'''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


A_     :						Optional[Any]    							=os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'''{bindir}/../../examples/pytorch/translation'''):
 from run_translation import main  # noqa


set_seed(42)
A_     :						Tuple    							='''sshleifer/student_marian_en_ro_6_1'''
A_     :						Tuple    							='''sshleifer/tiny-mbart'''


@require_torch
class     __UpperCAmelCase       ( __a		):





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase=False    ,     _lowerCamelCase=None    ,     _lowerCamelCase=True    ,     _lowerCamelCase=True    ,     _lowerCamelCase=True    ,     _lowerCamelCase=True    ,     ):
     lowerCAmelCase_			=      self.run_trainer(
         eval_steps=1    ,     max_len=12    ,     model_name=_lowerCamelCase    ,     num_train_epochs=1    ,     distributed=_lowerCamelCase    ,     extra_args_str=_lowerCamelCase    ,     predict_with_generate=_lowerCamelCase    ,     do_train=_lowerCamelCase    ,     do_eval=_lowerCamelCase    ,     do_predict=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      TrainerState.load_from_json(os.path.join(_lowerCamelCase    ,     '''trainer_state.json''' ) ).log_history

     if not do_eval:
        return

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

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

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





  @require_torch_non_multi_gpu
  def 						UpperCAmelCase_	(	self ):
     self.run_seqaseq_quick()





  @require_torch_multi_gpu
  def 						UpperCAmelCase_	(	self ):
     self.run_seqaseq_quick(distributed=_lowerCamelCase )





  @require_torch_multi_gpu
  def 						UpperCAmelCase_	(	self ):
     self.run_seqaseq_quick(distributed=_lowerCamelCase )





  @unittest.skip('''Requires an update of the env running those tests''' )
  @require_torch_multi_gpu
  @require_fairscale
  def 						UpperCAmelCase_	(	self ):
     self.run_seqaseq_quick(distributed=_lowerCamelCase    ,     extra_args_str='''--sharded_ddp simple''' )





  @unittest.skip('''Requires an update of the env running those tests''' )
  @require_torch_multi_gpu
  @require_fairscale
  def 						UpperCAmelCase_	(	self ):
     self.run_seqaseq_quick(distributed=_lowerCamelCase    ,     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 						UpperCAmelCase_	(	self ):
     self.run_seqaseq_quick(distributed=_lowerCamelCase    ,     extra_args_str='''--sharded_ddp zero_dp_2'''    ,     predict_with_generate=_lowerCamelCase )





  @unittest.skip('''Requires an update of the env running those tests''' )
  @require_torch_multi_gpu
  @require_fairscale
  def 						UpperCAmelCase_	(	self ):
     self.run_seqaseq_quick(
         distributed=_lowerCamelCase    ,     extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''    ,     predict_with_generate=_lowerCamelCase )





  @require_apex
  @require_torch_gpu
  def 						UpperCAmelCase_	(	self ):
     # 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=_lowerCamelCase    ,     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=_lowerCamelCase    ,     extra_args_str='''--fp16 --fp16_backend=apex''' )





  @parameterized.expand(['''base''', '''low''', '''high''', '''mixed'''] )
  @require_torch_multi_gpu
  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
     lowerCAmelCase_			=      {
         # 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_			=      experiments[experiment_id]
     lowerCAmelCase_			=      {'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
     lowerCAmelCase_			=      '''Running training'''
     with CaptureStderr() as cl:
        self.run_seqaseq_quick(**_lowerCamelCase    ,     extra_args_str=data['''extra_args_str'''] )
     lowerCAmelCase_			=      len(re.findall(_lowerCamelCase    ,     cl.err ) )
     self.assertEqual(_lowerCamelCase    ,     data['''n_matches'''] )





  @slow
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.run_trainer(
         eval_steps=2    ,     max_len=128    ,     model_name=_lowerCamelCase    ,     learning_rate=3E-4    ,     num_train_epochs=10    ,     distributed=_lowerCamelCase    ,     )

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

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

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





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

     def train_and_return_metrics(_lowerCamelCase ) -> Tuple[int, float]:
        lowerCAmelCase_			=      '''--skip_memory_metrics 0'''

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

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

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

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

     lowerCAmelCase_			=      gpu_alloc_mem_orig - gpu_alloc_mem_bnb

     lowerCAmelCase_			=      gpu_peak_mem_orig + gpu_alloc_mem_orig
     lowerCAmelCase_			=      gpu_peak_mem_bnb + gpu_alloc_mem_bnb
     lowerCAmelCase_			=      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_			=      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(
         _lowerCamelCase    ,     _lowerCamelCase    ,     '''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(
         _lowerCamelCase    ,     _lowerCamelCase    ,     '''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(
         _lowerCamelCase    ,     _lowerCamelCase    ,     F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}''' )







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = 3E-3    ,     _lowerCamelCase = "adafactor"    ,     _lowerCamelCase = False    ,     _lowerCamelCase = None    ,     _lowerCamelCase = 0    ,     _lowerCamelCase = True    ,     _lowerCamelCase = True    ,     _lowerCamelCase = True    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     ):
     lowerCAmelCase_			=      self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
     lowerCAmelCase_			=      self.get_auto_remove_tmp_dir()
     lowerCAmelCase_			=      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(_lowerCamelCase )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(_lowerCamelCase )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

     lowerCAmelCase_			=      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(_lowerCamelCase )}
        '''.split()

     lowerCAmelCase_			=      '''
            --do_predict
        '''.split()

     lowerCAmelCase_			=      []
     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_			=      get_gpu_count()
        lowerCAmelCase_			=      get_torch_dist_unique_port()
        lowerCAmelCase_			=      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_			=      [sys.executable] + distributed_args + args
        # keep for quick debug
        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
        execute_subprocess_async(_lowerCamelCase    ,     env=self.get_env() )
     else:
        lowerCAmelCase_			=      ['''run_translation.py'''] + args
        with patch.object(_lowerCamelCase    ,     '''argv'''    ,     _lowerCamelCase ):
           main()

     return output_dir






'''simple docstring'''

from __future__ import annotations



def snake_case_      (					__snake_case  :							list[int | str])			->					None:
   create_state_space_tree(__snake_case	,   []	,   0	,   [0 for i in range(len(__snake_case))])



def snake_case_      (					__snake_case  :							list[int | str]	,   __snake_case  :							list[int | str]	,   __snake_case  :							int	,   __snake_case  :							list[int]	,   )			->					None:

   if index == len(__snake_case):
      print(__snake_case)
      return

   for i in range(len(__snake_case)):
      if not index_used[i]:
         current_sequence.append(sequence[i])
         lowerCAmelCase_			=      True
         create_state_space_tree(__snake_case	,   __snake_case	,   index + 1	,   __snake_case)
         current_sequence.pop()
         lowerCAmelCase_			=      False
A_     :						list[int | str]    							=[3, 1, 2, 4]
generate_all_permutations(sequence)

A_     :						list[int | str]    							=["A", "B", "C"]






generate_all_permutations(sequence_a)





