


"""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


__lowercase    =				"""sshleifer/mar_enro_6_3_student"""






class 						_A  (  _a  ):







    """simple docstring"""




    def      __snake_case							( self    :   Any):
      super().setUp()

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






    @slow
    @require_torch_gpu
    def      __snake_case							( self    :   Dict):
      MarianMTModel.from_pretrained(__UpperCAmelCase)






    @slow
    @require_torch_gpu
    def      __snake_case							( self    :   int):
      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						:		str											=      (self.test_file_dir / "train_mbart_cc25_enro.sh").open().read().split("finetune.py")[1].strip()
      a						:		Union[str, Any]											=      bash_script.replace("\\\n"    ,			"").strip().replace("\"$@\""    ,			"")
      for k, v in env_vars_to_replace.items():
        a						:		Optional[int]											=      bash_script.replace(__UpperCAmelCase    ,			str(__UpperCAmelCase))
      a						:		Dict											=      self.get_auto_remove_tmp_dir()

      # bash_script = bash_script.replace("--fp16 ", "")
      a						:		Union[str, Any]											=      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						:		Dict											=      ["finetune.py"] + bash_script.split() + args
      with patch.object(__UpperCAmelCase    ,			"argv"    ,			__UpperCAmelCase):
        a						:		str											=      argparse.ArgumentParser()
        a						:		Dict											=      pl.Trainer.add_argparse_args(__UpperCAmelCase)
        a						:		int											=      SummarizationModule.add_model_specific_args(__UpperCAmelCase    ,			os.getcwd())
        a						:		List[str]											=      parser.parse_args()
        a						:		List[str]											=      main(__UpperCAmelCase)

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







    """simple docstring"""




    @timeout_decorator.timeout(600)
    @slow
    @require_torch_gpu
    def      __snake_case							( self    :   List[str]):
      a						:		Optional[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						:		Any											=      (
          (self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py")[1].strip()
      )
      a						:		int											=      bash_script.replace("\\\n"    ,			"").strip().replace("\"$@\""    ,			"")
      a						:		Optional[int]											=      bash_script.replace("--fp16 "    ,			" ")

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

        a						:		Any											=      distill_main(__UpperCAmelCase)

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

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







import sys
import turtle


def 					lowercase      (    A_  ,       A_	)->				tuple[float, float]:
  '''simple docstring'''







  return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       )->				None:
  '''simple docstring'''







  my_pen.up()
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)
  my_pen.down()
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)

  if depth == 0:
    return

  triangle(A_  ,       get_mid(A_  ,       A_	)  ,       get_mid(A_  ,       A_	)  ,       depth - 1	)
  triangle(A_  ,       get_mid(A_  ,       A_	)  ,       get_mid(A_  ,       A_	)  ,       depth - 1	)
  triangle(A_  ,       get_mid(A_  ,       A_	)  ,       get_mid(A_  ,       A_	)  ,       depth - 1	)


if __name__ == "__main__":
 if len(sys.argv) != 2:
  raise ValueError(
      """Correct format for using this script: """
      """python fractals.py <int:depth_for_fractal>"""
  )
 __lowercase    =				turtle.Turtle()
 my_pen.ht()
 my_pen.speed(5)
 my_pen.pencolor("""red""")

 __lowercase    =				[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
 triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))






