


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__UpperCAmelCase    :Union[str, Any]				       =     {
    "configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    :int				       =     [
          "MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MegaForCausalLM",
          "MegaForMaskedLM",
          "MegaForMultipleChoice",
          "MegaForQuestionAnswering",
          "MegaForSequenceClassification",
          "MegaForTokenClassification",
          "MegaModel",
          "MegaPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mega import (
                MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
                MegaForCausalLM,
                MegaForMaskedLM,
                MegaForMultipleChoice,
                MegaForQuestionAnswering,
                MegaForSequenceClassification,
                MegaForTokenClassification,
                MegaModel,
                MegaPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase    :Any				       =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



'''simple docstring'''



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    :Any				       =     "sshleifer/mar_enro_6_3_student"



class 			a       (	_a ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	int    )		->							Optional[Any]:
          super().setUp()

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

     @slow
     @require_torch_gpu
     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Optional[Any]:
          MarianMTModel.from_pretrained(snake_case    )






     @slow
     @require_torch_gpu
     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							int:
          __UpperCAmelCase			:				Optional[Any]													=    {
              '''$MAX_LEN''': 64,
              '''$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
          __UpperCAmelCase			:				Tuple													=    (self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''    )[1].strip()
          __UpperCAmelCase			:				List[Any]													=    bash_script.replace('''\\\n'''							,				''''''    ).strip().replace('''"$@"'''							,				''''''    )
          for k, v in env_vars_to_replace.items():
               __UpperCAmelCase			:				Any													=    bash_script.replace(snake_case							,				str(snake_case    )    )
          __UpperCAmelCase			:				str													=    self.get_auto_remove_tmp_dir()

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

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

          # Check metrics
          __UpperCAmelCase			:				Optional[int]													=    load_json(model.metrics_save_path    )
          __UpperCAmelCase			:				int													=    metrics['''val'''][0]
          __UpperCAmelCase			:				List[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}']							,				snake_case    )

          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
          __UpperCAmelCase			:				Any													=    os.listdir(snake_case    )
          __UpperCAmelCase			:				Tuple													=    [x for x in contents if x.endswith('''.ckpt'''    )][0]
          __UpperCAmelCase			:				Optional[int]													=    os.path.join(args.output_dir							,				snake_case    )
          __UpperCAmelCase			:				Optional[Any]													=    torch.load(snake_case							,				map_location='''cpu'''    )
          __UpperCAmelCase			:				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:
               __UpperCAmelCase			:				Union[str, Any]													=    {os.path.basename(snake_case    ) 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 ):


     """simple docstring"""






     @timeout_decorator.timeout(600    )
     @slow
     @require_torch_gpu
     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Optional[Any]:
          __UpperCAmelCase			:				Any													=    f'{self.test_file_dir_str}/test_data/wmt_en_ro'
          __UpperCAmelCase			:				List[str]													=    {
              '''--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
          __UpperCAmelCase			:				str													=    (
              (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''    )[1].strip()
          )
          __UpperCAmelCase			:				Dict													=    bash_script.replace('''\\\n'''							,				''''''    ).strip().replace('''"$@"'''							,				''''''    )
          __UpperCAmelCase			:				Tuple													=    bash_script.replace('''--fp16 '''							,				''' '''    )

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

               __UpperCAmelCase			:				str													=    distill_main(snake_case    )

          # Check metrics
          __UpperCAmelCase			:				int													=    load_json(model.metrics_save_path    )
          __UpperCAmelCase			:				List[str]													=    metrics['''val'''][0]
          __UpperCAmelCase			:				Tuple													=    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}']							,				snake_case    )

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