


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


snake_case       :					List[str]        =  torch.cuda.is_available()
snake_case       :					List[str]        =  [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
snake_case       :					str        =  ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
snake_case       :					str        =  {
    """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		(				UpperCAmelCase_				,						UpperCAmelCase_		):




      """simple docstring"""





      a		:Dict											= '\n'.join(lowerCAmelCase__		)
      Path(lowerCAmelCase__		).open('''w'''		).writelines(lowerCAmelCase__		)
def        __lowerCamelCase		(				UpperCAmelCase_		):




      """simple docstring"""





      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 	_snake_case						(   snake_case__							):




 @classmethod
 def  SCREAMING_SNAKE_CASE__       (	cls					):
       return cls




 @require_torch_multi_gpu
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= {
           '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  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase=True					):
       a		:Any											= {
           '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		:dict											= CHEAP_ARGS.copy()
       a		:Optional[Any]											= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
       a		:Union[str, Any]											= 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		:Union[str, Any]											= [x for x in (convert(_lowerCamelCase      ,						_lowerCamelCase					) for k, v in args_d.items()) if len(_lowerCamelCase					)]
       a		:List[Any]											= [sys.executable, F'''{self.test_file_dir}/distillation.py'''] + cli_args
       execute_subprocess_async(_lowerCamelCase      ,						env=self.get_env()					)

       a		:Optional[int]											= os.listdir(_lowerCamelCase					)
       a		:Tuple											= {os.path.basename(_lowerCamelCase					) for p in contents}
       a		:int											= [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		:Any											= os.path.join(_lowerCamelCase      ,						'''metrics.json'''					)
       a		:Union[str, Any]											= 'rouge2'

       a		:int											= 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		: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}''']      ,						_lowerCamelCase					)
       self.assertEqual(len(metrics['''test''']					)      ,						1					)
       a		:Dict											= int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1					)
       self.assertEqual(len(metrics['''val''']					)      ,						_lowerCamelCase					)









import numpy as np
def        __lowerCamelCase		(				UpperCAmelCase_		:	Optional[Any]		):




      """simple docstring"""





      a							,       a		:Union[str, Any]											= a.shape
      a		:Any											= min(UpperCAmelCase_				,						UpperCAmelCase_		)
      a		:Tuple											= np.eye(UpperCAmelCase_		)
      a		:Any											= a.copy()

      for k in range(t - 1		):
            # select a column of modified matrix A':
            a		:List[str]											= r[k:, [k]]
            # construct first basis vector
            a		:List[str]											= np.zeros_like(UpperCAmelCase_		)
            a		:List[Any]											= 1.0
            # determine scaling factor
            a		:Union[str, Any]											= np.linalg.norm(UpperCAmelCase_		)
            # construct vector v for Householder reflection
            a		:List[str]											= x + np.sign(x[0]		) * alpha * ea
            v /= np.linalg.norm(UpperCAmelCase_		)

            # construct the Householder matrix
            a		:Union[str, Any]											= np.eye(m - k		) - 2.0 * v @ v.T
            # pad with ones and zeros as necessary
            a		:Tuple											= np.block([[np.eye(UpperCAmelCase_		), np.zeros((k, m - k)		)], [np.zeros((m - k, k)		), q_k]]		)

            a		:List[Any]											= q @ q_k.T
            a		:int											= q_k @ r

      return q, r


if __name__ == "__main__":
     import doctest

     doctest.testmod()





