
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__		:List[Any]   =    "sshleifer/mar_enro_6_3_student"







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def  A__      (				self):
												super().setUp()

												lowercase							=						cached_path(
												    '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''   ,extract_compressed_file=A__   ,)
												lowercase							=						f'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'




							@slow
							@require_torch_gpu
							def  A__      (				self):
												MarianMTModel.from_pretrained(A__)





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

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

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

												# Check metrics
												lowercase							=						load_json(model.metrics_save_path)
												lowercase							=						metrics['''val'''][0]
												lowercase							=						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''']   ,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							=						os.listdir(A__)
												lowercase							=						[x for x in contents if x.endswith('''.ckpt''')][0]
												lowercase							=						os.path.join(args.output_dir   ,A__)
												lowercase							=						torch.load(A__   ,map_location='''cpu''')
												lowercase							=						'''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							=						{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 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							@timeout_decorator.timeout(6_0_0)
							@slow
							@require_torch_gpu
							def  A__      (				self):
												lowercase							=						f'{self.test_file_dir_str}/test_data/wmt_en_ro'
												lowercase							=						{
												    '''--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							=						(
												    (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py''')[1].strip()
												)
												lowercase							=						bash_script.replace('''\\\n'''   ,'''''').strip().replace('''"$@"'''   ,'''''')
												lowercase							=						bash_script.replace('''--fp16 '''   ,''' ''')

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

																	lowercase							=						distill_main(A__)

												# Check metrics
												lowercase							=						load_json(model.metrics_save_path)
												lowercase							=						metrics['''val'''][0]
												lowercase							=						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
												lowercase							=						os.listdir(A__)
												lowercase							=						[x for x in contents if x.endswith('''.ckpt''')][0]
												lowercase							=						os.path.join(args.output_dir   ,A__)
												lowercase							=						torch.load(A__   ,map_location='''cpu''')
												lowercase							=						'''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							=						{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





def lowerCamelCase  (   SCREAMING_SNAKE_CASE = 1  ,			SCREAMING_SNAKE_CASE = 1_000	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		1
    __UpperCamelCase     :Any			  =		0

    for divide_by_number in range(SCREAMING_SNAKE_CASE  ,			digit + 1	):
        __UpperCamelCase     :list[int]			  =		[]
        __UpperCamelCase     :Optional[int]			  =		numerator
        for _ in range(1  ,			digit + 1	):
            if now_divide in has_been_divided:
                if longest_list_length < len(SCREAMING_SNAKE_CASE	):
                    __UpperCamelCase     :Optional[Any]			  =		len(SCREAMING_SNAKE_CASE	)
                    __UpperCamelCase     :int			  =		divide_by_number
            else:
                has_been_divided.append(SCREAMING_SNAKE_CASE	)
                __UpperCamelCase     :Any			  =		now_divide * 10 % divide_by_number

    return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()





