

"""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-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     _A    (    UpperCamelCase_	:			Tuple,     UpperCamelCase_	:			Dict)      ->		Tuple:
   '''simple docstring'''


   __lowercase								=		"\n".join(UpperCAmelCase_)
   Path(UpperCAmelCase_).open("w").writelines(UpperCAmelCase_)




def     _A    (    UpperCamelCase_	:			Union[str, Any])      ->		Optional[int]:
   '''simple docstring'''


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




class    _lowerCAmelCase    (      lowercase__  ):







 """simple docstring"""







 @classmethod
 def    _lowercase					(     cls				:     str      ):
    return cls


 @require_torch_multi_gpu
 def    _lowercase					(     self				:     Any      ):
    __lowercase								=		{
        "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    _lowercase					(     self				:     Any,   UpperCAmelCase__				:     Any,   UpperCAmelCase__				:     int=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(UpperCAmelCase__      )
    __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=UpperCAmelCase__,   output_dir=UpperCAmelCase__,   **UpperCAmelCase__      )

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

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

    __lowercase								=		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__      )
    __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}"""],   UpperCAmelCase__      )
    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"]      ),   UpperCAmelCase__      )










from itertools import count
from math import asin, pi, sqrt
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    return (
        (1 - 2 * point) * sqrt(point - point**2		) + 2 * point + asin(sqrt(1 - point		)		)
    ) / 4
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    a__  			=							(circles_number + 1 - sqrt(2 * circles_number		)) / (
        2 * (circles_number**2 + 1)
    )
    a__  			=							circles_number * intersection_y

    a__  			=							intersection_x * intersection_y / 2
    a__  			=							circle_bottom_arc_integral(
        1 / 2		) - circle_bottom_arc_integral(UpperCAmelCase_		)

    return triangle_area + concave_region_area
def 							__lowerCAmelCase			(UpperCAmelCase_ = 1 / 1_000		):
    a__  			=							(1 - pi / 4) / 4

    for n in count(1		):
        if concave_triangle_area(UpperCAmelCase_		) / l_section_area < fraction:
            return n

    return -1


if __name__ == "__main__":
 print(F"{solution() = }")






