






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


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


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





@require_torch
class 					A__     (     __snake_case ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    int   ,						_SCREAMING_SNAKE_CASE      :    Dict=False   ,						_SCREAMING_SNAKE_CASE      :    Dict=None   ,						_SCREAMING_SNAKE_CASE      :    List[str]=True   ,						_SCREAMING_SNAKE_CASE      :    List[Any]=True   ,						_SCREAMING_SNAKE_CASE      :    Tuple=True   ,						_SCREAMING_SNAKE_CASE      :    int=True   ,						):






           """simple docstring"""




           UpperCamelCase					       =  self.run_trainer(
               eval_steps=1   ,						max_len=12   ,						model_name=_SCREAMING_SNAKE_CASE   ,						num_train_epochs=1   ,						distributed=_SCREAMING_SNAKE_CASE   ,						extra_args_str=_SCREAMING_SNAKE_CASE   ,						predict_with_generate=_SCREAMING_SNAKE_CASE   ,						do_train=_SCREAMING_SNAKE_CASE   ,						do_eval=_SCREAMING_SNAKE_CASE   ,						do_predict=_SCREAMING_SNAKE_CASE   ,						)
           UpperCamelCase					       =  TrainerState.load_from_json(os.path.join(_SCREAMING_SNAKE_CASE   ,						'trainer_state.json' ) ).log_history

           if not do_eval:
                 return

           UpperCamelCase					       =  [log for log in logs if 'eval_loss' in log.keys()]

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

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







     @require_torch_non_multi_gpu
     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           self.run_seqaseq_quick()







     @require_torch_multi_gpu
     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE )







     @require_torch_multi_gpu
     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE )







     @unittest.skip('Requires an update of the env running those tests' )
     @require_torch_multi_gpu
     @require_fairscale
     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE   ,						extra_args_str='--sharded_ddp simple' )







     @unittest.skip('Requires an update of the env running those tests' )
     @require_torch_multi_gpu
     @require_fairscale
     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE   ,						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   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE   ,						extra_args_str='--sharded_ddp zero_dp_2'   ,						predict_with_generate=_SCREAMING_SNAKE_CASE )







     @unittest.skip('Requires an update of the env running those tests' )
     @require_torch_multi_gpu
     @require_fairscale
     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           self.run_seqaseq_quick(
               distributed=_SCREAMING_SNAKE_CASE   ,						extra_args_str='--sharded_ddp zero_dp_2 --fp16'   ,						predict_with_generate=_SCREAMING_SNAKE_CASE )







     @require_apex
     @require_torch_gpu
     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE   ,						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=_SCREAMING_SNAKE_CASE   ,						extra_args_str='--fp16 --fp16_backend=apex' )







     @parameterized.expand(['base', 'low', 'high', 'mixed'] )
     @require_torch_multi_gpu
     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  {
               # 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},
           }

           UpperCamelCase					       =  experiments[experiment_id]
           UpperCamelCase					       =  {'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
           UpperCamelCase					       =  'Running training'
           with CaptureStderr() as cl:
                 self.run_seqaseq_quick(**_SCREAMING_SNAKE_CASE   ,						extra_args_str=data['extra_args_str'] )
           UpperCamelCase					       =  len(re.findall(_SCREAMING_SNAKE_CASE   ,						cl.err ) )
           self.assertEqual(_SCREAMING_SNAKE_CASE   ,						data['n_matches'] )







     @slow
     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  self.run_trainer(
               eval_steps=2   ,						max_len=128   ,						model_name=_SCREAMING_SNAKE_CASE   ,						learning_rate=3E-4   ,						num_train_epochs=10   ,						distributed=_SCREAMING_SNAKE_CASE   ,						)

           # Check metrics
           UpperCamelCase					       =  TrainerState.load_from_json(os.path.join(_SCREAMING_SNAKE_CASE   ,						'trainer_state.json' ) ).log_history
           UpperCamelCase					       =  [log for log in logs if 'eval_loss' in log.keys()]
           UpperCamelCase					       =  eval_metrics[0]
           UpperCamelCase					       =  eval_metrics[-1]

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

           # test if do_predict saves generations and metrics
           UpperCamelCase					       =  os.listdir(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  {os.path.basename(_SCREAMING_SNAKE_CASE ) for p in contents}
           assert "generated_predictions.txt" in contents
           assert "predict_results.json" in contents







     @slow
     @require_bitsandbytes
     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           from transformers.training_args import OptimizerNames

           def train_and_return_metrics(_SCREAMING_SNAKE_CASE      :    str ) -> Tuple[int, float]:
                 UpperCamelCase					       =  '--skip_memory_metrics 0'

                 UpperCamelCase					       =  self.run_trainer(
                     max_len=128   ,						model_name=_SCREAMING_SNAKE_CASE   ,						learning_rate=3E-4   ,						num_train_epochs=1   ,						optim=_SCREAMING_SNAKE_CASE   ,						distributed=_SCREAMING_SNAKE_CASE   ,						extra_args_str=_SCREAMING_SNAKE_CASE   ,						do_eval=_SCREAMING_SNAKE_CASE   ,						do_predict=_SCREAMING_SNAKE_CASE   ,						n_gpus_to_use=1   ,						)

                 # Check metrics
                 UpperCamelCase					       =  TrainerState.load_from_json(Path(_SCREAMING_SNAKE_CASE   ,						'trainer_state.json' ) ).log_history
                 UpperCamelCase					       =  int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20 )
                 UpperCamelCase					       =  int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20 )

                 UpperCamelCase					       =  logs[0]['train_loss']
                 return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

           UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
           UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )

           UpperCamelCase					       =  gpu_alloc_mem_orig - gpu_alloc_mem_bnb

           UpperCamelCase					       =  gpu_peak_mem_orig + gpu_alloc_mem_orig
           UpperCamelCase					       =  gpu_peak_mem_bnb + gpu_alloc_mem_bnb
           UpperCamelCase					       =  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
           UpperCamelCase					       =  120

           # 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(
               _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						'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(
               _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						'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(
               _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						f'loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}' )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    int   ,						_SCREAMING_SNAKE_CASE      :    str   ,						_SCREAMING_SNAKE_CASE      :    int   ,						_SCREAMING_SNAKE_CASE      :    float = 3E-3   ,						_SCREAMING_SNAKE_CASE      :    str = "adafactor"   ,						_SCREAMING_SNAKE_CASE      :    bool = False   ,						_SCREAMING_SNAKE_CASE      :    str = None   ,						_SCREAMING_SNAKE_CASE      :    int = 0   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    int = None   ,						):






           """simple docstring"""




           UpperCamelCase					       =  self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
           UpperCamelCase					       =  self.get_auto_remove_tmp_dir()
           UpperCamelCase					       =  f'\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(_SCREAMING_SNAKE_CASE )}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(_SCREAMING_SNAKE_CASE )}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        '.split()

           UpperCamelCase					       =  f'\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(_SCREAMING_SNAKE_CASE )}\n        '.split()

           UpperCamelCase					       =  '\n            --do_predict\n        '.split()

           UpperCamelCase					       =  []
           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:
                       UpperCamelCase					       =  get_gpu_count()
                 UpperCamelCase					       =  get_torch_dist_unique_port()
                 UpperCamelCase					       =  f'\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            '.split()
                 UpperCamelCase					       =  [sys.executable] + distributed_args + args
                 # keep for quick debug
                 # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
                 execute_subprocess_async(_SCREAMING_SNAKE_CASE   ,						env=self.get_env() )
           else:
                 UpperCamelCase					       =  ['run_translation.py'] + args
                 with patch.object(_SCREAMING_SNAKE_CASE   ,						'argv'   ,						_SCREAMING_SNAKE_CASE ):
                       main()

           return output_dir











def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

      if bit_count < 0:
            raise ValueError('The given input must be positive')

      # get the generated string sequence
      UpperCamelCase					       =  gray_code_sequence_string(_UpperCamelCase)
      #
      # convert them to integers
      for i in range(len(_UpperCamelCase)):
            UpperCamelCase					       =  int(sequence[i]       ,							2)

      return sequence
def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

      if bit_count == 0:
            return ["0"]

      if bit_count == 1:
            return ["0", "1"]

      UpperCamelCase					       =  1 << bit_count  # defines the length of the sequence
      # 1<< n is equivalent to 2^n

      # recursive answer will generate answer for n-1 bits
      UpperCamelCase					       =  gray_code_sequence_string(bit_count - 1)

      UpperCamelCase					       =  []

      # append 0 to first half of the smaller sequence generated
      for i in range(seq_len // 2):
            UpperCamelCase					       =  '0' + smaller_sequence[i]
            sequence.append(_UpperCamelCase)

      # append 1 to second half ... start from the end of the list
      for i in reversed(range(seq_len // 2)):
            UpperCamelCase					       =  '1' + smaller_sequence[i]
            sequence.append(_UpperCamelCase)

      return sequence


if __name__ == "__main__":
       import doctest

       doctest.testmod()



