






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						= torch.cuda.is_available()
__lowerCamelCase						= [' Sam ate lunch today.', 'Sams lunch ingredients.']
__lowerCamelCase						= ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
__lowerCamelCase						= {
    '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': 42,
    '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': 12,
    'max_target_length': 12,
    'val_max_target_length': 12,
    'test_max_target_length': 12,
    '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    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		Dict:







							"""simple docstring"""





							_a  :    int			=						'''\n'''.join(UpperCAmelCase     )
							Path(UpperCAmelCase     ).open('''w'''     ).writelines(UpperCAmelCase     )






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		Optional[int]:







							"""simple docstring"""





							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_							(				UpperCamelCase       ):




						@classmethod
						def      snake_case__(    cls       )			->						Union[str, Any]:
													return cls




						@require_torch_multi_gpu
						def      snake_case__(    self       )			->						str:
													_a  :    Any			=						{
													    '''no_teacher''': True,
													    '''freeze_encoder''': True,
													    '''gpus''': 2,
													    '''overwrite_output_dir''': True,
													    '''sortish_sampler''': True,
													}
													self._test_distiller_cli_fork(lowercase		,	check_contents=lowercase       )







						def      snake_case__(    self		,	lowercase		,	lowercase=True       )			->						Optional[int]:
													_a  :    Dict			=						{
													    '''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(lowercase       )
													_a  :    dict			=						CHEAP_ARGS.copy()
													_a  :    str			=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
													_a  :    Any			=						self.get_auto_remove_tmp_dir()
													args_d.update(data_dir=lowercase		,	output_dir=lowercase		,	**lowercase       )

													def convert(lowercase		,	lowercase       ):
																				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}'

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

													_a  :    List[Any]			=						os.listdir(lowercase       )
													_a  :    Dict			=						{os.path.basename(lowercase       ) for p in contents}
													_a  :    List[Any]			=						[p for p in contents if p.endswith('''ckpt'''       )]
													assert len(lowercase       ) > 0

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

													# get the following from the module, (we don't have access to `model` here)
													_a  :    List[Any]			=						os.path.join(lowercase		,	'''metrics.json'''       )
													_a  :    str			=						'''rouge2'''

													_a  :    str			=						load_json(lowercase       )
													# {'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(lowercase       )
													_a  :    Tuple			=						metrics['''val'''][-1]
													self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']		,	0.01       )
													self.assertIsInstance(last_step_stats[F'val_avg_{val_metric}']		,	lowercase       )
													self.assertEqual(len(metrics['''test''']       )		,	1       )
													_a  :    Dict			=						int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1       )
													self.assertEqual(len(metrics['''val''']       )		,	lowercase       )







import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
						import librosa  # noqa: E402

						__lowerCamelCase						= True
						__lowerCamelCase						= ''
except Exception as e:
						__lowerCamelCase						= False
						__lowerCamelCase						= (
						    F'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
						)


from PIL import Image  # noqa: E402

class      UpperCamelCase_							(				UpperCamelCase ,       UpperCamelCase       ):
						lowercase									=  '''mel_config.json'''




						@register_to_config
						def __init__(    self		,	lowercase = 256		,	lowercase = 256		,	lowercase = 22_050		,	lowercase = 2_048		,	lowercase = 512		,	lowercase = 80		,	lowercase = 32		,	)			->						Optional[Any]:
													_a  :    Tuple			=						hop_length
													_a  :    Dict			=						sample_rate
													_a  :    Dict			=						n_fft
													_a  :    List[str]			=						top_db
													_a  :    Optional[int]			=						n_iter
													self.set_resolution(lowercase		,	lowercase       )
													_a  :    Optional[int]			=						None

													if not _librosa_can_be_imported:
																				raise ValueError(_import_error       )




						def      snake_case__(    self		,	lowercase		,	lowercase       )			->						Any:
													_a  :    Any			=						x_res
													_a  :    Optional[Any]			=						y_res
													_a  :    List[Any]			=						self.y_res
													_a  :    Tuple			=						self.x_res * self.hop_length - 1




						def      snake_case__(    self		,	lowercase = None		,	lowercase = None       )			->						Union[str, Any]:
													if audio_file is not None:
																				_a		,					_a  :    str			=						librosa.load(lowercase		,	mono=lowercase		,	sr=self.sr       )
													else:
																				_a  :    Any			=						raw_audio

													# Pad with silence if necessary.
													if len(self.audio       ) < self.x_res * self.hop_length:
																				_a  :    str			=						np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio       ),)       )]       )




						def      snake_case__(    self       )			->						int:
													return len(self.audio       ) // self.slice_size




						def      snake_case__(    self		,	lowercase = 0       )			->						np.ndarray:
													return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]




						def      snake_case__(    self       )			->						int:
													return self.sr




						def      snake_case__(    self		,	lowercase       )			->						Image.Image:
													_a  :    int			=						librosa.feature.melspectrogram(
													    y=self.get_audio_slice(lowercase       )		,	sr=self.sr		,	n_fft=self.n_fft		,	hop_length=self.hop_length		,	n_mels=self.n_mels       )
													_a  :    List[str]			=						librosa.power_to_db(lowercase		,	ref=np.max		,	top_db=self.top_db       )
													_a  :    List[str]			=						(((log_S + self.top_db) * 255 / self.top_db).clip(0		,	255       ) + 0.5).astype(np.uinta       )
													_a  :    Any			=						Image.fromarray(lowercase       )
													return image







						def      snake_case__(    self		,	lowercase       )			->						np.ndarray:
													_a  :    Any			=						np.frombuffer(image.tobytes()		,	dtype='''uint8'''       ).reshape((image.height, image.width)       )
													_a  :    Any			=						bytedata.astype('''float'''       ) * self.top_db / 255 - self.top_db
													_a  :    Dict			=						librosa.db_to_power(lowercase       )
													_a  :    List[str]			=						librosa.feature.inverse.mel_to_audio(
													    lowercase		,	sr=self.sr		,	n_fft=self.n_fft		,	hop_length=self.hop_length		,	n_iter=self.n_iter       )
													return audio