



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


UpperCAmelCase_									=							"sshleifer/mar_enro_6_3_student"





class 		UpperCAmelCase						(						snake_case_				):





    def 	__lowerCAmelCase							(	self			):
          super().setUp()

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





    @slow
    @require_torch_gpu
    def 	__lowerCAmelCase							(	self			):
          MarianMTModel.from_pretrained(_lowerCAmelCase			)





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

          # bash_script = bash_script.replace("--fp16 ", "")
          _lowerCAmelCase 			=  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

          _lowerCAmelCase 			=  ['''finetune.py'''] + bash_script.split() + args
          with patch.object(_lowerCAmelCase       ,     '''argv'''       ,     _lowerCAmelCase			):
                _lowerCAmelCase 			=  argparse.ArgumentParser()
                _lowerCAmelCase 			=  pl.Trainer.add_argparse_args(_lowerCAmelCase			)
                _lowerCAmelCase 			=  SummarizationModule.add_model_specific_args(_lowerCAmelCase       ,     os.getcwd()			)
                _lowerCAmelCase 			=  parser.parse_args()
                _lowerCAmelCase 			=  main(_lowerCAmelCase			)

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

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





    @timeout_decorator.timeout(600			)
    @slow
    @require_torch_gpu
    def 	__lowerCAmelCase							(	self			):
          _lowerCAmelCase 			=  F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
          _lowerCAmelCase 			=  {
              '''--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
          _lowerCAmelCase 			=  (
              (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''			)[1].strip()
          )
          _lowerCAmelCase 			=  bash_script.replace('''\\\n'''       ,     ''''''			).strip().replace('''"$@"'''       ,     ''''''			)
          _lowerCAmelCase 			=  bash_script.replace('''--fp16 '''       ,     ''' '''			)

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

                _lowerCAmelCase 			=  distill_main(_lowerCAmelCase			)

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

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




from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch





@require_torch
@require_tf
class 		UpperCAmelCase						(						snake_case_				):





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  SMALL_MODEL_IDENTIFIER
         _lowerCAmelCase 			=  '''pt'''
         _lowerCAmelCase 			=  '''tf'''





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  AutoModel.from_pretrained(self.test_model			)
         model_pt.save_pretrained(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  TFAutoModel.from_pretrained(self.test_model       ,     from_pt=_lowerCAmelCase			)
         model_tf.save_pretrained(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''mock_framework'''

         # Framework provided - return whatever the user provides
         _lowerCAmelCase 			=  FeaturesManager.determine_framework(self.test_model       ,     _lowerCAmelCase			)
         self.assertEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)

         # Local checkpoint and framework provided - return provided framework
         # PyTorch checkpoint
         with TemporaryDirectory() as local_pt_ckpt:
               self._setup_pt_ckpt(_lowerCAmelCase			)
               _lowerCAmelCase 			=  FeaturesManager.determine_framework(_lowerCAmelCase       ,     _lowerCAmelCase			)
               self.assertEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)

         # TensorFlow checkpoint
         with TemporaryDirectory() as local_tf_ckpt:
               self._setup_tf_ckpt(_lowerCAmelCase			)
               _lowerCAmelCase 			=  FeaturesManager.determine_framework(_lowerCAmelCase       ,     _lowerCAmelCase			)
               self.assertEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         # PyTorch checkpoint
         with TemporaryDirectory() as local_pt_ckpt:
               self._setup_pt_ckpt(_lowerCAmelCase			)
               _lowerCAmelCase 			=  FeaturesManager.determine_framework(_lowerCAmelCase			)
               self.assertEqual(_lowerCAmelCase       ,     self.framework_pt			)

         # TensorFlow checkpoint
         with TemporaryDirectory() as local_tf_ckpt:
               self._setup_tf_ckpt(_lowerCAmelCase			)
               _lowerCAmelCase 			=  FeaturesManager.determine_framework(_lowerCAmelCase			)
               self.assertEqual(_lowerCAmelCase       ,     self.framework_tf			)

         # Invalid local checkpoint
         with TemporaryDirectory() as local_invalid_ckpt:
               with self.assertRaises(_lowerCAmelCase			):
                     _lowerCAmelCase 			=  FeaturesManager.determine_framework(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  MagicMock(return_value=_lowerCAmelCase			)
         with patch('''transformers.onnx.features.is_tf_available'''       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  FeaturesManager.determine_framework(self.test_model			)
               self.assertEqual(_lowerCAmelCase       ,     self.framework_pt			)

         # PyTorch not in environment -> use TensorFlow
         _lowerCAmelCase 			=  MagicMock(return_value=_lowerCAmelCase			)
         with patch('''transformers.onnx.features.is_torch_available'''       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  FeaturesManager.determine_framework(self.test_model			)
               self.assertEqual(_lowerCAmelCase       ,     self.framework_tf			)

         # Both in environment -> use PyTorch
         _lowerCAmelCase 			=  MagicMock(return_value=_lowerCAmelCase			)
         _lowerCAmelCase 			=  MagicMock(return_value=_lowerCAmelCase			)
         with patch('''transformers.onnx.features.is_tf_available'''       ,     _lowerCAmelCase			), patch(
             '''transformers.onnx.features.is_torch_available'''       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  FeaturesManager.determine_framework(self.test_model			)
               self.assertEqual(_lowerCAmelCase       ,     self.framework_pt			)

         # Both not in environment -> raise error
         _lowerCAmelCase 			=  MagicMock(return_value=_lowerCAmelCase			)
         _lowerCAmelCase 			=  MagicMock(return_value=_lowerCAmelCase			)
         with patch('''transformers.onnx.features.is_tf_available'''       ,     _lowerCAmelCase			), patch(
             '''transformers.onnx.features.is_torch_available'''       ,     _lowerCAmelCase			):
               with self.assertRaises(_lowerCAmelCase			):
                     _lowerCAmelCase 			=  FeaturesManager.determine_framework(self.test_model			)