





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


_SCREAMING_SNAKE_CASE   						=		torch.cuda.is_available()
_SCREAMING_SNAKE_CASE   						=		[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_SCREAMING_SNAKE_CASE   						=		["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_SCREAMING_SNAKE_CASE   						=		{
    """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 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	snake_case_		:      Optional[Any]           =						'\n'.join(__a )
	Path(__a ).open('w' ).writelines(__a )


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	for split in ["train", "val", "test"]:
		_dump_articles(os.path.join(__a ,			f"""{split}.source""" ) ,			__a )
		_dump_articles(os.path.join(__a ,			f"""{split}.target""" ) ,			__a )
	return tmp_dir


class SCREAMING_SNAKE_CASE_    (       snake_case_       ):






						@classmethod
						def UpperCAmelCase_					(   cls   :   Optional[Any]						)				->     List[Any]:







							"""simple docstring"""


							return cls






						@require_torch_multi_gpu
						def UpperCAmelCase_					(   self   :   Any						)				->     Dict:







							"""simple docstring"""


							snake_case_		:      Optional[int]           =						{
							    '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 UpperCAmelCase_					(   self   :   Optional[Any]		, _A   :   Union[str, Any]		, _A   :   Any=True						)				->     Dict:







							"""simple docstring"""


							snake_case_		:      Tuple           =						{
							    '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						)
							snake_case_		:      dict           =						CHEAP_ARGS.copy()
							snake_case_		:      Optional[Any]           =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
							snake_case_		:      Union[str, Any]           =						self.get_auto_remove_tmp_dir()
							args_d.update(data_dir=_A		, output_dir=_A		, **_A						)

							def convert(_A   :   Dict		, _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}"""

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

							snake_case_		:      Optional[int]           =						os.listdir(_A						)
							snake_case_		:      Dict           =						{os.path.basename(_A						) for p in contents}
							snake_case_		:      Dict           =						[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)
							snake_case_		:      List[Any]           =						os.path.join(_A		, 'metrics.json'						)
							snake_case_		:      str           =						'rouge2'

							snake_case_		:      str           =						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						)
							snake_case_		:      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						)
							snake_case_		:      List[str]           =						int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1						)
							self.assertEqual(len(metrics['val']						)		, _A						)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase										=					logging.get_logger(__name__)

_UpperCAmelCase										=					{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}



class 				_UpperCamelCase       (						lowerCAmelCase_  ):
       _UpperCamelCase					:       Union[str, Any]       = '''qdqbert'''

       def __init__(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Any=30522       , _SCREAMING_SNAKE_CASE:			List[str]=768       , _SCREAMING_SNAKE_CASE:			int=12       , _SCREAMING_SNAKE_CASE:			List[Any]=12       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=3072       , _SCREAMING_SNAKE_CASE:			List[str]="gelu"       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Dict=512       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=2       , _SCREAMING_SNAKE_CASE:			int=0.02       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=1e-12       , _SCREAMING_SNAKE_CASE:			str=True       , _SCREAMING_SNAKE_CASE:			Any=1       , _SCREAMING_SNAKE_CASE:			int=0       , _SCREAMING_SNAKE_CASE:			Tuple=2       , **_SCREAMING_SNAKE_CASE:			List[str]       , )							->			Tuple:




              """simple docstring"""


              super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE       , bos_token_id=_SCREAMING_SNAKE_CASE       , eos_token_id=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	vocab_size
              UpperCamelCase_ 							=	max_position_embeddings
              UpperCamelCase_ 							=	hidden_size
              UpperCamelCase_ 							=	num_hidden_layers
              UpperCamelCase_ 							=	num_attention_heads
              UpperCamelCase_ 							=	intermediate_size
              UpperCamelCase_ 							=	hidden_act
              UpperCamelCase_ 							=	hidden_dropout_prob
              UpperCamelCase_ 							=	attention_probs_dropout_prob
              UpperCamelCase_ 							=	initializer_range
              UpperCamelCase_ 							=	type_vocab_size
              UpperCamelCase_ 							=	layer_norm_eps
              UpperCamelCase_ 							=	use_cache

