


"""simple docstring"""





import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


snake_case__						:   str			 = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
 from run_translation import main  # noqa


set_seed(42)
snake_case__						:   int			 = '''sshleifer/student_marian_en_ro_6_1'''
snake_case__						:   Union[str, Any]			 = '''sshleifer/tiny-mbart'''



@require_torch
class 						snake_case_(				a__	):






     def    lowerCamelCase__   (		self     :    Optional[int]    , UpperCamelCase_     :    Optional[int]=False    , UpperCamelCase_     :    Dict=None    , UpperCamelCase_     :    int=True    , UpperCamelCase_     :    Optional[int]=True    , UpperCamelCase_     :    Optional[int]=True    , UpperCamelCase_     :    Optional[int]=True    , ):
           lowerCAmelCase    :		Any				=      self.run_trainer(
               eval_steps=1    , max_len=1_2    , model_name=UpperCamelCase_    , num_train_epochs=1    , distributed=UpperCamelCase_    , extra_args_str=UpperCamelCase_    , predict_with_generate=UpperCamelCase_    , do_train=UpperCamelCase_    , do_eval=UpperCamelCase_    , do_predict=UpperCamelCase_    , )
           lowerCAmelCase    :		Dict				=      TrainerState.load_from_json(os.path.join(UpperCamelCase_    , '''trainer_state.json''' ) ).log_history

           if not do_eval:
                 return

           lowerCAmelCase    :		Any				=      [log for log in logs if '''eval_loss''' in log.keys()]

           lowerCAmelCase    :		Dict				=      eval_metrics[0]
           if predict_with_generate:
                 assert "eval_bleu" in first_step_stats

                 lowerCAmelCase    :		Tuple				=      eval_metrics[-1]
                 assert isinstance(last_step_stats['''eval_bleu''']    , UpperCamelCase_ )
                 assert not math.isnan(float(last_step_stats['''eval_loss'''] ) ), "eval_loss must not be `nan`"






     @require_torch_non_multi_gpu
     def    lowerCamelCase__   (		self     :    Optional[int] ):
           self.run_seqaseq_quick()






     @require_torch_multi_gpu
     def    lowerCamelCase__   (		self     :    Dict ):
           self.run_seqaseq_quick(distributed=UpperCamelCase_ )






     @require_torch_multi_gpu
     def    lowerCamelCase__   (		self     :    Tuple ):
           self.run_seqaseq_quick(distributed=UpperCamelCase_ )






     @unittest.skip('''Requires an update of the env running those tests''' )
     @require_torch_multi_gpu
     @require_fairscale
     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           self.run_seqaseq_quick(distributed=UpperCamelCase_    , extra_args_str='''--sharded_ddp simple''' )






     @unittest.skip('''Requires an update of the env running those tests''' )
     @require_torch_multi_gpu
     @require_fairscale
     def    lowerCamelCase__   (		self     :    Optional[int] ):
           self.run_seqaseq_quick(distributed=UpperCamelCase_    , extra_args_str='''--sharded_ddp simple --fp16''' )






     @unittest.skip('''Requires an update of the env running those tests''' )
     @require_torch_multi_gpu
     @require_fairscale
     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           self.run_seqaseq_quick(distributed=UpperCamelCase_    , extra_args_str='''--sharded_ddp zero_dp_2'''    , predict_with_generate=UpperCamelCase_ )






     @unittest.skip('''Requires an update of the env running those tests''' )
     @require_torch_multi_gpu
     @require_fairscale
     def    lowerCamelCase__   (		self     :    List[str] ):
           self.run_seqaseq_quick(
               distributed=UpperCamelCase_    , extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''    , predict_with_generate=UpperCamelCase_ )






     @require_apex
     @require_torch_gpu
     def    lowerCamelCase__   (		self     :    List[Any] ):
           # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
           # program and it breaks other tests that run from the same pytest worker, therefore until this is
           # sorted out it must be run only in an external program, that is distributed=True in this
           # test and only under one or more gpus - if we want cpu will need to make a special test
           #
           # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
           # 2nd main() call it botches the future eval.
           #
           self.run_seqaseq_quick(distributed=UpperCamelCase_    , extra_args_str='''--fp16 --fp16_backend=apex''' )
           # test 2nd time - was getting eval_loss': nan'
           # to reproduce the problem set distributed=False
           self.run_seqaseq_quick(distributed=UpperCamelCase_    , extra_args_str='''--fp16 --fp16_backend=apex''' )






     @parameterized.expand(['''base''', '''low''', '''high''', '''mixed'''] )
     @require_torch_multi_gpu
     def    lowerCamelCase__   (		self     :    Any    , UpperCamelCase_     :    Optional[Any] ):
           # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
           lowerCAmelCase    :		Optional[Any]				=      {
               # test with the default log_level - should be info and thus log info once
               '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
               # test with low log_level and log_level_replica - should be noisy on all processes
               # now the info string should appear twice on 2 processes
               '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
               # test with high log_level and low log_level_replica
               # now the info string should appear once only on the replica
               '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
               # test with high log_level and log_level_replica - should be quiet on all processes
               '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
           }

           lowerCAmelCase    :		List[str]				=      experiments[experiment_id]
           lowerCAmelCase    :		Optional[Any]				=      {'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
           lowerCAmelCase    :		Union[str, Any]				=      '''Running training'''
           with CaptureStderr() as cl:
                 self.run_seqaseq_quick(**UpperCamelCase_    , extra_args_str=data['''extra_args_str'''] )
           lowerCAmelCase    :		Tuple				=      len(re.findall(UpperCamelCase_    , cl.err ) )
           self.assertEqual(UpperCamelCase_    , data['''n_matches'''] )






     @slow
     def    lowerCamelCase__   (		self     :    str ):
           lowerCAmelCase    :		Tuple				=      self.run_trainer(
               eval_steps=2    , max_len=1_2_8    , model_name=UpperCamelCase_    , learning_rate=3E-4    , num_train_epochs=1_0    , distributed=UpperCamelCase_    , )

           # Check metrics
           lowerCAmelCase    :		Optional[int]				=      TrainerState.load_from_json(os.path.join(UpperCamelCase_    , '''trainer_state.json''' ) ).log_history
           lowerCAmelCase    :		List[Any]				=      [log for log in logs if '''eval_loss''' in log.keys()]
           lowerCAmelCase    :		Optional[int]				=      eval_metrics[0]
           lowerCAmelCase    :		List[str]				=      eval_metrics[-1]

           assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
           assert isinstance(last_step_stats['''eval_bleu''']    , UpperCamelCase_ )

           # test if do_predict saves generations and metrics
           lowerCAmelCase    :		List[Any]				=      os.listdir(UpperCamelCase_ )
           lowerCAmelCase    :		Optional[int]				=      {os.path.basename(UpperCamelCase_ ) for p in contents}
           assert "generated_predictions.txt" in contents
           assert "predict_results.json" in contents






     @slow
     @require_bitsandbytes
     def    lowerCamelCase__   (		self     :    Tuple ):
           from transformers.training_args import OptimizerNames

           def train_and_return_metrics(UpperCamelCase_     :    str ) -> Tuple[int, float]:
                 lowerCAmelCase    :		List[Any]				=      '''--skip_memory_metrics 0'''

                 lowerCAmelCase    :		Optional[int]				=      self.run_trainer(
                     max_len=1_2_8    , model_name=UpperCamelCase_    , learning_rate=3E-4    , num_train_epochs=1    , optim=UpperCamelCase_    , distributed=UpperCamelCase_    , extra_args_str=UpperCamelCase_    , do_eval=UpperCamelCase_    , do_predict=UpperCamelCase_    , n_gpus_to_use=1    , )

                 # Check metrics
                 lowerCAmelCase    :		Dict				=      TrainerState.load_from_json(Path(UpperCamelCase_    , '''trainer_state.json''' ) ).log_history
                 lowerCAmelCase    :		Optional[int]				=      int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**2_0 )
                 lowerCAmelCase    :		Tuple				=      int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**2_0 )

                 lowerCAmelCase    :		List[Any]				=      logs[0]['''train_loss''']
                 return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		List[str]				=      train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		Dict				=      train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )

           lowerCAmelCase    :		Union[str, Any]				=      gpu_alloc_mem_orig - gpu_alloc_mem_bnb

           lowerCAmelCase    :		int				=      gpu_peak_mem_orig + gpu_alloc_mem_orig
           lowerCAmelCase    :		Union[str, Any]				=      gpu_peak_mem_bnb + gpu_alloc_mem_bnb
           lowerCAmelCase    :		Optional[int]				=      gpu_total_mem_orig - gpu_total_mem_bnb

           # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
           # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
           # in 2 bytes and the diff in optim memory usage is derived as so:
           #
           # - normal 25*8=~200MB (8 bytes per param)
           # - bnb    25*2= ~50MB (2 bytes per param)
           #
           # Thus we should expect ~150MB total memory saved.
           #
           # Peak memory should be the same - the total should be different by about that same margin
           #
           # After leaving a small margin to accommodate for differences between gpus let's check
           # that we have at least 120MB in savings
           lowerCAmelCase    :		Optional[Any]				=      1_2_0

           # uncomment the following if this test starts failing - requires py38 for a new print feature
           # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
           # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
           # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
           # print(f"{gpu_alloc_mem_diff=}MB")
           # print(f"{gpu_peak_mem_diff=}MB")
           # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
           # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

           self.assertGreater(
               UpperCamelCase_    , UpperCamelCase_    , '''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
               F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
               F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''    , )

           self.assertGreater(
               UpperCamelCase_    , UpperCamelCase_    , '''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
               F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
               F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''    , )

           self.assertEqual(
               UpperCamelCase_    , UpperCamelCase_    , F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}''' )






     def    lowerCamelCase__   (		self     :    Optional[int]    , UpperCamelCase_     :    int    , UpperCamelCase_     :    str    , UpperCamelCase_     :    int    , UpperCamelCase_     :    float = 3E-3    , UpperCamelCase_     :    str = "adafactor"    , UpperCamelCase_     :    bool = False    , UpperCamelCase_     :    str = None    , UpperCamelCase_     :    int = 0    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    int = None    , ):
           lowerCAmelCase    :		str				=      self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
           lowerCAmelCase    :		Tuple				=      self.get_auto_remove_tmp_dir()
           lowerCAmelCase    :		Dict				=      F'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(UpperCamelCase_ )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(UpperCamelCase_ )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

           lowerCAmelCase    :		int				=      F'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(UpperCamelCase_ )}
        '''.split()

           lowerCAmelCase    :		List[Any]				=      '''
            --do_predict
        '''.split()

           lowerCAmelCase    :		List[str]				=      []
           if do_train:
                 args += args_train

           if do_eval:
                 args += args_eval

           if do_predict:
                 args += args_predict

           if predict_with_generate:
                 args += "--predict_with_generate".split()

           if do_train:
                 if optim == "adafactor":
                       args += "--adafactor".split()
                 else:
                       args += F'''--optim {optim}'''.split()

           if extra_args_str is not None:
                 args += extra_args_str.split()

           if distributed:
                 if n_gpus_to_use is None:
                       lowerCAmelCase    :		List[Any]				=      get_gpu_count()
                 lowerCAmelCase    :		Dict				=      get_torch_dist_unique_port()
                 lowerCAmelCase    :		Optional[Any]				=      F'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
                 lowerCAmelCase    :		str				=      [sys.executable] + distributed_args + args
                 # keep for quick debug
                 # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
                 execute_subprocess_async(UpperCamelCase_    , env=self.get_env() )
           else:
                 lowerCAmelCase    :		List[str]				=      ['''run_translation.py'''] + args
                 with patch.object(UpperCamelCase_    , '''argv'''    , UpperCamelCase_ ):
                       main()

           return output_dir






"""simple docstring"""





import math


def    _snake_case  (  ):
      lowerCAmelCase    :		Union[str, Any]				=      input('''Enter message: '''	)
      lowerCAmelCase    :		Optional[int]				=      int(input(f'''Enter key [2-{len(_snake_case	) - 1}]: '''	)	)
      lowerCAmelCase    :		str				=      input('''Encryption/Decryption [e/d]: '''	)

      if mode.lower().startswith('''e'''	):
            lowerCAmelCase    :		Any				=      encrypt_message(_snake_case       ,      _snake_case	)
      elif mode.lower().startswith('''d'''	):
            lowerCAmelCase    :		Union[str, Any]				=      decrypt_message(_snake_case       ,      _snake_case	)

      # Append pipe symbol (vertical bar) to identify spaces at the end.
      print(f'''Output:\n{text + "|"}'''	)


def    _snake_case  (  _snake_case :	int       ,      _snake_case :	str	):
      lowerCAmelCase    :		Optional[Any]				=      [''''''] * key
      for col in range(_snake_case	):
            lowerCAmelCase    :		Optional[Any]				=      col
            while pointer < len(_snake_case	):
                  cipher_text[col] += message[pointer]
                  pointer += key
      return "".join(_snake_case	)


def    _snake_case  (  _snake_case :	int       ,      _snake_case :	str	):
      lowerCAmelCase    :		Union[str, Any]				=      math.ceil(len(_snake_case	) / key	)
      lowerCAmelCase    :		str				=      key
      lowerCAmelCase    :		Any				=      (num_cols * num_rows) - len(_snake_case	)
      lowerCAmelCase    :		Dict				=      [''''''] * num_cols
      lowerCAmelCase    :		int				=      0
      lowerCAmelCase    :		int				=      0

      for symbol in message:
            plain_text[col] += symbol
            col += 1

            if (
                (col == num_cols)
                or (col == num_cols - 1)
                and (row >= num_rows - num_shaded_boxes)
            ):
                  lowerCAmelCase    :		int				=      0
                  row += 1

      return "".join(_snake_case	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()


