




'''simple docstring'''


from collections.abc import Callable

import numpy as np



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Callable ,    UpperCamelCase__    :					float ,    UpperCamelCase__    :					float ,    UpperCamelCase__    :					float ,    UpperCamelCase__    :					float				):




     """simple docstring"""


     a_     :							Any     						=  int(np.ceil((x_end - xa) / step_size				)				)
     a_     :							List[str]     						=  np.zeros((n + 1,)				)
     a_     :							str     						=  ya
     a_     :							Tuple     						=  xa

     for k in range(UpperCamelCase__				):
          a_     :							Any     						=  y[k] + step_size * ode_func(UpperCamelCase__ ,    y[k]				)
          x += step_size

     return y


if __name__ == "__main__":
 import doctest

 doctest.testmod()






'''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


lowerCAmelCase_      :  List[str]				     =  'sshleifer/mar_enro_6_3_student'







class      SCREAMING_SNAKE_CASE    (						snake_case_				):






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           super().setUp()

           a_     :							Optional[int]     						=  cached_path(
               """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""						,						extract_compressed_file=lowercase__						,						)
           a_     :							List[Any]     						=  F"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"






      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           MarianMTModel.from_pretrained(lowercase__       )






      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Union[str, 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
           a_     :							Optional[int]     						=  (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""       )[1].strip()
           a_     :							List[str]     						=  bash_script.replace("""\\\n"""						,						""""""       ).strip().replace("""\"$@\""""						,						""""""       )
           for k, v in env_vars_to_replace.items():
                a_     :							Optional[Any]     						=  bash_script.replace(lowercase__						,						str(lowercase__       )       )
           a_     :							Union[str, Any]     						=  self.get_auto_remove_tmp_dir()

           # bash_script = bash_script.replace("--fp16 ", "")
           a_     :							List[str]     						=  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

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

           # Check metrics
           a_     :							List[str]     						=  load_json(model.metrics_save_path       )
           a_     :							Any     						=  metrics["""val"""][0]
           a_     :							int     						=  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}"]						,						lowercase__       )

           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_     :							Dict     						=  os.listdir(lowercase__       )
           a_     :							Union[str, Any]     						=  [x for x in contents if x.endswith(""".ckpt"""       )][0]
           a_     :							Optional[Any]     						=  os.path.join(args.output_dir						,						lowercase__       )
           a_     :							Dict     						=  torch.load(lowercase__						,						map_location="""cpu"""       )
           a_     :							Tuple     						=  """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_     :							int     						=  {os.path.basename(lowercase__       ) 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    (						snake_case_				):






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


           '''simple docstring'''

           a_     :							int     						=  F"{self.test_file_dir_str}/test_data/wmt_en_ro"
           a_     :							Dict     						=  {
               """--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_     :							List[str]     						=  (
               (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""       )[1].strip()
           )
           a_     :							List[str]     						=  bash_script.replace("""\\\n"""						,						""""""       ).strip().replace("""\"$@\""""						,						""""""       )
           a_     :							Tuple     						=  bash_script.replace("""--fp16 """						,						""" """       )

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

                a_     :							Union[str, Any]     						=  distill_main(lowercase__       )

           # Check metrics
           a_     :							Union[str, Any]     						=  load_json(model.metrics_save_path       )
           a_     :							List[str]     						=  metrics["""val"""][0]
           a_     :							Optional[Any]     						=  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}"]						,						lowercase__       )

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