


"""simple docstring"""



from __future__ import annotations

import math






def       lowerCamelCase					(a_						:Dict				,				a_						:Union[str, Any]				,				a_						:str				,				a_						:Tuple				,				a_						:int)     ->							Dict:

       '''simple docstring'''





       if depth < 0:
              raise ValueError('''Depth cannot be less than 0''')

       if len(SCREAMING_SNAKE_CASE_) == 0:
              raise ValueError('''Scores cannot be empty''')

       if depth == height:
              return scores[node_index]

       if is_max:
              return max(
                  minimax(depth + 1				,				node_index * 2				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_)				,				minimax(depth + 1				,				node_index * 2 + 1				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_)				,				)

       return min(
           minimax(depth + 1				,				node_index * 2				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_)				,				minimax(depth + 1				,				node_index * 2 + 1				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_)				,				)






def       lowerCamelCase					()     ->							Any:

       '''simple docstring'''





       lowercase		:List[Any]       							=				[90, 23, 6, 33, 21, 65, 123, 3_4423]
       lowercase		:Optional[int]       							=				math.log(len(SCREAMING_SNAKE_CASE_)				,				2)
       print('''Optimal value : '''				,				end='''''')
       print(minimax(0				,				0				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_))


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()





"""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 					__magic_name__		(			__UpperCAmelCase     ):






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              super().setUp()

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






       @slow
       @require_torch_gpu
       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              MarianMTModel.from_pretrained(snake_case__				)







       @slow
       @require_torch_gpu
       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase		:str       							=				{
                  '''$MAX_LEN''': 6_4,
                  '''$BS''': 6_4,
                  '''$GAS''': 1,
                  '''$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
              lowercase		:str       							=				(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''				)[1].strip()
              lowercase		:Any       							=				bash_script.replace('''\\\n'''  ,     ''''''				).strip().replace('''"$@"'''  ,     ''''''				)
              for k, v in env_vars_to_replace.items():
                     lowercase		:Dict       							=				bash_script.replace(snake_case__  ,     str(snake_case__				)				)
              lowercase		:Any       							=				self.get_auto_remove_tmp_dir()

              # bash_script = bash_script.replace("--fp16 ", "")
              lowercase		:Optional[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

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

              # Check metrics
              lowercase		:int       							=				load_json(model.metrics_save_path				)
              lowercase		:Tuple       							=				metrics['''val'''][0]
              lowercase		:Any       							=				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}"""]  ,     snake_case__				)

              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''']  ,     1_7				)

              # 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
              lowercase		:Any       							=				os.listdir(snake_case__				)
              lowercase		:int       							=				[x for x in contents if x.endswith('''.ckpt'''				)][0]
              lowercase		:Any       							=				os.path.join(args.output_dir  ,     snake_case__				)
              lowercase		:int       							=				torch.load(snake_case__  ,     map_location='''cpu'''				)
              lowercase		: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:
                     lowercase		:Optional[Any]       							=				{os.path.basename(snake_case__				) 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 					__magic_name__		(			__UpperCAmelCase     ):






       @timeout_decorator.timeout(6_0_0				)
       @slow
       @require_torch_gpu
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
              lowercase		:List[Any]       							=				{
                  '''--fp16_opt_level=O1''': '''''',
                  '''$MAX_LEN''': 1_2_8,
                  '''$BS''': 1_6,
                  '''$GAS''': 1,
                  '''$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
              lowercase		:Dict       							=				(
                  (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''				)[1].strip()
              )
              lowercase		:Union[str, Any]       							=				bash_script.replace('''\\\n'''  ,     ''''''				).strip().replace('''"$@"'''  ,     ''''''				)
              lowercase		:Dict       							=				bash_script.replace('''--fp16 '''  ,     ''' '''				)

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

                     lowercase		:Tuple       							=				distill_main(snake_case__				)

              # Check metrics
              lowercase		:Tuple       							=				load_json(model.metrics_save_path				)
              lowercase		:int       							=				metrics['''val'''][0]
              lowercase		:List[str]       							=				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}"""]  ,     snake_case__				)

              # check lightning ckpt can be loaded and has a reasonable statedict
              lowercase		:Union[str, Any]       							=				os.listdir(snake_case__				)
              lowercase		:List[str]       							=				[x for x in contents if x.endswith('''.ckpt'''				)][0]
              lowercase		:Dict       							=				os.path.join(args.output_dir  ,     snake_case__				)
              lowercase		:Optional[int]       							=				torch.load(snake_case__  ,     map_location='''cpu'''				)
              lowercase		: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:
                     lowercase		:int       							=				{os.path.basename(snake_case__				) 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

