





"""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


A			      =	'''sshleifer/mar_enro_6_3_student'''




class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




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

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







					@slow
					@require_torch_gpu
					def 			_lowerCamelCase		(    self  ):
									MarianMTModel.from_pretrained(_UpperCAmelCase  )







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

									# bash_script = bash_script.replace("--fp16 ", "")
									__a			:    int      							=	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

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

									# Check metrics
									__a			:    int      							=	load_json(model.metrics_save_path  )
									__a			:    Tuple      							=	metrics['''val'''][0]
									__a			:    Union[str, 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}"""]						,      _UpperCAmelCase  )

									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			:    Optional[int]      							=	os.listdir(_UpperCAmelCase  )
									__a			:    Optional[int]      							=	[x for x in contents if x.endswith('''.ckpt'''  )][0]
									__a			:    Tuple      							=	os.path.join(args.output_dir						,      _UpperCAmelCase  )
									__a			:    int      							=	torch.load(_UpperCAmelCase						,      map_location='''cpu'''  )
									__a			:    Optional[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:
													__a			:    Optional[int]      							=	{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 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




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

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

													__a			:    List[Any]      							=	distill_main(_UpperCAmelCase  )

									# Check metrics
									__a			:    Union[str, Any]      							=	load_json(model.metrics_save_path  )
									__a			:    Union[str, Any]      							=	metrics['''val'''][0]
									__a			:    Tuple      							=	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}"""]						,      _UpperCAmelCase  )

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


import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text





def      A__					(			__A  :						List[str]			)					->str:
      __A    =[]
      for line in lines:
            __A    =re.sub(r'''#.*'''     ,				''''''     ,				__A			)  # remove comments
            if line:
                  filtered_lines.append(__A			)
      __A    ='''\n'''.join(__A			)

      # Make a hash from all this code
      __A    =full_str.encode('''utf-8'''			)
      return shaaaa(__A			).hexdigest()


# get importable module names and hash for caching
_lowerCamelCase      :					Dict					    =      {
    '''csv''': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    '''json''': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    '''pandas''': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    '''parquet''': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    '''arrow''': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    '''text''': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    '''imagefolder''': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    '''audiofolder''': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_lowerCamelCase      :					int					    =      {
    '''.csv''': ('''csv''', {}),
    '''.tsv''': ('''csv''', {'''sep''': '''\t'''}),
    '''.json''': ('''json''', {}),
    '''.jsonl''': ('''json''', {}),
    '''.parquet''': ('''parquet''', {}),
    '''.arrow''': ('''arrow''', {}),
    '''.txt''': ('''text''', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_lowerCamelCase      :					List[str]					    =      {'''imagefolder''', '''audiofolder'''}

# Used to filter data files based on extensions given a module name
_lowerCamelCase      :					Dict[str, List[str]]					    =      {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
   _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('''.zip''')


_MODULE_TO_EXTENSIONS["audiofolder"].append('''.zip''')


