

import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


_lowerCamelCase       :     Optional[Any]           =							torch.cuda.is_available()
_lowerCamelCase       :     Union[str, Any]           =							[" Sam ate lunch today.", "Sams lunch ingredients."]
_lowerCamelCase       :     List[Any]           =							["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_lowerCamelCase       :     Union[str, Any]           =							{
    "max_tokens_per_batch": None,
    "supervise_forward": True,
    "normalize_hidden": True,
    "label_smoothing": 0.2,
    "eval_max_gen_length": None,
    "eval_beams": 1,
    "val_metric": "loss",
    "save_top_k": 1,
    "adafactor": True,
    "early_stopping_patience": 2,
    "logger_name": "default",
    "length_penalty": 0.5,
    "cache_dir": "",
    "task": "summarization",
    "num_workers": 2,
    "alpha_hid": 0,
    "freeze_embeds": True,
    "enc_only": False,
    "tgt_suffix": "",
    "resume_from_checkpoint": None,
    "sortish_sampler": True,
    "student_decoder_layers": 1,
    "val_check_interval": 1.0,
    "output_dir": "",
    "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    "no_teacher": False,
    "fp16_opt_level": "O1",
    "gpus": 1 if CUDA_AVAILABLE else 0,
    "n_tpu_cores": 0,
    "max_grad_norm": 1.0,
    "do_train": True,
    "do_predict": True,
    "accumulate_grad_batches": 1,
    "server_ip": "",
    "server_port": "",
    "seed": 4_2,
    "model_name_or_path": "sshleifer/bart-tiny-random",
    "config_name": "",
    "tokenizer_name": "facebook/bart-large",
    "do_lower_case": False,
    "learning_rate": 0.3,
    "lr_scheduler": "linear",
    "weight_decay": 0.0,
    "adam_epsilon": 1E-08,
    "warmup_steps": 0,
    "max_epochs": 1,
    "train_batch_size": 2,
    "eval_batch_size": 2,
    "max_source_length": 1_2,
    "max_target_length": 1_2,
    "val_max_target_length": 1_2,
    "test_max_target_length": 1_2,
    "fast_dev_run": False,
    "no_cache": False,
    "n_train": -1,
    "n_val": -1,
    "n_test": -1,
    "student_encoder_layers": 1,
    "freeze_encoder": False,
    "auto_scale_batch_size": False,
    "overwrite_output_dir": False,
    "student": None,
}

def     a__  (	UpperCAmelCase							:    Path       ,				UpperCAmelCase							:    list    )						->		List[str]:
  UpperCAmelCase :							Any								=				'''\n'''.join(UpperCAmelCase    )
  Path(UpperCAmelCase    ).open('''w'''    ).writelines(UpperCAmelCase    )

def     a__  (	UpperCAmelCase							:    Optional[int]    )						->		int:
  for split in ["train", "val", "test"]:
    _dump_articles(os.path.join(UpperCAmelCase       ,				f'''{split}.source'''    )       ,				UpperCAmelCase    )
    _dump_articles(os.path.join(UpperCAmelCase       ,				f'''{split}.target'''    )       ,				UpperCAmelCase    )
  return tmp_dir






class       __UpperCAmelCase  ( lowerCamelCase__		):



   @classmethod
   def  __magic_name__					(							cls							:  int		):
     return cls



   @require_torch_multi_gpu
   def  __magic_name__					(							self							:  Optional[int]		):
     UpperCAmelCase :							Dict								=				{
         '''no_teacher''': True,
         '''freeze_encoder''': True,
         '''gpus''': 2,
         '''overwrite_output_dir''': True,
         '''sortish_sampler''': True,
     }
     self._test_distiller_cli_fork(__A,	check_contents=__A		)





   def  __magic_name__					(							self							:  Union[str, Any],	__A							:  str,	__A							:  Tuple=True		):
     UpperCAmelCase :							str								=				{
         '''label_smoothing''': 0.0,
         '''early_stopping_patience''': -1,
         '''train_batch_size''': 1,
         '''eval_batch_size''': 2,
         '''max_epochs''': 2,
         '''alpha_mlm''': 0.2,
         '''alpha_ce''': 0.8,
         '''do_predict''': True,
         '''model_name_or_path''': '''sshleifer/tinier_bart''',
         '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
         '''val_check_interval''': 0.5,
     }
     default_updates.update(__A		)
     UpperCAmelCase :							dict								=				CHEAP_ARGS.copy()
     UpperCAmelCase :							Tuple								=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
     UpperCAmelCase :							Dict								=				self.get_auto_remove_tmp_dir()
     args_d.update(data_dir=__A,	output_dir=__A,	**__A		)

     def convert(__A							:  Tuple,	__A							:  Tuple		):
       if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
         return ""
       if v is False or v is None:
         return ""
       if v is True:  # or len(str(v))==0:
         return F'''--{k}'''
       return F'''--{k}={v}'''

     UpperCAmelCase :							Any								=				[x for x in (convert(__A,	__A		) for k, v in args_d.items()) if len(__A		)]
     UpperCAmelCase :							Optional[Any]								=				[sys.executable, F'''{self.test_file_dir}/distillation.py'''] + cli_args
     execute_subprocess_async(__A,	env=self.get_env()		)

     UpperCAmelCase :							List[Any]								=				os.listdir(__A		)
     UpperCAmelCase :							Optional[Any]								=				{os.path.basename(__A		) for p in contents}
     UpperCAmelCase :							Union[str, Any]								=				[p for p in contents if p.endswith('''ckpt'''		)]
     assert len(__A		) > 0

     self.assertIn('''test_generations.txt''',	__A		)
     self.assertIn('''test_results.txt''',	__A		)

     # get the following from the module, (we don't have access to `model` here)
     UpperCAmelCase :							Dict								=				os.path.join(__A,	'''metrics.json'''		)
     UpperCAmelCase :							Any								=				'''rouge2'''

     UpperCAmelCase :							Optional[int]								=				load_json(__A		)
     # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
     print(__A		)
     UpperCAmelCase :							Union[str, Any]								=				metrics['''val'''][-1]
     self.assertGreaterEqual(last_step_stats['''val_avg_gen_time'''],	0.0_1		)
     self.assertIsInstance(last_step_stats[F'''val_avg_{val_metric}'''],	__A		)
     self.assertEqual(len(metrics['''test''']		),	1		)
     UpperCAmelCase :							Dict								=				int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1		)
     self.assertEqual(len(metrics['''val''']		),	__A		)








import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def     a__  (	UpperCAmelCase							:    int       ,				UpperCAmelCase							:    int    )						->		List[str]:
  assert isinstance(UpperCAmelCase       ,				UpperCAmelCase    )
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize('''keep_in_memory'''       ,				[False, True]    )
def     a__  (	UpperCAmelCase							:    Any       ,				UpperCAmelCase							:    List[Any]       ,				UpperCAmelCase							:    Optional[Any]    )						->		Optional[int]:
  UpperCAmelCase :							Optional[int]								=				tmp_path / '''cache'''
  UpperCAmelCase :							str								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    UpperCAmelCase :							Union[str, Any]								=				CsvDatasetReader(UpperCAmelCase       ,				cache_dir=UpperCAmelCase       ,				keep_in_memory=UpperCAmelCase    ).read()
  _check_csv_dataset(UpperCAmelCase       ,				UpperCAmelCase    )

@pytest.mark.parametrize(
    '''features'''       ,				[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]       ,				)
def     a__  (	UpperCAmelCase							:    Optional[int]       ,				UpperCAmelCase							:    int       ,				UpperCAmelCase							:    int    )						->		Optional[Any]:
  UpperCAmelCase :							List[str]								=				tmp_path / '''cache'''
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  UpperCAmelCase :							Optional[Any]								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							List[Any]								=				features.copy() if features else default_expected_features
  UpperCAmelCase :							List[Any]								=				(
      Features({feature: Value(UpperCAmelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  UpperCAmelCase :							Any								=				CsvDatasetReader(UpperCAmelCase       ,				features=UpperCAmelCase       ,				cache_dir=UpperCAmelCase    ).read()
  _check_csv_dataset(UpperCAmelCase       ,				UpperCAmelCase    )

@pytest.mark.parametrize('''split'''       ,				[None, NamedSplit('''train'''    ), '''train''', '''test''']    )
def     a__  (	UpperCAmelCase							:    Union[str, Any]       ,				UpperCAmelCase							:    Tuple       ,				UpperCAmelCase							:    str    )						->		Any:
  UpperCAmelCase :							List[str]								=				tmp_path / '''cache'''
  UpperCAmelCase :							Any								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							int								=				CsvDatasetReader(UpperCAmelCase       ,				cache_dir=UpperCAmelCase       ,				split=UpperCAmelCase    ).read()
  _check_csv_dataset(UpperCAmelCase       ,				UpperCAmelCase    )
  assert dataset.split == split if split else "train"

@pytest.mark.parametrize('''path_type'''       ,				[str, list]    )
def     a__  (	UpperCAmelCase							:    int       ,				UpperCAmelCase							:    List[str]       ,				UpperCAmelCase							:    Any    )						->		str:
  if issubclass(UpperCAmelCase       ,				UpperCAmelCase    ):
    UpperCAmelCase :							Tuple								=				csv_path
  elif issubclass(UpperCAmelCase       ,				UpperCAmelCase    ):
    UpperCAmelCase :							List[str]								=				[csv_path]
  UpperCAmelCase :							Tuple								=				tmp_path / '''cache'''
  UpperCAmelCase :							str								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							Tuple								=				CsvDatasetReader(UpperCAmelCase       ,				cache_dir=UpperCAmelCase    ).read()
  _check_csv_dataset(UpperCAmelCase       ,				UpperCAmelCase    )

def     a__  (	UpperCAmelCase							:    str       ,				UpperCAmelCase							:    Any       ,				UpperCAmelCase							:    int=("train",)    )						->		Optional[int]:
  assert isinstance(UpperCAmelCase       ,				UpperCAmelCase    )
  for split in splits:
    UpperCAmelCase :							Optional[Any]								=				dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize('''keep_in_memory'''       ,				[False, True]    )
def     a__  (	UpperCAmelCase							:    Tuple       ,				UpperCAmelCase							:    List[Any]       ,				UpperCAmelCase							:    Tuple    )						->		Tuple:
  UpperCAmelCase :							Any								=				tmp_path / '''cache'''
  UpperCAmelCase :							Dict								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    UpperCAmelCase :							str								=				CsvDatasetReader({'''train''': csv_path}       ,				cache_dir=UpperCAmelCase       ,				keep_in_memory=UpperCAmelCase    ).read()
  _check_csv_datasetdict(UpperCAmelCase       ,				UpperCAmelCase    )

@pytest.mark.parametrize(
    '''features'''       ,				[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]       ,				)
def     a__  (	UpperCAmelCase							:    int       ,				UpperCAmelCase							:    Tuple       ,				UpperCAmelCase							:    Any    )						->		Optional[int]:
  UpperCAmelCase :							Tuple								=				tmp_path / '''cache'''
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  UpperCAmelCase :							List[str]								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							Union[str, Any]								=				features.copy() if features else default_expected_features
  UpperCAmelCase :							List[str]								=				(
      Features({feature: Value(UpperCAmelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  UpperCAmelCase :							List[Any]								=				CsvDatasetReader({'''train''': csv_path}       ,				features=UpperCAmelCase       ,				cache_dir=UpperCAmelCase    ).read()
  _check_csv_datasetdict(UpperCAmelCase       ,				UpperCAmelCase    )

@pytest.mark.parametrize('''split'''       ,				[None, NamedSplit('''train'''    ), '''train''', '''test''']    )
def     a__  (	UpperCAmelCase							:    Union[str, Any]       ,				UpperCAmelCase							:    List[Any]       ,				UpperCAmelCase							:    Optional[Any]    )						->		Dict:
  if split:
    UpperCAmelCase :							Union[str, Any]								=				{split: csv_path}
  else:
    UpperCAmelCase :							List[str]								=				{'''train''': csv_path, '''test''': csv_path}
  UpperCAmelCase :							str								=				tmp_path / '''cache'''
  UpperCAmelCase :							Tuple								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							str								=				CsvDatasetReader(UpperCAmelCase       ,				cache_dir=UpperCAmelCase    ).read()
  _check_csv_datasetdict(UpperCAmelCase       ,				UpperCAmelCase       ,				splits=list(path.keys()    )    )
  assert all(dataset[split].split == split for split in path.keys()    )

def     a__  (	UpperCAmelCase							:    Optional[Any]    )						->		int:
  with open(UpperCAmelCase       ,				encoding='''utf-8'''    ) as csvfile:
    yield from csv.reader(UpperCAmelCase    )

def     a__  (	UpperCAmelCase							:    List[str]       ,				UpperCAmelCase							:    Union[str, Any]    )						->		Union[str, Any]:
  UpperCAmelCase :							int								=				tmp_path / '''cache'''
  UpperCAmelCase :							int								=				os.path.join(UpperCAmelCase       ,				'''tmp.csv'''    )
  UpperCAmelCase :							List[str]								=				CsvDatasetReader({'''train''': csv_path}       ,				cache_dir=UpperCAmelCase    ).read()
  CsvDatasetWriter(dataset['''train''']       ,				UpperCAmelCase       ,				num_proc=1    ).write()

  UpperCAmelCase :							List[Any]								=				iter_csv_file(UpperCAmelCase    )
  UpperCAmelCase :							int								=				iter_csv_file(UpperCAmelCase    )

  for rowa, rowa in zip(UpperCAmelCase       ,				UpperCAmelCase    ):
    assert rowa == rowa

def     a__  (	UpperCAmelCase							:    Any       ,				UpperCAmelCase							:    Dict    )						->		Any:
  UpperCAmelCase :							List[Any]								=				tmp_path / '''cache'''
  UpperCAmelCase :							Tuple								=				os.path.join(UpperCAmelCase       ,				'''tmp.csv'''    )
  UpperCAmelCase :							List[str]								=				CsvDatasetReader({'''train''': csv_path}       ,				cache_dir=UpperCAmelCase    ).read()
  CsvDatasetWriter(dataset['''train''']       ,				UpperCAmelCase       ,				num_proc=2    ).write()

  UpperCAmelCase :							List[Any]								=				iter_csv_file(UpperCAmelCase    )
  UpperCAmelCase :							Union[str, Any]								=				iter_csv_file(UpperCAmelCase    )

  for rowa, rowa in zip(UpperCAmelCase       ,				UpperCAmelCase    ):
    assert rowa == rowa






def     a__  (	UpperCAmelCase							:    List[Any]       ,				UpperCAmelCase							:    Optional[int]    )						->		Optional[Any]:
  UpperCAmelCase :							Dict								=				tmp_path / '''cache'''
  UpperCAmelCase :							List[Any]								=				os.path.join(UpperCAmelCase       ,				'''tmp.csv'''    )
  UpperCAmelCase :							List[Any]								=				CsvDatasetReader({'''train''': csv_path}       ,				cache_dir=UpperCAmelCase    ).read()
  with pytest.raises(UpperCAmelCase    ):
    CsvDatasetWriter(dataset['''train''']       ,				UpperCAmelCase       ,				num_proc=0    )





