




# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


UpperCamelCase__       				=	logging.get_logger(__name__)


if is_torch_available():
  import torch
  from torch import nn
  from torch.utils.data import Dataset

  from transformers import Trainer
  class 							__lowercase   (			a__    ):



       def __init__(  self   :							str      ,		lowercase__   :							int = 1_0_1       ):
         a_		    =  length



       def __len__(  self   :							Optional[int]       ):
         return self.length



       def __getitem__(  self   :							List[Any]      ,		lowercase__   :							int       ):
         return i



  class 							__lowercase   :



       def __call__(  self   :							int      ,		lowercase__   :							List[Any]       ):
         return {"input_ids": torch.tensor(lowercase__       ), "labels": torch.tensor(lowercase__       )}



  class 							__lowercase   (			nn.Module    ):



       def __init__(  self   :							int       ):
         super().__init__()
         # Add some (unused) params otherwise DDP will complain.
         a_		    =  nn.Linear(1_2_0      ,		8_0       )



       def 				__magic_name__		(  self   :							Dict      ,		lowercase__   :							Dict      ,		lowercase__   :							Union[str, Any]=None       ):
         if labels is not None:
           return torch.tensor(0.0      ,		device=input_ids.device       ), input_ids
         else:
           return input_ids






def 				UpperCAmelCase__  (							):






  """simple docstring"""


  a_		    =  HfArgumentParser((TrainingArguments,)			)
  sys.argv += ["--output_dir", "./examples"]
  a_		    =  parser.parse_args_into_dataclasses()[0]

  logger.warning(
      f"Process rank: {training_args.local_rank}, device: {training_args.device}, "
      f"tpu_num_cores: {training_args.tpu_num_cores}"      ,      )

  # Essentially, what we want to verify in the distributed case is
  # that we get all samples back, in the right order.
  # (this is crucial for prediction for instance)
  for dataset_length in [1_001, 256, 15]:
    a_		    =  DummyDataset(_A			)

    def compute_metrics(_A			) -> Dict:
      a_		    =  list(range(len(_A			)			)			)
      a_		    =  p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

    a_		    =  Trainer(
        model=DummyModel()      ,      args=_A      ,      data_collator=DummyDataCollator()      ,      eval_dataset=_A      ,      compute_metrics=_A      ,      )
    a_		    =  trainer.evaluate()
    logger.info(_A			)
    if metrics["eval_success"] is not True:
      logger.error(_A			)
      exit(1			)

    a_		    =  trainer.predict(_A			)
    logger.info(p.metrics			)
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics			)
      exit(1			)

    a_		    =  2

    a_		    =  trainer.evaluate()
    logger.info(_A			)
    if metrics["eval_success"] is not True:
      logger.error(_A			)
      exit(1			)

    a_		    =  trainer.predict(_A			)
    logger.info(p.metrics			)
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics			)
      exit(1			)

    a_		    =  None

  logger.info('''🔥 All distributed tests successful'''			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  main()


if __name__ == "__main__":
  main()






from __future__ import annotations

import sys
class 							__lowercase   :



     def __init__(  self   :							Any      ,		lowercase__   :							str      ,		lowercase__   :							int       ):
       a_		    =  letter
       a_		    =  freq
       a_		    =  {}



     def __repr__(  self   :							List[Any]       ):
       return f"{self.letter}:{self.freq}"



class 							__lowercase   :



     def __init__(  self   :							Optional[int]      ,		lowercase__   :							int      ,		lowercase__   :							Letter | TreeNode      ,		lowercase__   :							Letter | TreeNode       ):
       a_		    =  freq
       a_		    =  left
       a_		    =  right






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  {}
  with open(_A			) as f:
    while True:
      a_		    =  f.read(1			)
      if not c:
        break
      a_		    =  chars[c] + 1 if c in chars else 1
  return sorted((Letter(_A      ,      _A			) for c, f in chars.items())      ,      key=lambda _A			: x.freq			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  letters  # type: ignore
  while len(_A			) > 1:
    a_		    =  response.pop(0			)
    a_		    =  response.pop(0			)
    a_		    =  left.freq + right.freq
    a_		    =  TreeNode(_A      ,      _A      ,      _A			)
    response.append(_A			)
    response.sort(key=lambda _A			: x.freq			)
  return response[0]






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if isinstance(_A      ,      _A			):
    a_		    =  bitstring
    return [root]
  a_		    =  root  # type: ignore
  a_		    =  []
  letters += traverse_tree(treenode.left      ,      bitstring + '''0'''			)
  letters += traverse_tree(treenode.right      ,      bitstring + '''1'''			)
  return letters






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  parse_file(_A			)
  a_		    =  build_tree(_A			)
  a_		    =  {
      k: v for letter in traverse_tree(_A      ,      ''''''			) for k, v in letter.bitstring.items()
  }
  print(f"Huffman Coding  of {file_path}: "			)
  with open(_A			) as f:
    while True:
      a_		    =  f.read(1			)
      if not c:
        break
      print(letters[c]      ,      end=''' '''			)
  print()


if __name__ == "__main__":
  # pass the file path to the huffman function
  huffman(sys.argv[1])
