

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


lowercase     :       str        =       torch.cuda.is_available()
lowercase     :       str        =       [" Sam ate lunch today.", "Sams lunch ingredients."]
lowercase     :       Union[str, Any]        =       ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
lowercase     :       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_   (_lowerCAmelCase :		Path     ,			_lowerCAmelCase :		list		):
			__UpperCamelCase    :    str								=      "\n".join(_lowerCAmelCase		)
			Path(_lowerCAmelCase		).open("w"		).writelines(_lowerCAmelCase		)







def  UpperCAmelCase_   (_lowerCAmelCase :		List[str]		):
			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(_lowerCAmelCase     ,			F'''{split}.source'''		)     ,			_lowerCAmelCase		)
						_dump_articles(os.path.join(_lowerCAmelCase     ,			F'''{split}.target'''		)     ,			_lowerCAmelCase		)
			return tmp_dir

class 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




						@classmethod
						def __lowerCamelCase					(				cls    )							->	Dict:




									'''simple docstring'''


									return cls

						@require_torch_multi_gpu
						def __lowerCamelCase					(				self    )							->	Any:




									'''simple docstring'''


									__UpperCamelCase    :    Dict								=      {
									    "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 __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase=True    )							->	List[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    List[str]								=      {
									    "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    )
									__UpperCamelCase    :    dict								=      CHEAP_ARGS.copy()
									__UpperCamelCase    :    Dict								=      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=__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}'''

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

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

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


lowercase     :       dict[str, float]        =       {
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0009842073,
    "short-ton": 0.0011023122,
    "pound": 2.2046244202,
    "stone": 0.1574731728,
    "ounce": 35.273990723,
    "carrat": 5000,
    "atomic-mass-unit": 6.0_22_13_66_52E26,
}

lowercase     :       dict[str, float]        =       {
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1016.04608,
    "short-ton": 907.184,
    "pound": 0.453592,
    "stone": 6.35029,
    "ounce": 0.0283495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.6_60_54_01_99E-27,
}







def  UpperCAmelCase_   (_lowerCAmelCase :		str     ,			_lowerCAmelCase :		str     ,			_lowerCAmelCase :		float		):
			if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
						__UpperCamelCase    :    Dict								=      (
						    F'''Invalid \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
						    F'''Supported values are: {", ".join(_lowerCAmelCase		)}'''
						)
						raise ValueError(_lowerCAmelCase		)
			return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
					import doctest

					doctest.testmod()