



'''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


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



     """simple docstring"""



     _a     =  '''\n'''.join(__A)
     Path(__A).open('''w''').writelines(__A)




def lowerCAmelCase  (__A):



     """simple docstring"""



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






class __A      (  A						):



 '''simple docstring'''







 @classmethod
 def  a__  (cls	)   ->					List[Any]:


      """simple docstring"""



      return cls



 @require_torch_multi_gpu
 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



      _a     =  {
          '''no_teacher''': True,
          '''freeze_encoder''': True,
          '''gpus''': 2,
          '''overwrite_output_dir''': True,
          '''sortish_sampler''': True,
      }
      self._test_distiller_cli_fork(A			,       check_contents=A	)



 def  a__  (self			,       A			,       A=True	)   ->					Union[str, Any]:


      """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(A	)
      _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=A			,       output_dir=A			,       **A	)

      def convert(A			,       A	):
           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(A			,       A	) for k, v in args_d.items()) if len(A	)]
      _a     =  [sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
      execute_subprocess_async(A			,       env=self.get_env()	)

      _a     =  os.listdir(A	)
      _a     =  {os.path.basename(A	) for p in contents}
      _a     =  [p for p in contents if p.endswith('''ckpt'''	)]
      assert len(A	) > 0

      self.assertIn('''test_generations.txt'''			,       A	)
      self.assertIn('''test_results.txt'''			,       A	)

      # get the following from the module, (we don't have access to `model` here)
      _a     =  os.path.join(A			,       '''metrics.json'''	)
      _a     =  '''rouge2'''

      _a     =  load_json(A	)
      # {'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(A	)
      _a     =  metrics['''val'''][-1]
      self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']			,       0.01	)
      self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']			,       A	)
      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''']	)			,       A	)








'''simple docstring'''





import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowercase_			=  logging.getLogger(__name__)




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  argparse.ArgumentParser(description='''PyTorch Transformer Language Model''')
     parser.add_argument('''--model_name'''  ,     type=__A  ,     default='''transfo-xl-wt103'''  ,     help='''pretrained model name''')
     parser.add_argument(
         '''--split'''  ,     type=__A  ,     default='''test'''  ,     choices=['''all''', '''valid''', '''test''']  ,     help='''which split to evaluate''')
     parser.add_argument('''--batch_size'''  ,     type=__A  ,     default=10  ,     help='''batch size''')
     parser.add_argument('''--tgt_len'''  ,     type=__A  ,     default=128  ,     help='''number of tokens to predict''')
     parser.add_argument('''--ext_len'''  ,     type=__A  ,     default=0  ,     help='''length of the extended context''')
     parser.add_argument('''--mem_len'''  ,     type=__A  ,     default=1_600  ,     help='''length of the retained previous heads''')
     parser.add_argument('''--clamp_len'''  ,     type=__A  ,     default=1_000  ,     help='''max positional embedding index''')
     parser.add_argument('''--no_cuda'''  ,     action='''store_true'''  ,     help='''Do not use CUDA even though CUA is available''')
     parser.add_argument('''--work_dir'''  ,     type=__A  ,     required=__A  ,     help='''path to the work_dir''')
     parser.add_argument('''--no_log'''  ,     action='''store_true'''  ,     help='''do not log the eval result''')
     parser.add_argument('''--same_length'''  ,     action='''store_true'''  ,     help='''set same length attention with masking''')
     parser.add_argument('''--server_ip'''  ,     type=__A  ,     default=''''''  ,     help='''Can be used for distant debugging.''')
     parser.add_argument('''--server_port'''  ,     type=__A  ,     default=''''''  ,     help='''Can be used for distant debugging.''')
     _a     =  parser.parse_args()
     assert args.ext_len >= 0, "extended context length must be non-negative"

     if args.server_ip and args.server_port:
          # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
          import ptvsd

          print('''Waiting for debugger attach''')
          ptvsd.enable_attach(address=(args.server_ip, args.server_port)  ,     redirect_output=__A)
          ptvsd.wait_for_attach()

     _a     =  torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')
     logger.info('''device: {}'''.format(__A))

     # Load a pre-processed dataset
     # You can also build the corpus yourself using TransfoXLCorpus methods
     # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
     # and tokenizing the dataset
     # The pre-processed corpus is a convertion (using the conversion script )
     _a     =  TransfoXLCorpus.from_pretrained(args.model_name)

     _a     =  corpus.get_iterator('''valid'''  ,     args.batch_size  ,     args.tgt_len  ,     device=__A  ,     ext_len=args.ext_len)
     _a     =  corpus.get_iterator('''test'''  ,     args.batch_size  ,     args.tgt_len  ,     device=__A  ,     ext_len=args.ext_len)

     # Load a pre-trained model
     _a     =  TransfoXLLMHeadModel.from_pretrained(args.model_name)
     model.to(__A)

     logger.info(
         '''Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'''.format(
             args.batch_size  ,     args.tgt_len  ,     args.ext_len  ,     args.mem_len  ,     args.clamp_len))

     model.reset_memory_length(args.mem_len)
     if args.clamp_len > 0:
          _a     =  args.clamp_len
     if args.same_length:
          _a     =  True

     ###############################################################################
     # Evaluation code
     ###############################################################################
     def evaluate(__A):
          # Turn on evaluation mode which disables dropout.
          model.eval()
          _a  ,   _a     =  0, 0.0
          _a     =  time.time()
          with torch.no_grad():
               _a     =  None
               for idx, (data, target, seq_len) in enumerate(__A):
                    _a     =  model(__A  ,     lm_labels=__A  ,     mems=__A)
                    _a  ,   _a  ,   _a     =  ret
                    _a     =  loss.mean()
                    total_loss += seq_len * loss.item()
                    total_len += seq_len
               _a     =  time.time() - start_time
          logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(__A  ,     1_000 * total_time / (idx + 1)))
          return total_loss / total_len

     # Run on test data.
     if args.split == "all":
          _a     =  evaluate(__A)
          _a     =  evaluate(__A)
     elif args.split == "valid":
          _a     =  evaluate(__A)
          _a     =  None
     elif args.split == "test":
          _a     =  evaluate(__A)
          _a     =  None

     def format_log(__A  ,     __A):
          _a     =  '''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(__A  ,     __A  ,     math.exp(__A))
          return log_str

     _a     =  ''''''
     if valid_loss is not None:
          log_str += format_log(__A  ,     '''valid''')
     if test_loss is not None:
          log_str += format_log(__A  ,     '''test''')

     logger.info('''=''' * 100)
     logger.info(__A)
     logger.info('''=''' * 100)


if __name__ == "__main__":
  main()



