
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 :       str     					=    torch.cuda.is_available()
_lowerCamelCase :       Optional[Any]     					=    [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
_lowerCamelCase :       Union[str, Any]     					=    ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
_lowerCamelCase :       List[str]     					=    {
    '''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  a_	( __lowercase     :			Path ,      __lowercase     :			list				)				->						List[str]:
			_snake_case						=    '\n'.join(__lowercase				)
			Path(__lowercase				).open('w'				).writelines(__lowercase				)






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




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	@classmethod
	def 				A							(						cls			:					int			):


				'''simple docstring'''


				return cls



	@require_torch_multi_gpu
	def 				A							(						self			:					Optional[int]			):


				'''simple docstring'''


				_snake_case						=    {
				    '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 				A							(						self			:					Dict    ,						lowercase			:					List[Any]    ,						lowercase			:					int=True			):


				'''simple docstring'''


				_snake_case						=    {
				    '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			)
				_snake_case						=    CHEAP_ARGS.copy()
				_snake_case						=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
				_snake_case						=    self.get_auto_remove_tmp_dir()
				args_d.update(data_dir=lowercase    ,						output_dir=lowercase    ,						**lowercase			)

				def convert(lowercase			:					str    ,						lowercase			:					Union[str, Any]			):
							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}'''

				_snake_case						=    [x for x in (convert(lowercase    ,						lowercase			) for k, v in args_d.items()) if len(lowercase			)]
				_snake_case						=    [sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
				execute_subprocess_async(lowercase    ,						env=self.get_env()			)

				_snake_case						=    os.listdir(lowercase			)
				_snake_case						=    {os.path.basename(lowercase			) for p in contents}
				_snake_case						=    [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)
				_snake_case						=    os.path.join(lowercase    ,						'metrics.json'			)
				_snake_case						=    'rouge2'

				_snake_case						=    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			)
				_snake_case						=    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			)
				_snake_case						=    int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1			)
				self.assertEqual(len(metrics['val']			)    ,						lowercase			)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_lowerCamelCase :       Optional[int]     					=    {}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       List[str]     					=    ['''MBart50Tokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       Optional[int]     					=    ['''MBart50TokenizerFast''']


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_mbartaa import MBartaaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
			import sys

			_lowerCamelCase :       int     					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)