






"""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__			=   torch.cuda.is_available()
lowerCamelCase__			=   [" Sam ate lunch today.", "Sams lunch ingredients."]
lowerCamelCase__			=   ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
lowerCamelCase__			=   {
    "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 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Path		,				UpperCamelCase     :	list    ):
  A__											=							"""\n""".join(UpperCamelCase    )
  Path(UpperCamelCase    ).open("""w"""    ).writelines(UpperCamelCase    )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Any    ):
  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 					_UpperCamelCase		( __snake_case):






    @classmethod
    def  A    (cls   ):


      """simple docstring"""
      return cls






    @require_torch_multi_gpu
    def  A    (self   ):


      """simple docstring"""
      A__											=							{
          """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   ):


      """simple docstring"""
      A__											=							{
          """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__   )
      A__											=							CHEAP_ARGS.copy()
      A__											=							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
      A__											=							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}"""

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

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

      A__											=							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__   )
      A__											=							metrics["""val"""][-1]
      self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]						,      0.0_1   )
      self.assertIsInstance(last_step_stats[F"""val_avg_{val_metric}"""]						,      lowerCamelCase__   )
      self.assertEqual(len(metrics["""test"""]   )						,      1   )
      A__											=							int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1   )
      self.assertEqual(len(metrics["""val"""]   )						,      lowerCamelCase__   )












"""simple docstring"""




def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Dict    ):
  A__											=							len(UpperCamelCase    )
  for _ in range(
      0		,				int(((no_of_elements - 1) / 2) + 1    )    ):  # we don't need to traverse to end of list as
    for j in range(0		,				no_of_elements - 1    ):
      if (
          lst[j + 1] < lst[j]
      ):  # applying bubble sort algorithm from left to right (or forwards)
        A__											=							lst[j + 1]
        A__											=							lst[j]
        A__											=							temp
      if (
          lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
      ):  # applying bubble sort algorithm from right to left (or backwards)
        A__											=							lst[no_of_elements - 1 - j]
        A__											=							lst[no_of_elements - 2 - j]
        A__											=							temp
  return lst


if __name__ == "__main__":
      print("enter the list to be sorted")
      lowerCamelCase__			=   [int(x) for x in input().split()]  # inputing elements of the list in one line
      lowerCamelCase__			=   double_sort(lst)
      print("the sorted list is")
      print(sorted_lst)




