
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__					:       int		=       torch.cuda.is_available()
lowerCAmelCase__					:       Union[str, Any]		=       [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
lowerCAmelCase__					:       Optional[int]		=       ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
lowerCAmelCase__					:       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""": 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__						(    A__      ,			A__   )       -> List[str]:
				snake_case__ :     Dict 							=				"\n".join(_UpperCAmelCase   )
				Path(_UpperCAmelCase   ).open('w'   ).writelines(_UpperCAmelCase   )
def 			UpperCamelCase__						(    A__   )       -> Optional[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 			__snake_case  (      snake_case__     ):




					@classmethod
					def       __a      (       cls		)					->  Any:



									'''simple docstring'''







									return cls




					@require_torch_multi_gpu
					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									snake_case__ :     str 							=				{
									    "no_teacher": True,
									    "freeze_encoder": True,
									    "gpus": 2,
									    "overwrite_output_dir": True,
									    "sortish_sampler": True,
									}
									self._test_distiller_cli_fork(__UpperCamelCase   ,	check_contents=__UpperCamelCase		)




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase=True		)					->  Any:



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				{
									    "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(__UpperCamelCase		)
									snake_case__ :     dict 							=				CHEAP_ARGS.copy()
									snake_case__ :     int 							=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
									snake_case__ :     Any 							=				self.get_auto_remove_tmp_dir()
									args_d.update(data_dir=__UpperCamelCase   ,	output_dir=__UpperCamelCase   ,	**__UpperCamelCase		)

									def convert(__UpperCamelCase   ,	__UpperCamelCase		):
													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__ :     str 							=				[x for x in (convert(__UpperCamelCase   ,	__UpperCamelCase		) for k, v in args_d.items()) if len(__UpperCamelCase		)]
									snake_case__ :     Dict 							=				[sys.executable, F"""{self.test_file_dir}/distillation.py"""] + cli_args
									execute_subprocess_async(__UpperCamelCase   ,	env=self.get_env()		)

									snake_case__ :     Optional[int] 							=				os.listdir(__UpperCamelCase		)
									snake_case__ :     List[Any] 							=				{os.path.basename(__UpperCamelCase		) for p in contents}
									snake_case__ :     Optional[Any] 							=				[p for p in contents if p.endswith('ckpt'		)]
									assert len(__UpperCamelCase		) > 0

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

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

									snake_case__ :     Tuple 							=				load_json(__UpperCamelCase		)
									# {'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(__UpperCamelCase		)
									snake_case__ :     int 							=				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}"""]   ,	__UpperCamelCase		)
									self.assertEqual(len(metrics['test']		)   ,	1		)
									snake_case__ :     str 							=				int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1		)
									self.assertEqual(len(metrics['val']		)   ,	__UpperCamelCase		)



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__					:       Tuple		=       ['''MBart50Tokenizer''']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Tuple		=       ['''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__					:       Optional[int]		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


