



'''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_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




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

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




  @slow
  @require_torch_gpu
  def 						_lowercase     ( self      :	Union[str, Any]   )					->					str:
    MarianMTModel.from_pretrained(_a   )




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

    # bash_script = bash_script.replace("--fp16 ", "")
    __lowerCamelCase       :       Tuple           = 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

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

    # Check metrics
    __lowerCamelCase       :       Dict           = load_json(model.metrics_save_path   )
    __lowerCamelCase       :       Tuple           = metrics['val'][0]
    __lowerCamelCase       :       str           = 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']			,	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       :       Any           = os.listdir(_a   )
    __lowerCamelCase       :       List[str]           = [x for x in contents if x.endswith('.ckpt'   )][0]
    __lowerCamelCase       :       str           = os.path.join(args.output_dir			,	_a   )
    __lowerCamelCase       :       List[Any]           = torch.load(_a			,	map_location='cpu'   )
    __lowerCamelCase       :       List[Any]           = '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       :       Tuple           = {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_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




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

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

      __lowerCamelCase       :       Union[str, Any]           = distill_main(_a   )

    # Check metrics
    __lowerCamelCase       :       Optional[int]           = load_json(model.metrics_save_path   )
    __lowerCamelCase       :       Tuple           = metrics['val'][0]
    __lowerCamelCase       :       int           = 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
    __lowerCamelCase       :       Union[str, Any]           = os.listdir(_a   )
    __lowerCamelCase       :       List[str]           = [x for x in contents if x.endswith('.ckpt'   )][0]
    __lowerCamelCase       :       List[str]           = os.path.join(args.output_dir			,	_a   )
    __lowerCamelCase       :       str           = torch.load(_a			,	map_location='cpu'   )
    __lowerCamelCase       :       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:
      __lowerCamelCase       :       List[Any]           = {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

import math
from collections.abc import Callable

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = 100       ,)      ->      float:
  __lowerCamelCase       :       Dict           = x_start
  __lowerCamelCase       :       int           = fnc(_lowerCAmelCase       )
  __lowerCamelCase       :       Dict           = 0.0

  for _ in range(_lowerCAmelCase       ):
    # Approximates curve as a sequence of linear lines and sums their length
    __lowerCamelCase       :       List[str]           = (x_end - x_start) / steps + xa
    __lowerCamelCase       :       List[Any]           = fnc(_lowerCAmelCase       )
    length += math.hypot(xa - xa       ,fxa - fxa       )

    # Increment step
    __lowerCamelCase       :       Any           = xa
    __lowerCamelCase       :       Tuple           = fxa

  return length


if __name__ == "__main__":

   def        a_     (							_lowerCAmelCase       )      ->      Dict:
     return math.sin(10 * x       )

   print('f(x) = sin(10 * x)')
   print('The length of the curve from x = -10 to x = 10 is:')
   _UpperCamelCase											=						10
   while i <= 100000:
      print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
      i *= 10






