
'''simple docstring'''



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


__A       =      torch.cuda.is_available()
__A       =      [" Sam ate lunch today.", "Sams lunch ingredients."]
__A       =      ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__A       =      {
    "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-0_8,
    "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__						,						lowercase__		):
						lowercase__								=    """\n""".join(lowercase__		)
						Path(lowercase__		).open("""w"""		).writelines(lowercase__		)





def 	_A						(							lowercase__		):
						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      A							(    __UpperCAmelCase			):



			@classmethod
			def A__	(   cls    )     ->						Any:




									'''simple docstring'''



									return cls



			@require_torch_multi_gpu
			def A__	(   self    )     ->						Any:




									'''simple docstring'''



									lowercase__								=    {
									    """no_teacher""": True,
									    """freeze_encoder""": True,
									    """gpus""": 2,
									    """overwrite_output_dir""": True,
									    """sortish_sampler""": True,
									}
									self._test_distiller_cli_fork(lowerCamelCase__    ,       check_contents=lowerCamelCase__    )



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__=True    )     ->						Optional[Any]:




									'''simple docstring'''



									lowercase__								=    {
									    """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(lowerCamelCase__    )
									lowercase__								=    CHEAP_ARGS.copy()
									lowercase__								=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
									lowercase__								=    self.get_auto_remove_tmp_dir()
									args_d.update(data_dir=lowerCamelCase__    ,       output_dir=lowerCamelCase__    ,       **lowerCamelCase__    )

									def convert(lowerCamelCase__    ,       lowerCamelCase__    ):
															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}'''

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

									lowercase__								=    os.listdir(lowerCamelCase__    )
									lowercase__								=    {os.path.basename(lowerCamelCase__    ) for p in contents}
									lowercase__								=    [p for p in contents if p.endswith("""ckpt"""    )]
									assert len(lowerCamelCase__    ) > 0

									self.assertIn("""test_generations.txt"""    ,       lowerCamelCase__    )
									self.assertIn("""test_results.txt"""    ,       lowerCamelCase__    )

									# get the following from the module, (we don't have access to `model` here)
									lowercase__								=    os.path.join(lowerCamelCase__    ,       """metrics.json"""    )
									lowercase__								=    """rouge2"""

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








'''simple docstring'''



def 	_A						(							lowercase__		):
						if not isinstance(lowercase__						,						lowercase__		):
												raise ValueError("""Input series is not valid, valid series - [1, 2/3, 2]"""		)
						if len(lowercase__		) == 0:
												raise ValueError("""Input list must be a non empty list"""		)
						if len(lowercase__		) == 1 and series[0] != 0:
												return True
						lowercase__								=    []
						lowercase__								=    len(lowercase__		)
						for i in range(0						,						lowercase__		):
												if series[i] == 0:
																		raise ValueError("""Input series cannot have 0 as an element"""		)
												rec_series.append(1 / series[i]		)
						lowercase__								=    rec_series[1] - rec_series[0]
						for index in range(2						,						lowercase__		):
												if rec_series[index] - rec_series[index - 1] != common_diff:
																		return False
						return True





def 	_A						(							lowercase__		):
						if not isinstance(lowercase__						,						lowercase__		):
												raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""		)
						if len(lowercase__		) == 0:
												raise ValueError("""Input list must be a non empty list"""		)
						lowercase__								=    0
						for val in series:
												answer += 1 / val
						return len(lowercase__		) / answer


if __name__ == "__main__":
				import doctest

				doctest.testmod()






