

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__ ( _snake_case     ):



    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           super().setUp()

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



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





           '''simple docstring'''
           MarianMTModel.from_pretrained(UpperCamelCase__    )







    @slow
    @require_torch_gpu
    def    snake_case_     (   self    )  -> Optional[int]:





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

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

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

           # Check metrics
           A_		   = load_json(model.metrics_save_path    )
           A_		   = metrics["""val"""][0]
           A_		   = 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
           A_		   = os.listdir(UpperCamelCase__    )
           A_		   = [x for x in contents if x.endswith(""".ckpt"""    )][0]
           A_		   = os.path.join(args.output_dir						,			UpperCamelCase__    )
           A_		   = torch.load(UpperCamelCase__						,			map_location="""cpu"""    )
           A_		   = """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:
                  A_		   = {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__ ( _snake_case     ):



    @timeout_decorator.timeout(600    )
    @slow
    @require_torch_gpu
    def    snake_case_     (   self    )  -> Optional[int]:





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

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

                  A_		   = distill_main(UpperCamelCase__    )

           # Check metrics
           A_		   = load_json(model.metrics_save_path    )
           A_		   = metrics["""val"""][0]
           A_		   = 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
           A_		   = os.listdir(UpperCamelCase__    )
           A_		   = [x for x in contents if x.endswith(""".ckpt"""    )][0]
           A_		   = os.path.join(args.output_dir						,			UpperCamelCase__    )
           A_		   = torch.load(UpperCamelCase__						,			map_location="""cpu"""    )
           A_		   = """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:
                  A_		   = {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'''







def  UpperCAmelCase__      ( UpperCAmelCase__ = 1_00			)     ->     int:
       A_		   = n * (n + 1) * (2 * n + 1) / 6
       A_		   = (n * (n + 1) / 2) ** 2
       return int(square_of_sum - sum_of_squares			)


if __name__ == "__main__":
      print(f"""{solution() = }""")
