






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


snake_case     					=		"""sshleifer/mar_enro_6_3_student"""


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     def 					_A							(					self							:						List[str]   ):
       super().setUp()

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


     @slow
     @require_torch_gpu
     def 					_A							(					self							:						List[Any]   ):
       MarianMTModel.from_pretrained(UpperCAmelCase_   )






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

       # bash_script = bash_script.replace("--fp16 ", "")
       SCREAMING_SNAKE_CASE							:   str						    =     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

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

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


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

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

         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     distill_main(UpperCAmelCase_   )

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







from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def    lowerCAmelCase_  (		lowercase:				str = ""		)     ->					dict[str, float]:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
 _UpperCamelCase:  Union[str, Any]    						=			BeautifulSoup(requests.get(lowercase		).text	,       '''html.parser'''		)
 _UpperCamelCase:  List[Any]    						=			soup.find_all('''td'''	,       attrs='''titleColumn'''		)
 _UpperCamelCase:  str    						=			soup.find_all('''td'''	,       class_='''ratingColumn imdbRating'''		)
 return {
     title.a.text: float(rating.strong.text		)
     for title, rating in zip(lowercase	,       lowercase		)
 }




def    lowerCAmelCase_  (		lowercase:				str = "IMDb_Top_250_Movies.csv"		)     ->					None:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			get_imdb_top_aaa_movies()
 with open(lowercase	,       '''w'''	,       newline=''''''		) as out_file:
  _UpperCamelCase:  Optional[Any]    						=			csv.writer(lowercase		)
  writer.writerow(['''Movie title''', '''IMDb rating''']		)
  for title, rating in movies.items():
   writer.writerow([title, rating]		)


if __name__ == "__main__":
  write_movies()