


"""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


__UpperCAmelCase    				=      torch.cuda.is_available()
__UpperCAmelCase    				=      [" Sam ate lunch today.", "Sams lunch ingredients."]
__UpperCAmelCase    				=      ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__UpperCAmelCase    				=      {
    "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     lowercase__	(  lowerCamelCase     :  Path				,	lowerCamelCase     :  list				)					->     Optional[Any]:
  lowerCAmelCase__    :    Optional[int]								=					"\n".join(lowerCamelCase				)
  Path(lowerCamelCase				).open("w"				).writelines(lowerCamelCase				)





def     lowercase__	(  lowerCamelCase     :  str				)					->     Dict:
  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  lowercase_  ( a_						):







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







      @require_torch_multi_gpu
      def 				_lowerCAmelCase       (      self :							List[str]    ):
        lowerCAmelCase__    :    Any								=					{
            "no_teacher": True,
            "freeze_encoder": True,
            "gpus": 2,
            "overwrite_output_dir": True,
            "sortish_sampler": True,
        }
        self._test_distiller_cli_fork(_lowercase		,  check_contents=_lowercase    )







      def 				_lowerCAmelCase       (      self :							Union[str, Any]		,  _lowercase :							Optional[Any]		,  _lowercase :							Union[str, Any]=True    ):
        lowerCAmelCase__    :    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(_lowercase    )
        lowerCAmelCase__    :    dict								=					CHEAP_ARGS.copy()
        lowerCAmelCase__    :    Tuple								=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
        lowerCAmelCase__    :    Tuple								=					self.get_auto_remove_tmp_dir()
        args_d.update(data_dir=_lowercase		,  output_dir=_lowercase		,  **_lowercase    )

        def convert(_lowercase :							List[Any]		,  _lowercase :							Tuple    ):
          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}"

        lowerCAmelCase__    :    Any								=					[x for x in (convert(_lowercase		,  _lowercase    ) for k, v in args_d.items()) if len(_lowercase    )]
        lowerCAmelCase__    :    Optional[Any]								=					[sys.executable, f"{self.test_file_dir}/distillation.py"] + cli_args
        execute_subprocess_async(_lowercase		,  env=self.get_env()    )

        lowerCAmelCase__    :    Any								=					os.listdir(_lowercase    )
        lowerCAmelCase__    :    Union[str, Any]								=					{os.path.basename(_lowercase    ) for p in contents}
        lowerCAmelCase__    :    List[Any]								=					[p for p in contents if p.endswith("ckpt"    )]
        assert len(_lowercase    ) > 0

        self.assertIn("test_generations.txt"		,  _lowercase    )
        self.assertIn("test_results.txt"		,  _lowercase    )

        # get the following from the module, (we don't have access to `model` here)
        lowerCAmelCase__    :    int								=					os.path.join(_lowercase		,  "metrics.json"    )
        lowerCAmelCase__    :    Optional[Any]								=					"rouge2"

        lowerCAmelCase__    :    Dict								=					load_json(_lowercase    )
        # {'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(_lowercase    )
        lowerCAmelCase__    :    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}"]		,  _lowercase    )
        self.assertEqual(len(metrics["test"]    )		,  1    )
        lowerCAmelCase__    :    Union[str, Any]								=					int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1    )
        self.assertEqual(len(metrics["val"]    )		,  _lowercase    )




"""simple docstring"""





from __future__ import annotations

__UpperCAmelCase    				=      [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]

class  lowercase_  :







      def __init__(      self :							int		,  _lowercase :							int    ):
        lowerCAmelCase__    :    int								=					-1
        lowerCAmelCase__    :    int								=					-1
        lowerCAmelCase__    :    list[int]								=					[-1] * length
        lowerCAmelCase__    :    Node | None								=					None
        lowerCAmelCase__    :    Node | None								=					None







      def __repr__(      self :							List[Any]    ):
        return f"Node(min_value={self.minn} max_value={self.maxx})"





def     lowercase__	(  lowerCamelCase     :  list[int]				)					->     Node | None:
  lowerCAmelCase__    :    Any								=					Node(len(lowerCamelCase				)				)
  lowerCAmelCase__				,		lowerCAmelCase__    :    Any								=					min(lowerCamelCase				), max(lowerCamelCase				)
  # Leaf node case where the node contains only one unique value
  if root.minn == root.maxx:
    return root
  lowerCAmelCase__    :    str								=					(root.minn + root.maxx) // 2

  lowerCAmelCase__    :    list[int]								=					[]
  lowerCAmelCase__    :    list[int]								=					[]

  for index, num in enumerate(lowerCamelCase				):
    if num <= pivot:
      left_arr.append(lowerCamelCase				)
    else:
      right_arr.append(lowerCamelCase				)
    lowerCAmelCase__    :    Optional[Any]								=					len(lowerCamelCase				)
  lowerCAmelCase__    :    List[str]								=					build_tree(lowerCamelCase				)
  lowerCAmelCase__    :    List[Any]								=					build_tree(lowerCamelCase				)
  return root





def     lowercase__	(  lowerCamelCase     :  Node | None				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				)					->     int:
  if index < 0 or node is None:
    return 0
  # Leaf node cases
  if node.minn == node.maxx:
    return index + 1 if node.minn == num else 0
  lowerCAmelCase__    :    int								=					(node.minn + node.maxx) // 2
  if num <= pivot:
    # go the left subtree and map index to the left subtree
    return rank_till_index(node.left				,	lowerCamelCase				,	node.map_left[index] - 1				)
  else:
    # go to the right subtree and map index to the right subtree
    return rank_till_index(node.right				,	lowerCamelCase				,	index - node.map_left[index]				)





def     lowercase__	(  lowerCamelCase     :  Node | None				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				)					->     int:
  if start > end:
    return 0
  lowerCAmelCase__    :    int								=					rank_till_index(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				)
  lowerCAmelCase__    :    Optional[Any]								=					rank_till_index(lowerCamelCase				,	lowerCamelCase				,	start - 1				)
  return rank_till_end - rank_before_start





def     lowercase__	(  lowerCamelCase     :  Node | None				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				)					->     int:
  if index > (end - start) or start > end or node is None:
    return -1
  # Leaf node case
  if node.minn == node.maxx:
    return node.minn
  # Number of elements in the left subtree in interval [start, end]
  lowerCAmelCase__    :    List[str]								=					node.map_left[end] - (
      node.map_left[start - 1] if start else 0
  )
  if num_elements_in_left_tree > index:
    return quantile(
        node.left				,	lowerCamelCase				,	(node.map_left[start - 1] if start else 0)				,	node.map_left[end] - 1				,	)
  else:
    return quantile(
        node.right				,	index - num_elements_in_left_tree				,	start - (node.map_left[start - 1] if start else 0)				,	end - node.map_left[end]				,	)





def     lowercase__	(  lowerCamelCase     :  Node | None				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				)					->     int:
  if (
      start > end
      or node is None
      or start_num > end_num
      or node.minn > end_num
      or node.maxx < start_num
  ):
    return 0
  if start_num <= node.minn and node.maxx <= end_num:
    return end - start + 1
  lowerCAmelCase__    :    List[str]								=					range_counting(
      node.left				,	(node.map_left[start - 1] if start else 0)				,	node.map_left[end] - 1				,	lowerCamelCase				,	lowerCamelCase				,	)
  lowerCAmelCase__    :    List[str]								=					range_counting(
      node.right				,	start - (node.map_left[start - 1] if start else 0)				,	end - node.map_left[end]				,	lowerCamelCase				,	lowerCamelCase				,	)
  return left + right


if __name__ == "__main__":
   import doctest

   doctest.testmod()
