if x.endswith('''.ckpt'''      )][0]
       lowercase					:   Optional[int]								= os.path.join(args.output_dir      ,						UpperCamelCase_      )
       lowercase					:   Optional[Any]								= torch.load(UpperCamelCase_      ,						map_location='''cpu'''      )
       lowercase					:   List[str]								= '''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
       assert expected_key in ckpt["state_dict"]
       assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

       # TODO: turn on args.do_predict when PL bug fixed.
       if args.do_predict:
            lowercase					:   str								= {os.path.basename(UpperCamelCase_      ) for p in contents}
            assert "test_generations.txt" in contents
            assert "test_results.txt" in contents
            # assert len(metrics["val"]) ==  desired_n_evals
            assert len(metrics['''test''']      ) == 1


class 						_A					(  a__      ):






  @timeout_decorator.timeout(600      )
  @slow
  @require_torch_gpu
  def 					__a			(  self		:	Optional[int]      )		->    Union[str, Any]:






       """simple docstring"""







       lowercase					:   Any								= f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
       lowercase					:   Any								= {
           '''--fp16_opt_level=O1''': '''''',
           '''$MAX_LEN''': 128,
           '''$BS''': 16,
           '''$GAS''': 1,
           '''$ENRO_DIR''': data_dir,
           '''$m''': '''sshleifer/student_marian_en_ro_6_1''',
           '''val_check_interval=0.25''': '''val_check_interval=1.0''',
       }

       # Clean up bash script
       lowercase					:   Tuple								= (
           (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''      )[1].strip()
       )
       lowercase					:   Union[str, Any]								= bash_script.replace('''\\\n'''      ,						''''''      ).strip().replace('''"$@"'''      ,						''''''      )
       lowercase					:   Dict								= bash_script.replace('''--fp16 '''      ,						''' '''      )

       for k, v in env_vars_to_replace.items():
            lowercase					:   List[Any]								= bash_script.replace(UpperCamelCase_      ,						str(UpperCamelCase_      )      )
       lowercase					:   Optional[int]								= self.get_auto_remove_tmp_dir()
       lowercase					:   str								= bash_script.replace('''--fp16'''      ,						''''''      )
       lowercase					:   Optional[Any]								= 6
       lowercase					:   Union[str, Any]								= (
           ['''distillation.py''']
           + bash_script.split()
           + [
               f"""--output_dir={output_dir}""",
               '''--gpus=1''',
               '''--learning_rate=1e-3''',
               f"""--num_train_epochs={epochs}""",
               '''--warmup_steps=10''',
               '''--val_check_interval=1.0''',
               '''--do_predict''',
           ]
       )
       with patch.object(UpperCamelCase_      ,						'''argv'''      ,						UpperCamelCase_      ):
            lowercase					:   Union[str, Any]								= argparse.ArgumentParser()
            lowercase					:   List[Any]								= pl.Trainer.add_argparse_args(UpperCamelCase_      )
            lowercase					:   int								= SummarizationDistiller.add_model_specific_args(UpperCamelCase_      ,						os.getcwd()      )
            lowercase					:   int								= parser.parse_args()
            # assert args.gpus == gpus THIS BREAKS for multi_gpu

            lowercase					:   int								= distill_main(UpperCamelCase_      )

       # Check metrics
       lowercase					:   Dict								= load_json(model.metrics_save_path      )
       lowercase					:   Optional[int]								= metrics['''val'''][0]
       lowercase					:   str								= metrics['''val'''][-1]
       assert len(metrics['''val''']      ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

       assert last_step_stats["val_avg_gen_time"] >= 0.01

       assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
       assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
       assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]      ,						UpperCamelCase_      )

       # check lightning ckpt can be loaded and has a reasonable statedict
       lowercase					:   Dict								= os.listdir(UpperCamelCase_      )
       lowercase					:   List[str]								= [x for x in contents if x.endswith('''.ckpt'''      )][0]
       lowercase					:   Dict								= os.path.join(args.output_dir      ,						UpperCamelCase_      )
       lowercase					:   Any								= torch.load(UpperCamelCase_      ,						map_location='''cpu'''      )
       lowercase					:   Dict								= '''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
       assert expected_key in ckpt["state_dict"]
       assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

       # TODO: turn on args.do_predict when PL bug fixed.
       if args.do_predict:
            lowercase					:   Tuple								= {os.path.basename(UpperCamelCase_      ) for p in contents}
            assert "test_generations.txt" in contents
            assert "test_results.txt" in contents
            # assert len(metrics["val"]) ==  desired_n_evals
            assert len(metrics['''test''']      ) == 1



"""simple docstring"""





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


snake_case__						:   Dict			 = logging.getLogger(__name__)


def    _snake_case  (  _snake_case :	Any       ,      _snake_case :	Any	):
      return (preds == labels).mean()



@dataclass
class 						snake_case_:
     __UpperCamelCase     =    field(
         metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
     __UpperCamelCase     =    field(
         default=a__							,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}	)
     __UpperCamelCase     =    field(
         default=a__							,    metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}	)
     __UpperCamelCase     =    field(
         default=a__							,    metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							,    )



@dataclass
class 						snake_case_:
     __UpperCamelCase     =    field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()	)}	)
     __UpperCamelCase     =    field(metadata={'''help''': '''Should contain the data files for the task.'''}	)
     __UpperCamelCase     =    field(
         default=128							,    metadata={
             '''help''': (
                 '''The maximum total input sequence length after tokenization. Sequences longer '''
                 '''than this will be truncated, sequences shorter will be padded.'''
             )
         }							,    )
     __UpperCamelCase     =    field(
         default=a__							,    metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)



def    _snake_case  (  ):
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.

      lowerCAmelCase    :		str				=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
      lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		Optional[int]				=      parser.parse_args_into_dataclasses()

      if (
          os.path.exists(training_args.output_dir	)
          and os.listdir(training_args.output_dir	)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
            raise ValueError(
                f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
                ''' --overwrite_output_dir to overcome.'''	)

      # Setup logging
      logging.basicConfig(
          format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''       ,      datefmt='''%m/%d/%Y %H:%M:%S'''       ,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN       ,      )
      logger.warning(
          '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''       ,      training_args.local_rank       ,      training_args.device       ,      training_args.n_gpu       ,      bool(training_args.local_rank != -1	)       ,      training_args.fpaa       ,      )
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank	):
            transformers.utils.logging.set_verbosity_info()
            transformers.utils.logging.enable_default_handler()
            transformers.utils.logging.enable_explicit_format()
      logger.info('''Training/evaluation parameters %s'''       ,      _snake_case	)

      # Set seed
      set_seed(training_args.seed	)

      try:
            lowerCAmelCase    :		Tuple				=      processors[data_args.task_name]()
            lowerCAmelCase    :		Any				=      processor.get_labels()
            lowerCAmelCase    :		Union[str, Any]				=      len(_snake_case	)
      except KeyError:
            raise ValueError('''Task not found: %s''' % (data_args.task_name)	)

      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      lowerCAmelCase    :		List[Any]				=      AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,      num_labels=_snake_case       ,      finetuning_task=data_args.task_name       ,      cache_dir=model_args.cache_dir       ,      )
      lowerCAmelCase    :		Optional[Any]				=      AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,      cache_dir=model_args.cache_dir       ,      )
      lowerCAmelCase    :		List[str]				=      AutoModelForMultipleChoice.from_pretrained(
          model_args.model_name_or_path       ,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path	)       ,      config=_snake_case       ,      cache_dir=model_args.cache_dir       ,      )

      # Get datasets
      lowerCAmelCase    :		Dict				=      (
          MultipleChoiceDataset(
              data_dir=data_args.data_dir       ,      tokenizer=_snake_case       ,      task=data_args.task_name       ,      max_seq_length=data_args.max_seq_length       ,      overwrite_cache=data_args.overwrite_cache       ,      mode=Split.train       ,      )
          if training_args.do_train
          else None
      )
      lowerCAmelCase    :		Any				=      (
          MultipleChoiceDataset(
              data_dir=data_args.data_dir       ,      tokenizer=_snake_case       ,      task=data_args.task_name       ,      max_seq_length=data_args.max_seq_length       ,      overwrite_cache=data_args.overwrite_cache       ,      mode=Split.dev       ,      )
          if training_args.do_eval
          else None
      )

      def compute_metrics(_snake_case :	EvalPrediction	) -> Dict:
            lowerCAmelCase    :		int				=      np.argmax(p.predictions       ,      axis=1	)
            return {"acc": simple_accuracy(_snake_case       ,      p.label_ids	)}

      # Data collator
      lowerCAmelCase    :		List[Any]				=      DataCollatorWithPadding(_snake_case       ,      pad_to_multiple_of=8	) if training_args.fpaa else None

      # Initialize our Trainer
      lowerCAmelCase    :		Union[str, Any]				=      Trainer(
          model=_snake_case       ,      args=_snake_case       ,      train_dataset=_snake_case       ,      eval_dataset=_snake_case       ,      compute_metrics=_snake_case       ,      data_collator=_snake_case       ,      )

      # Training
      if training_args.do_train:
            trainer.train(
                model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
            trainer.save_model()
            # For convenience, we also re-save the tokenizer to the same directory,
            # so that you can share your model easily on huggingface.co/models =)
            if trainer.is_world_master():
                  tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
      lowerCAmelCase    :		int				=      {}
      if training_args.do_eval:
            logger.info('''*** Evaluate ***'''	)

            lowerCAmelCase    :		Any				=      trainer.evaluate()

            lowerCAmelCase    :		int				=      os.path.join(training_args.output_dir       ,      '''eval_results.txt'''	)
            if trainer.is_world_master():
                  with open(_snake_case       ,      '''w'''	) as writer:
                        logger.info('''***** Eval results *****'''	)
                        for key, value in result.items():
                              logger.info('''  %s = %s'''       ,      _snake_case       ,      _snake_case	)
                              writer.write('''%s = %s\n''' % (key, value)	)

                        results.update(_snake_case	)

      return results


def    _snake_case  (  _snake_case :	List[str]	):
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
 main()


