





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


lowercase_          =		"""sshleifer/mar_enro_6_3_student"""
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Optional[Any]:





     """simple docstring"""







     super().setUp()

     lowercase__           =  cached_path(
         'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'      ,					extract_compressed_file=a      ,					)
     lowercase__           =  f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""


   @slow
   @require_torch_gpu
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       Dict:





     """simple docstring"""







     MarianMTModel.from_pretrained(a	)


   @slow
   @require_torch_gpu
   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       Tuple:





     """simple docstring"""







     lowercase__           =  {
         '$MAX_LEN': 64,
         '$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__           =  (self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'	)[1].strip()
     lowercase__           =  bash_script.replace('\\\n'      ,					''	).strip().replace('"$@"'      ,					''	)
     for k, v in env_vars_to_replace.items():
       lowercase__           =  bash_script.replace(a      ,					str(a	)	)
     lowercase__           =  self.get_auto_remove_tmp_dir()

     # bash_script = bash_script.replace("--fp16 ", "")
     lowercase__           =  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__           =  ['finetune.py'] + bash_script.split() + args
     with patch.object(a      ,					'argv'      ,					a	):
       lowercase__           =  argparse.ArgumentParser()
       lowercase__           =  pl.Trainer.add_argparse_args(a	)
       lowercase__           =  SummarizationModule.add_model_specific_args(a      ,					os.getcwd()	)
       lowercase__           =  parser.parse_args()
       lowercase__           =  main(a	)

     # Check metrics
     lowercase__           =  load_json(model.metrics_save_path	)
     lowercase__           =  metrics['val'][0]
     lowercase__           =  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}"""]      ,					a	)

     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__           =  os.listdir(a	)
     lowercase__           =  [x for x in contents if x.endswith('.ckpt'	)][0]
     lowercase__           =  os.path.join(args.output_dir      ,					a	)
     lowercase__           =  torch.load(a      ,					map_location='cpu'	)
     lowercase__           =  '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__           =  {os.path.basename(a	) 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     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):


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





     """simple docstring"""







     lowercase__           =  f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
     lowercase__           =  {
         '--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__           =  (
         (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'	)[1].strip()
     )
     lowercase__           =  bash_script.replace('\\\n'      ,					''	).strip().replace('"$@"'      ,					''	)
     lowercase__           =  bash_script.replace('--fp16 '      ,					' '	)

     for k, v in env_vars_to_replace.items():
       lowercase__           =  bash_script.replace(a      ,					str(a	)	)
     lowercase__           =  self.get_auto_remove_tmp_dir()
     lowercase__           =  bash_script.replace('--fp16'      ,					''	)
     lowercase__           =  6
     lowercase__           =  (
         ['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(a      ,					'argv'      ,					a	):
       lowercase__           =  argparse.ArgumentParser()
       lowercase__           =  pl.Trainer.add_argparse_args(a	)
       lowercase__           =  SummarizationDistiller.add_model_specific_args(a      ,					os.getcwd()	)
       lowercase__           =  parser.parse_args()
       # assert args.gpus == gpus THIS BREAKS for multi_gpu

       lowercase__           =  distill_main(a	)

     # Check metrics
     lowercase__           =  load_json(model.metrics_save_path	)
     lowercase__           =  metrics['val'][0]
     lowercase__           =  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}"""]      ,					a	)

     # check lightning ckpt can be loaded and has a reasonable statedict
     lowercase__           =  os.listdir(a	)
     lowercase__           =  [x for x in contents if x.endswith('.ckpt'	)][0]
     lowercase__           =  os.path.join(args.output_dir      ,					a	)
     lowercase__           =  torch.load(a      ,					map_location='cpu'	)
     lowercase__           =  '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__           =  {os.path.basename(a	) 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









import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Union[str, Any]:
  lowercase__           =  [
      'encoder.version',
      'decoder.version',
      'model.encoder.version',
      'model.decoder.version',
      '_float_tensor',
      'decoder.output_projection.weight',
  ]
  for k in ignore_keys:
    state_dict.pop(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Dict:
  lowercase__      ,   lowercase__           =  emb.weight.shape
  lowercase__           =  nn.Linear(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						bias=_SCREAMING_SNAKE_CASE		)
  lowercase__           =  emb.weight.data
  return lin_layer




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE="facebook/mbart-large-en-ro"		,						_SCREAMING_SNAKE_CASE=False		,						_SCREAMING_SNAKE_CASE=False		)    ->       str:
  lowercase__           =  torch.load(_SCREAMING_SNAKE_CASE		,						map_location='cpu'		)['model']
  remove_ignore_keys_(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  state_dict['encoder.embed_tokens.weight'].shape[0]

  lowercase__           =  MBartConfig.from_pretrained(_SCREAMING_SNAKE_CASE		,						vocab_size=_SCREAMING_SNAKE_CASE		)
  if mbart_aa and finetuned:
    lowercase__           =  'relu'

  lowercase__           =  state_dict['decoder.embed_tokens.weight']
  lowercase__           =  MBartForConditionalGeneration(_SCREAMING_SNAKE_CASE		)
  model.model.load_state_dict(_SCREAMING_SNAKE_CASE		)

  if finetuned:
    lowercase__           =  make_linear_from_emb(model.model.shared		)

  return model


if __name__ == "__main__":
      lowercase_          =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
      )
      parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
      parser.add_argument(
          """--hf_config""",
          default="""facebook/mbart-large-cc25""",
          type=str,
          help="""Which huggingface architecture to use: mbart-large""",
      )
      parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
      parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
      lowercase_          =		parser.parse_args()
      lowercase_          =		convert_fairseq_mbart_checkpoint_from_disk(
          args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
      )
      model.save_pretrained(args.pytorch_dump_folder_path)


