



"""simple docstring"""




def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list       )						->     bool:





					'''simple docstring'''

					if not isinstance(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
										raise ValueError("Input series is not valid, valid series - [2, 4, 8]"       )
					if len(SCREAMING_SNAKE_CASE__       ) == 0:
										raise ValueError("Input list must be a non empty list"       )
					if len(SCREAMING_SNAKE_CASE__       ) == 1:
										return True
					try:
										_UpperCAmelCase    :			Any              =					series[1] / series[0]
										for index in range(len(SCREAMING_SNAKE_CASE__       ) - 1       ):
															if series[index + 1] / series[index] != common_ratio:
																				return False
					except ZeroDivisionError:
										return False
					return True

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list       )						->     float:





					'''simple docstring'''

					if not isinstance(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
										raise ValueError("Input series is not valid, valid series - [2, 4, 8]"       )
					if len(SCREAMING_SNAKE_CASE__       ) == 0:
										raise ValueError("Input list must be a non empty list"       )
					_UpperCAmelCase    :			Dict              =					1
					for value in series:
										answer *= value
					return pow(SCREAMING_SNAKE_CASE__		,						1 / len(SCREAMING_SNAKE_CASE__       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





"""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


_lowerCAmelCase       :      Optional[Any]							=							torch.cuda.is_available()
_lowerCAmelCase       :      str							=							[" Sam ate lunch today.", "Sams lunch ingredients."]
_lowerCAmelCase       :      Dict							=							["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_lowerCAmelCase       :      Union[str, 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 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Path		,						SCREAMING_SNAKE_CASE__			:		list       )						->     Dict:





     '''simple docstring'''

     _UpperCAmelCase    :			Tuple              =					"\n".join(SCREAMING_SNAKE_CASE__       )
     Path(SCREAMING_SNAKE_CASE__       ).open("w"       ).writelines(SCREAMING_SNAKE_CASE__       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]       )						->     Union[str, Any]:





     '''simple docstring'''

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






class 			UpperCAmelCase_						(       _UpperCamelCase		):



      @classmethod
      def 		snake_case_      (  cls  :			Optional[int]		):
           return cls



      @require_torch_multi_gpu
      def 		snake_case_      (  self  :			Union[str, Any]		):
           _UpperCAmelCase    :			Dict              =					{
               "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 		snake_case_      (  self  :			List[str]  ,			A  :			List[Any]  ,			A  :			Optional[Any]=True		):
           _UpperCAmelCase    :			int              =					{
               "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		)
           _UpperCAmelCase    :			dict              =					CHEAP_ARGS.copy()
           _UpperCAmelCase    :			Union[str, Any]              =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
           _UpperCAmelCase    :			List[Any]              =					self.get_auto_remove_tmp_dir()
           args_d.update(data_dir=A  ,			output_dir=A  ,			**A		)

           def convert(A  :			List[Any]  ,			A  :			Optional[int]		):
                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}'

           _UpperCAmelCase    :			Tuple              =					[x for x in (convert(A  ,			A		) for k, v in args_d.items()) if len(A		)]
           _UpperCAmelCase    :			Union[str, Any]              =					[sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
           execute_subprocess_async(A  ,			env=self.get_env()		)

           _UpperCAmelCase    :			int              =					os.listdir(A		)
           _UpperCAmelCase    :			List[Any]              =					{os.path.basename(A		) for p in contents}
           _UpperCAmelCase    :			List[Any]              =					[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)
           _UpperCAmelCase    :			List[Any]              =					os.path.join(A  ,			"metrics.json"		)
           _UpperCAmelCase    :			str              =					"rouge2"

           _UpperCAmelCase    :			List[Any]              =					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		)
           _UpperCAmelCase    :			List[str]              =					metrics["val"][-1]
           self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]  ,			0.01		)
           self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}']  ,			A		)
           self.assertEqual(len(metrics["test"]		)  ,			1		)
           _UpperCAmelCase    :			Dict              =					int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1		)
           self.assertEqual(len(metrics["val"]		)  ,			A		)
