



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_  =   torch.cuda.is_available()
lowercase_  =   [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowercase_  =   ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowercase_  =   {
    '''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 __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_     ):
			lowercase__         = "\n".join(a__     )
			Path(a__     ).open("w"     ).writelines(a__     )



def __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_     ):
			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  _snake_case	(    UpperCamelCase__):




				@classmethod
				def 							A__					(  cls    :  Tuple  ):
							return cls




				@require_torch_multi_gpu
				def 							A__					(  self    :  Dict  ):
							lowercase__         = {
							    "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 							A__					(  self    :  Tuple,    __lowercase    :  List[Any],    __lowercase    :  Any=True  ):
							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(_a  )
							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=_a,    output_dir=_a,    **_a  )

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

							lowercase__         = os.listdir(_a  )
							lowercase__         = {os.path.basename(_a  ) for p in contents}
							lowercase__         = [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)
							lowercase__         = os.path.join(_a,    "metrics.json"  )
							lowercase__         = "rouge2"

							lowercase__         = 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  )
							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}'''],    _a  )
							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"]  ),    _a  )








from __future__ import annotations

from collections.abc import Callable







def   lowerCamelCase(      a__		,a__		,a__		,a__ = 100		,):
				_SCREAMING_SNAKE_CASE	     =x_start
				_SCREAMING_SNAKE_CASE	     =fnc(a__)
				_SCREAMING_SNAKE_CASE	     =0.0

				for _ in range(a__):
								# Approximates small segments of curve as linear and solve
								# for trapezoidal area
								_SCREAMING_SNAKE_CASE	     =(x_end - x_start) / steps + xa
								_SCREAMING_SNAKE_CASE	     =fnc(a__)
								area += abs(fxa + fxa) * (xa - xa) / 2

								# Increment step
								_SCREAMING_SNAKE_CASE	     =xa
								_SCREAMING_SNAKE_CASE	     =fxa
				return area


if __name__ == "__main__":







							def   lowerCamelCase(      a__):
											return x**3

							print('''f(x) = x^3''')
							print('''The area between the curve, x = -10, x = 10 and the x axis is:''')
							snake_case_  :	Any							  =							10
							while i <= 10_00_00:
														snake_case_  :	Tuple							  =							trapezoidal_area(f, -5, 5, i)
														print(f"""with {i} steps: {area}""")
														i *= 10