





'''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[Any]		     ="sshleifer/mar_enro_6_3_student"






class        UpperCAmelCase     (	UpperCamelCase__    ):
 def 					UpperCAmelCase_							(    self				:Dict							)->							int:
     super().setUp()

     A__    =      cached_path(
         "https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"						,	extract_compressed_file=lowercase_						,	)
     A__    =      F"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"
 @slow
 @require_torch_gpu
 def 					UpperCAmelCase_							(    self				:int							)->							Dict:
     MarianMTModel.from_pretrained(lowercase_							)



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

     # bash_script = bash_script.replace("--fp16 ", "")
     A__    =      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__    =      ["finetune.py"] + bash_script.split() + args
     with patch.object(lowercase_						,	"argv"						,	lowercase_							):
         A__    =      argparse.ArgumentParser()
         A__    =      pl.Trainer.add_argparse_args(lowercase_							)
         A__    =      SummarizationModule.add_model_specific_args(lowercase_						,	os.getcwd()							)
         A__    =      parser.parse_args()
         A__    =      main(lowercase_							)

     # Check metrics
     A__    =      load_json(model.metrics_save_path							)
     A__    =      metrics["val"][0]
     A__    =      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"]						,	0.0_1							)
     # 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__    =      os.listdir(lowercase_							)
     A__    =      [x for x in contents if x.endswith(".ckpt"							)][0]
     A__    =      os.path.join(args.output_dir						,	lowercase_							)
     A__    =      torch.load(lowercase_						,	map_location="cpu"							)
     A__    =      "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__    =      {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        UpperCAmelCase     (	UpperCamelCase__    ):
 @timeout_decorator.timeout(6_00							)
 @slow
 @require_torch_gpu
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							int:
     A__    =      F"{self.test_file_dir_str}/test_data/wmt_en_ro"
     A__    =      {
         "--fp16_opt_level=O1": "",
         "$MAX_LEN": 1_28,
         "$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__    =      (
         (self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py"							)[1].strip()
     )
     A__    =      bash_script.replace("\\\n"						,	""							).strip().replace("\"$@\""						,	""							)
     A__    =      bash_script.replace("--fp16 "						,	" "							)

     for k, v in env_vars_to_replace.items():
         A__    =      bash_script.replace(lowercase_						,	str(lowercase_							)							)
     A__    =      self.get_auto_remove_tmp_dir()
     A__    =      bash_script.replace("--fp16"						,	""							)
     A__    =      6
     A__    =      (
         ["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__    =      argparse.ArgumentParser()
         A__    =      pl.Trainer.add_argparse_args(lowercase_							)
         A__    =      SummarizationDistiller.add_model_specific_args(lowercase_						,	os.getcwd()							)
         A__    =      parser.parse_args()
         # assert args.gpus == gpus THIS BREAKS for multi_gpu

         A__    =      distill_main(lowercase_							)

     # Check metrics
     A__    =      load_json(model.metrics_save_path							)
     A__    =      metrics["val"][0]
     A__    =      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"] >= 0.0_1

     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__    =      os.listdir(lowercase_							)
     A__    =      [x for x in contents if x.endswith(".ckpt"							)][0]
     A__    =      os.path.join(args.output_dir						,	lowercase_							)
     A__    =      torch.load(lowercase_						,	map_location="cpu"							)
     A__    =      "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__    =      {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










'''simple docstring'''

def 						UpperCamelCase    (							):
    A__    =      [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]

    A__    =      6
    A__    =      1
    A__    =      19_01

    A__    =      0

    while year < 20_01:
        day += 7

        if (year % 4 == 0 and year % 1_00 != 0) or (year % 4_00 == 0):
            if day > days_per_month[month - 1] and month != 2:
                month += 1
                A__    =      day - days_per_month[month - 2]
            elif day > 29 and month == 2:
                month += 1
                A__    =      day - 29
        else:
            if day > days_per_month[month - 1]:
                month += 1
                A__    =      day - days_per_month[month - 2]

        if month > 12:
            year += 1
            A__    =      1

        if year < 20_01 and day == 1:
            sundays += 1
    return sundays


if __name__ == "__main__":
  print(solution())



