


"""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__  							="sshleifer/mar_enro_6_3_student"





class    lowerCamelCase__						(		_a    ):
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''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	:	Tuple							):






       '''simple docstring'''


       MarianMTModel.from_pretrained(A_							)






      @slow
      @require_torch_gpu
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	{
           """$MAX_LEN""": 6_4,
           """$BS""": 6_4,
           """$GAS""": 1,
           """$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"""]      ,       1_7							)

       # 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    lowerCamelCase__						(		_a    ):
      @timeout_decorator.timeout(6_0_0							)
      @slow
      @require_torch_gpu
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
       __lowercase									=	{
           """--fp16_opt_level=O1""": """""",
           """$MAX_LEN""": 1_2_8,
           """$BS""": 1_6,
           """$GAS""": 1,
           """$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




"""simple docstring"""

from __future__ import annotations


def lowerCAmelCase_      (				UpperCamelCase__	: list[float] ):






 """simple docstring"""







 if len(UpperCamelCase__ ) < 2:
  raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
 if any(i <= 0 for i in nums ):
  raise ValueError("""All values must be greater than 0""" )
 __lowercase									=	nums.copy()
 copy_nums.sort()
 return copy_nums[-1] < sum(copy_nums[:-1] )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
