





"""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__				  =   "sshleifer/mar_enro_6_3_student"



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				List[Any]:
        super().setUp()

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

  @slow
  @require_torch_gpu
  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Optional[int]:
        MarianMTModel.from_pretrained(__a	)


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

        # bash_script = bash_script.replace("--fp16 ", "")
        _UpperCamelCase      :	Dict						=       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

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

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





class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


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

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

              _UpperCamelCase      :	str						=       distill_main(__a	)

        # Check metrics
        _UpperCamelCase      :	Optional[int]						=       load_json(model.metrics_save_path	)
        _UpperCamelCase      :	List[str]						=       metrics["val"][0]
        _UpperCamelCase      :	Dict						=       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
        _UpperCamelCase      :	List[str]						=       os.listdir(__a	)
        _UpperCamelCase      :	Tuple						=       [x for x in contents if x.endswith(".ckpt"	)][0]
        _UpperCamelCase      :	str						=       os.path.join(args.output_dir					,   __a	)
        _UpperCamelCase      :	Optional[int]						=       torch.load(__a					,   map_location="cpu"	)
        _UpperCamelCase      :	Union[str, 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:
              _UpperCamelCase      :	Optional[int]						=       {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 string
from itertools import cycle, product
from pathlib import Path

lowerCamelCase__				  =   (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCamelCase__				  =   [ord(letter) for letter in string.ascii_lowercase]
lowerCamelCase__				  =   {ord(char) for char in VALID_CHARS}

lowerCamelCase__				  =   ["the", "be", "to", "of", "and", "in", "that", "have"]




def 					lowercase__    (					lowercase_		,lowercase_			)					->		str | None:




      """simple docstring"""

      _UpperCamelCase      :	str						=       ""
      _UpperCamelCase      :	int
      _UpperCamelCase      :	int
      _UpperCamelCase      :	int

      for keychar, cipherchar in zip(cycle(lowercase_			)		,lowercase_			):
            _UpperCamelCase      :	Dict						=       cipherchar ^ keychar
            if decodedchar not in VALID_INTS:
                  return None
            decoded += chr(lowercase_			)

      return decoded




def 					lowercase__    (					lowercase_			)					->		list[str]:




      """simple docstring"""

      _UpperCamelCase      :	list[str]						=       []
      for key in product(lowercase_		,repeat=3			):
            _UpperCamelCase      :	int						=       try_key(lowercase_		,lowercase_			)
            if encoded is not None:
                  possibles.append(lowercase_			)
      return possibles




def 					lowercase__    (					lowercase_		,lowercase_			)					->		list[str]:




      """simple docstring"""

      return [possible for possible in possibles if common_word in possible.lower()]




def 					lowercase__    (					lowercase_ = "p059_cipher.txt"			)					->		int:




      """simple docstring"""

      _UpperCamelCase      :	list[int]
      _UpperCamelCase      :	list[str]
      _UpperCamelCase      :	str
      _UpperCamelCase      :	str
      _UpperCamelCase      :	str						=       Path(lowercase_			).parent.joinpath(lowercase_			).read_text(encoding="utf-8"			)

      _UpperCamelCase      :	Optional[Any]						=       [int(lowercase_			) for number in data.strip().split(","			)]

      _UpperCamelCase      :	List[str]						=       filter_valid_chars(lowercase_			)
      for common_word in COMMON_WORDS:
            _UpperCamelCase      :	Union[str, Any]						=       filter_common_word(lowercase_		,lowercase_			)
            if len(lowercase_			) == 1:
                  break

      _UpperCamelCase      :	Union[str, Any]						=       possibles[0]
      return sum(ord(lowercase_			) for char in decoded_text			)


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